Analysing solutions

Setup

import itertools
import os
import re
import string
import xml.etree.ElementTree as ET

import cobra
import matplotlib as mpl
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from IPython.display import SVG
from matplotlib.ticker import MultipleLocator
from mmon_gcm.analysing import get_escher_map
from mmon_gcm.supermodel import SuperModel
sns.set_theme()
sns.set_style("ticks")
sns.set_palette(
    sns.color_palette(
        [
            "#332288",
            "#88CCEE",
            "#44AA99",
            "#117733",
            "#999933",
            "#DDCC77",
            "#CC6677",
            "#882255",
            "#AA4499",
        ]
    )
)
colours = sns.color_palette()

params = {
    "xtick.labelsize": "large",
    "ytick.labelsize": "large",
    "axes.labelsize": "large",
    "axes.titlesize": "x-large",
    #"axes.labelweight": "bold",
    #"axes.titleweight": "bold",
    #"font.weight": "bold",
    "axes.spines.right": False,
    "axes.spines.top": False,
    "legend.frameon": False,
}

#plt.rcParams['font.family'] = 'Arial'

plt.rcParams.update(params)
def get_multiphase_fluxes(df, reaction):
    reaction_phased = [f"{reaction}_{i+1}" for i in range(4)]
    selected_df = df.loc[reaction_phased, "fluxes"]
    return selected_df
def convert_phases_to_times(df, phase_times=[6, 6.5, 18, 24]):
    df.index = pd.MultiIndex.from_tuples([(row[0], phase_times[row[1] - 1]) for row in df.index], names=df.index.names)
    df = df.reorder_levels(["Phase", "Reaction"]).sort_index()
    df = df.append(pd.concat([df.loc[24]], keys=[0]))
    df = df.sort_index()
    df = df.reorder_levels(["Reaction", "Phase"]).sort_index()

    return df
def select_volume_based_on_id(reaction_id, SuperModel):
    total_gc_volume = SuperModel.get_volumes(per_guard_cell=False)

    if "total" in reaction_id:
        phase_volumes = total_gc_volume = SuperModel.get_volumes(per_guard_cell=False)
    elif "v_gc" in reaction_id:
        phase_volumes = total_gc_volume = SuperModel.get_volumes(per_guard_cell=False) * SuperModel.Vac_frac
    elif "c_gc" in reaction_id:
        phase_volumes = total_gc_volume = SuperModel.get_volumes(per_guard_cell=False) * (1 - SuperModel.Vac_frac)
    elif "p_gc" in reaction_id:
        phase_volumes = [1] * 4
    else:
        raise ValueError(f"Don't know how to deal with {reaction_id}")

    return phase_volumes
def convert_to_conc(row, SuperModel, phases_in_df=[0, 6, 6.5, 18, 24]):
    phase = row.name[1]

    phase_conversion_dict = {
        phases_in_df[0]: 4,
        phases_in_df[1]: 1,
        phases_in_df[2]: 2,
        phases_in_df[3]: 3,
        phases_in_df[4]: 4,
    }

    phase_volumes = select_volume_based_on_id(row.name[0], SuperModel)

    volume_for_phase = phase_volumes[phase_conversion_dict[phase] - 1]

    return row / volume_for_phase
def get_metabolite_df(solutions_df, concentrations=True, total_mets=None, SuperModel=None):
    if total_mets == None:
        total_mets = ["SUCROSE", "GLC", "MAL", "FRU", "K", "Cl", "CIT"]
    total_mets_reactions = [met + "_total_pseudolinker" for met in total_mets] + ["STARCH_p_gc_Linker"]
    total_mets_reactions_phased = [f"{reaction}_{i+1}" for i in range(4) for reaction in total_mets_reactions]
    met_df = (solutions_df.loc[total_mets_reactions]).copy()

    # change index to phase times instead of tags
    met_df = convert_phases_to_times(met_df)

    if concentrations == True:
        met_df = met_df.apply(convert_to_conc, args=([SuperModel]), axis=1)

    met_df = met_df.reorder_levels(["Phase", "Reaction"]).sort_index()

    met_df.index = pd.MultiIndex.from_tuples(
        [(index[0], index[1].split("_")[0]) for index in met_df.index],
        names=met_df.index.names,
    )

    return met_df
def get_closed_open_values(met_df_mm, light, atpase):
    open_closed_dict = {}

    for genotype in ["wt", "starchko"]:
        open_closed_dict[genotype] = {}
        open_closed_dict[genotype]["closed"] = met_df_mm.loc[~(met_df_mm.index == (6, "STARCH"))].loc[
            6, (light, atpase, genotype, "fluxes")
        ]
        open_closed_dict[genotype]["open"] = met_df_mm.loc[~(met_df_mm.index == (6.5, "STARCH"))].loc[
            6.5, (light, atpase, genotype, "fluxes")
        ]

    return open_closed_dict
def get_closed_open_values(met_df_mm, light, atpase):
    open_closed_dict = {}

    for genotype in ["wt", "starchko"]:
        open_closed_dict[genotype] = {}
        open_closed_dict[genotype]["closed"] = met_df_mm.loc[6, (light, atpase, genotype, "fluxes")]
        open_closed_dict[genotype]["open"] = met_df_mm.loc[6.5, (light, atpase, genotype, "fluxes")]

    return open_closed_dict
def get_totals_plot(specific_met_df):
    colours = ["#999999ff", "#ff7043ff"]
    mpl.rcParams["hatch.linewidth"] = 1.5
    fig, axs = plt.subplots(1, 2, figsize=(10, 4), gridspec_kw={"width_ratios": [7, 1]})

    closed_metabolite_df = specific_met_df.loc[6.0].drop("STARCH")
    open_metabolite_df = specific_met_df.loc[6.5].drop("STARCH")

    # plot wt
    axs[0].bar(
        np.array(range(len(closed_metabolite_df))) * 2 - 0.05,
        closed_metabolite_df["wt"],
        color=colours[0],
        width=-0.3,
        align="edge",
        edgecolor=colours[0],
    )
    axs[0].bar(
        np.array(range(len(open_metabolite_df))) * 2 + 0.05,
        open_metabolite_df["wt"],
        color=colours[1],
        width=0.3,
        align="edge",
        edgecolor=colours[1],
    )

    axs[0].bar(
        np.array(range(len(closed_metabolite_df))) * 2 + 1 - 0.05,
        closed_metabolite_df["starchko"],
        color=colours[0],
        width=-0.3,
        align="edge",
        edgecolor="white",
        hatch="//",
    )
    axs[0].bar(
        np.array(range(len(closed_metabolite_df))) * 2 + 1 + 0.05,
        open_metabolite_df["starchko"],
        color=colours[1],
        width=0.3,
        align="edge",
        edgecolor="white",
        hatch="//",
    )
    axs[0].bar(
        np.array(range(len(closed_metabolite_df))) * 2 + 1 - 0.05,
        closed_metabolite_df["starchko"],
        color="none",
        width=-0.3,
        align="edge",
        edgecolor=colours[0],
    )
    axs[0].bar(
        np.array(range(len(closed_metabolite_df))) * 2 + 1 + 0.05,
        open_metabolite_df["starchko"],
        color="none",
        width=0.3,
        align="edge",
        edgecolor=colours[1],
    )

    x_ticks_pad = 12
    axs[0].tick_params(axis="x", which="both", bottom=False, pad=x_ticks_pad)
    axs[1].tick_params(axis="x", which="both", bottom=False, pad=x_ticks_pad)

    starch_closed = specific_met_df.loc[(6.0, "STARCH")]
    starch_open = specific_met_df.loc[(6.5, "STARCH")]

    axs[1].bar(
        -0.05,
        starch_closed["wt"],
        width=-0.3,
        align="edge",
        edgecolor=colours[0],
        color=colours[0],
    )
    axs[1].bar(
        0.05,
        starch_open["wt"],
        width=0.3,
        align="edge",
        edgecolor=colours[1],
        color=colours[1],
    )

    axs[1].bar(
        1 - 0.05,
        starch_closed["starchko"],
        width=-0.3,
        align="edge",
        edgecolor=colours[0],
        color=colours[0],
        hatch="//",
    )
    axs[1].bar(
        1 + 0.05,
        starch_open["starchko"],
        width=0.3,
        align="edge",
        edgecolor=colours[1],
        color=colours[1],
        hatch="//",
    )

    main_x_tick_labels = [name[0] + name[1:3].lower() for name in open_metabolite_df.index]

    main_x_tick_labels_superscripted = []
    for label in main_x_tick_labels:
        if label == "K":
            new_label = r"K$^+$"
        elif label == "Cl":
            new_label = r"Cl$^-$"
        else:
            new_label = label
        main_x_tick_labels_superscripted.append(new_label)

    main_x_tick_labels = main_x_tick_labels_superscripted

    main_x_ticks = np.array(range(len(closed_metabolite_df))) * 2 + 0.5

    ax0_y_max = 60

    axs[0].set_xticks(main_x_ticks)
    axs[0].set_xticklabels(main_x_tick_labels)
    axs[0].set_ylim(-0.1, ax0_y_max)
    axs[0].set_xlim(-0.5, 13.5)
    axs[0].set_ylabel(r"Osmolyte concentration (mM)")
    axs[0].yaxis.set_major_locator(MultipleLocator(10))
    # axs[0].yaxis.set_minor_locator(AutoMinorLocator(2))

    for x_loc in [(x - 1) * 2 + 1.5 for x in range(len(closed_metabolite_df))]:
        axs[0].hlines(-3.7, x_loc + 0.1, x_loc + 1.9, clip_on=False, color="black", alpha=0.9)
        for offset, label in zip([0.5, 1.5], ["WT", "KO"]):
            axs[0].text(x_loc + offset, -2, label, ha="center", va="center", size="small")

    starch_x_tick_labels = ["Starch"]
    starch_x_ticks = [0.5]

    ax1_y_max = 14

    axs[1].set_xticks(starch_x_ticks)
    axs[1].set_xticklabels(starch_x_tick_labels)
    axs[1].set_ylim(-0.1 * ax1_y_max / ax0_y_max, ax1_y_max)
    axs[1].yaxis.set_label_position("right")
    axs[1].yaxis.tick_right()
    axs[1].yaxis.set_major_locator(MultipleLocator(2))
    # axs[1].yaxis.set_minor_locator(AutoMinorLocator(2))
    axs[1].spines["right"].set_visible(True)
    axs[1].spines["left"].set_visible(False)
    axs[1].set_ylabel(r"Amount (fmol$\cdot$GC$^{-1}$)")

    for x_loc in [(x - 1) * 2 + 1.5 for x in [0]]:
        axs[1].hlines(
            -3.7 * ax1_y_max / ax0_y_max,
            x_loc + 0.1,
            x_loc + 2,
            clip_on=False,
            color="black",
            alpha=0.9,
        )
        for offset, label in zip([0.5, 1.5], ["WT", "KO"]):
            axs[1].text(
                x_loc + offset,
                -2 * ax1_y_max / ax0_y_max,
                label,
                ha="center",
                va="center",
                size="small",
            )

    colors = {"EoN": "#999999ff", "30 mins": "#ff7043ff"}
    labels = list(colors.keys())
    handles = [plt.Rectangle((0, 0), 1, 1, color=colors[label]) for label in labels]
    # plt.legend(handles, labels)

    plt.subplots_adjust(wspace=0.05)

    return fig
def getgcdiagram(
    modeldf,
    modelname,
    condition_label,
    genotype_label,
    scale_height,
    minimal=True,
    flipped=False,
    display_svg=True,
    debug=False,
    brokenaxis=False,
    aspser=False,
    save_png=True,
):
    if minimal == True:
        if aspser == True:
            tree = ET.parse("../inputs/fluxmap_template_aspserminimal.svg")
            root = tree.getroot
        else:
            tree = ET.parse("../inputs/fluxmap_template_minimal.svg")
            root = tree.getroot
    else:
        tree = ET.parse("../inputs/fluxmap_template.svg")
        root = updatetextnames(tree, condition_label, genotype_label, debug=debug)

    # get the background and the flipped background, and set the opacity of the wrong one to 0
    background = getelement(
        tree,
        condition_label=condition_label,
        genotype_label=genotype_label,
        compartment_label="Background",
        debug=debug,
    )
    background_flipped = getelement(
        tree,
        condition_label=condition_label,
        genotype_label=genotype_label,
        compartment_label="Background_flipped",
        debug=debug,
    )

    if flipped == True:
        updateelementopacity(background, 0)
        updateelementopacity(background_flipped, 1)
    else:
        updateelementopacity(background, 1)
        updateelementopacity(background_flipped, 0)

    if minimal is not True:
        # get the scale bar, either keep the hatched or don't, and set the height to the scale conc specified
        for phase_label in ["EoN", "30", "EoN_hatched", "30_hatched"]:
            scale_bar_element = getelement(
                tree,
                condition_label=condition_label,
                genotype_label=genotype_label,
                compartment_label="other",
                metabolite_label="scale",
                phase_label=phase_label,
                debug=debug,
            )
            if "hatched" in phase_label:
                updateelementopacity(scale_bar_element, int(flipped))
            else:
                updateelementopacity(scale_bar_element, int(not flipped))
            updateelementheight(scale_bar_element, scale_height)

    for row in modeldf.iterrows():
        compartment_label = row[0][1]
        metabolite_label = row[0][0]

        if debug == True:
            print(row[0], row[1])

        try:
            eon_element = getelement(
                tree,
                condition_label=condition_label,
                genotype_label=genotype_label,
                compartment_label=compartment_label,
                metabolite_label=metabolite_label,
                phase_label="EoN",
                debug=debug,
            )
            thirty_element = getelement(
                tree,
                condition_label=condition_label,
                genotype_label=genotype_label,
                compartment_label=compartment_label,
                metabolite_label=metabolite_label,
                phase_label="30",
                debug=debug,
            )

            eon_element_hatched = getelement(
                tree,
                condition_label=condition_label,
                genotype_label=genotype_label,
                compartment_label=compartment_label,
                metabolite_label=metabolite_label,
                phase_label="EoN_hatched",
                debug=debug,
            )
            thirty_element_hatched = getelement(
                tree,
                condition_label=condition_label,
                genotype_label=genotype_label,
                compartment_label=compartment_label,
                metabolite_label=metabolite_label,
                phase_label="30_hatched",
                debug=debug,
            )

            if flipped == True:
                updateelementopacity(eon_element, 0)
                updateelementopacity(eon_element_hatched, 1)
                updateelementopacity(thirty_element, 0)
                updateelementopacity(thirty_element_hatched, 1)
            else:
                updateelementopacity(eon_element, 1)
                updateelementopacity(eon_element_hatched, 0)
                updateelementopacity(thirty_element, 1)
                updateelementopacity(thirty_element_hatched, 0)

            if row[1]["EoN"] <= 0.0001:
                updateelementheight(eon_element, 0.5)
                updateelementheight(eon_element_hatched, 0.5)
            else:
                updateelementheight(eon_element, row[1]["EoN"])
                updateelementheight(eon_element_hatched, row[1]["EoN"])

            if row[1]["30 mins"] <= 0.0001:
                updateelementheight(thirty_element, 0.5)
                updateelementheight(thirty_element_hatched, 0.5)
            else:
                updateelementheight(thirty_element, row[1]["30 mins"])
                updateelementheight(thirty_element_hatched, row[1]["30 mins"])

        except AttributeError:
            print(f"{row[0]} not in template")

    svgpath = f"../outputs/flux_maps/{modelname}.svg"
    tree.write(svgpath, encoding="UTF-8", xml_declaration=True)
    if display_svg == True:
        display(SVG(svgpath))

    return svgpath


def getelement(
    tree,
    condition_label,
    genotype_label=None,
    compartment_label=None,
    metabolite_label=None,
    phase_label=None,
    debug=False,
):
    if debug == True:
        print(
            "Trying: ",
            condition_label,
            genotype_label,
            compartment_label,
            metabolite_label,
            phase_label,
        )
    root = tree.getroot()
    if debug == True:
        print("Got root", end=" ")
    for condition in root.findall("{http://www.w3.org/2000/svg}g"):
        if condition.get("{http://www.inkscape.org/namespaces/inkscape}label") == "condition":
            if debug == True:
                print(condition_label, end=" ")
            if genotype_label == None:
                return conditon
            else:
                for genotype in condition:
                    if genotype.get("{http://www.inkscape.org/namespaces/inkscape}label") == "genotype":
                        if debug == True:
                            print(genotype_label, end=" ")
                        if compartment_label == None:
                            return genotype
                        else:
                            for compartment in genotype:
                                if (
                                    compartment.get("{http://www.inkscape.org/namespaces/inkscape}label")
                                    == compartment_label
                                ):
                                    if debug == True:
                                        print(compartment_label, end=" ")
                                    if metabolite_label == None:
                                        return compartment
                                    else:
                                        for metabolite in compartment:
                                            if (
                                                metabolite.get("{http://www.inkscape.org/namespaces/inkscape}label")
                                                == metabolite_label
                                            ):
                                                if debug == True:
                                                    print(metabolite_label, end=" ")
                                                if phase_label == None:
                                                    return metabolite
                                                else:
                                                    for phase in metabolite:
                                                        if (
                                                            phase.get(
                                                                "{http://www.inkscape.org/namespaces/inkscape}label"
                                                            )
                                                            == phase_label
                                                        ):
                                                            return phase


def updateelementopacity(element, opacity):
    style_attribs = element.attrib["style"].split(";")
    for i, style in enumerate(style_attribs):
        if re.match(r"^opacity", style):
            del style_attribs[i]
    style_attribs.append("opacity:" + str(opacity))
    element.attrib["style"] = ";".join(style_attribs)
    return element


def updateelementheight(element, height):
    try:
        element.attrib["height"]
        element.attrib["height"] = str(height)
    except:
        element.attrib["d"] = (
            element.attrib["d"].split("v")[0] + "v -" + str(height) + " h" + element.attrib["d"].split("h")[1]
        )
    return element


def updatetextnames(tree, condition_label, genotype_label, debug=False):
    root = tree.getroot()
    for condition in root.findall("{http://www.w3.org/2000/svg}g"):
        if condition.get("{http://www.inkscape.org/namespaces/inkscape}label") == "condition":
            for genotype in condition:
                if genotype.get("{http://www.inkscape.org/namespaces/inkscape}label") == "condition_name":
                    for tspan in genotype:
                        tspan.text = condition_label
                        if debug == True:
                            print("Condition label changed to " + tspan.text)
                elif genotype.get("{http://www.inkscape.org/namespaces/inkscape}label") == "genotype":
                    for compartment in genotype:
                        if compartment.get("{http://www.inkscape.org/namespaces/inkscape}label") == "genotype_name":
                            for tspan in compartment:
                                tspan.text = genotype_label
                                if debug == True:
                                    print("Genotype label changed to " + tspan.text)
                                return root
parameters_df = pd.read_csv("../inputs/arabidopsis_parameters.csv", index_col=0)
four_stage_GC_model = cobra.io.sbml.read_sbml_model("../models/4_stage_GC.xml")  # read model
arabidopsis_supermodel = SuperModel(parameters_df.loc[:, "Value"], fba_model=four_stage_GC_model);
No objective coefficients in model. Unclear what should be optimized

Import solutions and set up dataframes

solutions_dict = {
    file[:-4]: pd.read_csv(f"../outputs/model_solutions/{file}", index_col=0)
    for file in os.listdir("../outputs/model_solutions")
    if file.endswith(".csv")
}

Check we’re in a good range for the solver

maxes = {}
mins = {}
for name, solution in solutions_dict.items():
    maxes[name] = abs(solution.loc[:, "fluxes"]).max()
    mins[name] = abs(solution.loc[:, "fluxes"][abs(solution.loc[:, "fluxes"]) > 10**-7]).min()
mins
{'blue_constrained_wt': 4.31244284473815e-06,
 'blue_constrained_starchko': 4.291294149081833e-06,
 'white_unconstrained_starchko': 8.359240012413899e-07,
 'nops_unconstrained_wt': 2.7073713978167244e-06,
 'blue_unconstrained_wt': 3.6707915071407277e-07,
 'blue_unconstrained_starchko': 8.34944986206833e-07,
 'nops_constrained_wt': 4.969355485606848e-06,
 'white_unconstrained_wt': 5.523520793885328e-07,
 'white_constrained_wt': 4.281246479161383e-06,
 'white_constrained_starchko': 4.2812129271807015e-06,
 'nops_constrained_starchko': 5.607231189748266e-06,
 'nops_unconstrained_starchko': 2.708509015023009e-06}
abs(
    solutions_dict["blue_unconstrained_wt"].loc[:, "fluxes"][
        abs(solutions_dict["blue_unconstrained_wt"].loc[:, "fluxes"]) > 10**-7
    ]
).sort_values()
MALTOSE_c_gc_Linker_3                             3.670792e-07
MALTOSE_c_gc_Linker_1                             3.670792e-07
MALTOSE_c_gc_Linker_2                             3.670792e-07
PGLUCISOM_RXN_c_gc_1                              3.004235e-06
6PFRUCTPHOS_RXN_c_gc_1                            3.004235e-06
                                                      ...     
PLASTOQUINOL_PLASTOCYANIN_REDUCTASE_RXN_p_me_3    1.214954e+02
RXN490_3650_p_me_3                                2.429908e+02
Photon_tx_me_3                                    4.859816e+02
Photon_ep_me_3                                    4.859816e+02
EX_X_Photon_t_me_3                                4.859816e+02
Name: fluxes, Length: 1136, dtype: float64
abs(
    solutions_dict["blue_unconstrained_starchko"].loc[:, "fluxes"][
        abs(solutions_dict["blue_unconstrained_starchko"].loc[:, "fluxes"]) > 10**-7
    ]
).sort_values()
PROTON_ATPase_c_gc_4                              8.349450e-07
Cl_PROTON_ec_gc_4                                 8.349450e-07
Cl_a_tx_4                                         1.669890e-06
K_a_tx_4                                          1.669890e-06
Cl_ae_gc_4                                        1.669890e-06
                                                      ...     
PLASTOQUINOL_PLASTOCYANIN_REDUCTASE_RXN_p_me_3    1.214954e+02
RXN490_3650_p_me_3                                2.429908e+02
Photon_tx_me_3                                    4.859816e+02
Photon_ep_me_3                                    4.859816e+02
EX_X_Photon_t_me_3                                4.859816e+02
Name: fluxes, Length: 1133, dtype: float64

With solver tolerance of 10e-7 we treat any fluxes below that value as being effectively 0, and this doesn’t include any fluxes we’re interested in. They’re 10-5 and above, so there is a difference of two orders of magnitude.

conditions_permutations = [
    ["blue", "white", "nops"],
    ["unconstrained", "constrained"],
    ["wt", "starchko"],
    ["fluxes", "minimum", "maximum"],
]

solutions_df_columns = pd.MultiIndex.from_product(
    conditions_permutations, names=["Light", "ATPase", "Starch", "Solution"]
)
solutions_df = pd.DataFrame(columns=solutions_df_columns)

conditions_list_of_lists = [
    ["blue", "white", "nops"],
    ["unconstrained", "constrained"],
    ["wt", "starchko"],
]
conditions_iterations = list(itertools.product(*conditions_list_of_lists))

cols_to_drop = []
for conditions in conditions_iterations:
    light_condition, atpase_condition, starch_condition = conditions
    file_name = f"../outputs/model_solutions/{light_condition}_{atpase_condition}_{starch_condition}.csv"
    try:
        condition_df = pd.read_csv(file_name, index_col=0)
        for col in ["fluxes", "minimum", "maximum"]:
            solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
    except:
        cols_to_drop.append(conditions)
        print(f"File {file_name} doesn't exist, skipping")


solutions_df_unphased = solutions_df.copy()
index_phased = pd.MultiIndex.from_tuples(
    [
        (reaction[:-2], int(reaction[-1])) if reaction[-1] in ["1", "2", "3", "4"] else (reaction, None)
        for reaction in solutions_df.index
    ],
    names=["Reaction", "Phase"],
)
solutions_df.index = index_phased

solutions_df = solutions_df.mask(abs(solutions_df) < 10**-7, 0)  # drop fluxes below 10^-8

solutions_df = solutions_df.drop(cols_to_drop, axis=1)
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
/tmp/ipykernel_5280/3555352746.py:27: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  solutions_df.loc[:, (light_condition, atpase_condition, starch_condition, col)] = condition_df.loc[:, col]
filter_df = solutions_df.filter(like='fluxes', axis=1)
filter_df = filter_df[filter_df.index.get_level_values(0).str.contains('gc_Linker')]
filter_df

# Get the unique first-level indices
unique_first_level_indices = filter_df.index.get_level_values('Reaction').unique()

# Define a threshold
threshold = 1e-7  # You can adjust this value to your desired threshold

# Check if the values within each group are within the threshold
for idx in unique_first_level_indices:
    group = filter_df.xs(key=idx, level='Reaction')
    if (group <= (group.iloc[0] + threshold)).all().all() and (group >= (group.iloc[0] - threshold)).all().all():
        #print(f"Values for First_Level '{idx}' are within the threshold.")
        pass
    else:
        print(f"Values for First_Level '{idx}' are not within the threshold.")
        #print(filter_df.loc[idx])
Values for First_Level 'CIT_c_gc_Linker' are not within the threshold.
Values for First_Level 'Cl_c_gc_Linker' are not within the threshold.
Values for First_Level 'FRU_c_gc_Linker' are not within the threshold.
Values for First_Level 'GLC_c_gc_Linker' are not within the threshold.
Values for First_Level 'K_c_gc_Linker' are not within the threshold.
Values for First_Level 'MAL_c_gc_Linker' are not within the threshold.
Values for First_Level 'SUCROSE_c_gc_Linker' are not within the threshold.
Values for First_Level 'Cl_v_gc_Linker' are not within the threshold.
Values for First_Level 'FRU_v_gc_Linker' are not within the threshold.
Values for First_Level 'GLC_v_gc_Linker' are not within the threshold.
Values for First_Level 'K_v_gc_Linker' are not within the threshold.
Values for First_Level 'MAL_v_gc_Linker' are not within the threshold.
Values for First_Level 'SUCROSE_v_gc_Linker' are not within the threshold.
Values for First_Level 'aMAL_v_gc_Linker' are not within the threshold.
Values for First_Level 'STARCH_p_gc_Linker' are not within the threshold.
Values for First_Level 'MALTOSE_c_gc_Linker' are not within the threshold.

Plotting Linker fluxes

All gc osmolytes

def get_linkers_df(
    solutions_df,
    concentrations=True,
    osmolytes_path="../inputs/osmolytes.csv",
    SuperModel=None,
):
    osmolytes = pd.read_csv(osmolytes_path, index_col=0)
    gc_osmolytes = [os for os in osmolytes.index if os[-2:] == "gc"]
    gc_osmolytes_linker_reactions = [os + "_Linker" for os in gc_osmolytes]

    linkers_df = solutions_df.loc[gc_osmolytes_linker_reactions].copy()

    linkers_df = convert_phases_to_times(linkers_df)

    if concentrations == True:
        linkers_df = linkers_df.apply(convert_to_conc, args=([SuperModel]), axis=1)

    linkers_df = linkers_df.reorder_levels(["Phase", "Reaction"]).sort_index()

    index_key = {"p": "Chloroplast", "v": "Vacuole", "c": "Cytoplasm"}

    linkers_df.index = pd.MultiIndex.from_tuples(
        [
            (
                index[0],
                "_".join(index[1].split("_")[:-3]),
                index_key[index[1].split("_")[-3]],
            )
            for index in linkers_df.index
        ],
        names=linkers_df.index.names + ["Compartment"],
    )

    return linkers_df
linkers_df = get_linkers_df(solutions_df, SuperModel=arabidopsis_supermodel)
/tmp/ipykernel_5280/2463702405.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df = df.append(pd.concat([df.loc[24]], keys=[0]))
linkers_df.loc[(slice(None), 'MAL', slice(None))]
Light blue ... nops
ATPase unconstrained constrained ... unconstrained constrained
Starch wt starchko wt starchko ... wt starchko wt starchko
Solution fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum fluxes ... maximum fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum
Phase Compartment
0.0 Cytoplasm 6.661263 NaN NaN 0.000000 NaN NaN 5.505091 NaN NaN 1.772218 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
Vacuole 17.944579 NaN NaN 22.517698 NaN NaN 7.671832 NaN NaN 5.746564 ... NaN 7.528567 NaN NaN 0.000000 NaN NaN 7.659888 NaN NaN
6.0 Cytoplasm 0.000000 0.0 59.527114 0.000000 0.0 59.527114 0.000000 0.0 59.527114 0.000000 ... 59.527114 0.000000 0.0 59.527114 0.000000 0.0 59.527114 0.000000 0.0 0.000000
Vacuole 17.944579 0.0 46.298866 22.517698 0.0 46.298866 7.671832 0.0 46.298866 5.746564 ... 46.298866 7.528567 0.0 46.298866 0.000000 0.0 46.298866 7.659888 0.0 46.298866
6.5 Cytoplasm 70.356673 0.0 72.147605 71.629349 0.0 58.166494 37.324187 0.0 72.147605 27.957588 ... 72.147605 36.627203 0.0 72.147605 14.534914 0.0 72.147605 37.266089 0.0 58.166494
Vacuole 0.000000 0.0 56.114804 0.000000 0.0 56.114804 0.000000 0.0 56.114804 0.000000 ... 56.114804 0.000000 0.0 56.114804 0.000000 0.0 56.114804 0.000000 0.0 56.114804
18.0 Cytoplasm 15.283172 NaN NaN 0.000000 NaN NaN 14.874878 NaN NaN 11.142004 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
Vacuole 17.944579 NaN NaN 22.517698 NaN NaN 7.671832 NaN NaN 5.746564 ... NaN 7.528567 NaN NaN 0.000000 NaN NaN 7.659888 NaN NaN
24.0 Cytoplasm 6.661263 NaN NaN 0.000000 NaN NaN 5.505091 NaN NaN 1.772218 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
Vacuole 17.944579 NaN NaN 22.517698 NaN NaN 7.671832 NaN NaN 5.746564 ... NaN 7.528567 NaN NaN 0.000000 NaN NaN 7.659888 NaN NaN

10 rows × 36 columns

# Combine metabolites with multiple charges (MAL, CIT, HIS) in linkers_df
mets_to_combine = ['aCIT', 'aMAL', 'bHIS']
for idx in linkers_df.index:
    if idx[1] in mets_to_combine:
        main_met_idx = (idx[0], idx[1][1:], idx[2])
        linkers_df.loc[main_met_idx] = linkers_df.loc[main_met_idx] + linkers_df.loc[idx]
        linkers_df = linkers_df.drop(idx)
linkers_df.loc[(slice(None), 'MAL', slice(None))]
Light blue ... nops
ATPase unconstrained constrained ... unconstrained constrained
Starch wt starchko wt starchko ... wt starchko wt starchko
Solution fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum fluxes ... maximum fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum
Phase Compartment
0.0 Cytoplasm 6.661263 NaN NaN 0.000000 NaN NaN 5.505091 NaN NaN 1.772218 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
Vacuole 25.635113 NaN NaN 32.168140 NaN NaN 10.959760 NaN NaN 8.209377 ... NaN 10.755095 NaN NaN 0.000000 NaN NaN 10.942697 NaN NaN
6.0 Cytoplasm 0.000000 0.0 59.527114 0.000000 0.0 59.527114 0.000000 0.0 59.527114 0.000000 ... 59.527114 0.000000 0.0 59.527114 0.000000 0.0 59.527114 0.000000 0.0 0.000000
Vacuole 25.635113 0.0 66.141238 32.168140 0.0 66.141238 10.959760 0.0 66.141238 8.209377 ... 66.141238 10.755095 0.0 66.141238 0.000000 0.0 66.141238 10.942697 0.0 66.141238
6.5 Cytoplasm 70.356673 0.0 72.147605 71.629349 0.0 58.166494 37.324187 0.0 72.147605 27.957588 ... 72.147605 36.627203 0.0 72.147605 14.534914 0.0 72.147605 37.266089 0.0 58.166494
Vacuole 0.000000 0.0 80.164005 0.000000 0.0 80.164005 0.000000 0.0 80.164005 0.000000 ... 80.164005 0.000000 0.0 80.164005 0.000000 0.0 80.164005 0.000000 0.0 80.164005
18.0 Cytoplasm 15.283172 NaN NaN 0.000000 NaN NaN 14.874878 NaN NaN 11.142004 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
Vacuole 25.635113 NaN NaN 32.168140 NaN NaN 10.959760 NaN NaN 8.209377 ... NaN 10.755095 NaN NaN 0.000000 NaN NaN 10.942697 NaN NaN
24.0 Cytoplasm 6.661263 NaN NaN 0.000000 NaN NaN 5.505091 NaN NaN 1.772218 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
Vacuole 25.635113 NaN NaN 32.168140 NaN NaN 10.959760 NaN NaN 8.209377 ... NaN 10.755095 NaN NaN 0.000000 NaN NaN 10.942697 NaN NaN

10 rows × 36 columns

met_df_mm = get_metabolite_df(solutions_df, SuperModel=arabidopsis_supermodel)
/tmp/ipykernel_5280/2463702405.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df = df.append(pd.concat([df.loc[24]], keys=[0]))
met_df_mm
Light blue ... nops
ATPase unconstrained constrained ... unconstrained constrained
Starch wt starchko wt starchko ... wt starchko wt starchko
Solution fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum fluxes ... maximum fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum
Phase Reaction
0.0 CIT 1.431237 NaN NaN 0.000000 NaN NaN 1.555385 NaN NaN 1.555385 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
Cl 16.524279 NaN NaN 21.954453 NaN NaN 13.391074 NaN NaN 0.718695 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.458382 NaN NaN 0.659272 NaN NaN 0.658861 ... NaN 0.637511 NaN NaN 0.672046 NaN NaN 0.989743 NaN NaN
GLC 0.633834 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
K 24.135299 NaN NaN 21.955092 NaN NaN 20.798763 NaN NaN 6.267413 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
MAL 20.910624 NaN NaN 24.158592 NaN NaN 9.601548 NaN NaN 6.606525 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
STARCH 0.000244 NaN NaN 0.000000 NaN NaN 0.005126 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.002569 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.098507 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 0.637511 NaN NaN 18.115847 NaN NaN 30.149261 NaN NaN
6.0 CIT 2.537007 0.0 49.435142 0.000000 0.0 49.435142 2.469230 0.0 49.435142 1.849573 ... 49.435142 2.423123 0.0 49.435142 2.777890 0.0 49.435142 2.465388 0.0 49.435142
Cl 16.524279 0.0 89.290671 22.232898 0.0 89.290671 13.949171 0.0 89.290671 1.121672 ... 89.290671 2.969559 0.0 89.290671 0.000000 0.0 89.290671 0.000000 0.0 80.403347
FRU 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 ... 178.581341 1.275022 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 160.500964
GLC 1.268503 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 ... 178.581341 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 160.500964
K 24.135299 0.0 140.262888 22.233537 0.0 140.262888 21.356860 0.0 140.262888 6.670390 ... 140.262888 10.238928 0.0 140.262888 8.333669 0.0 140.262888 7.396173 0.0 133.708937
MAL 19.251970 0.0 64.494321 24.158592 0.0 64.494321 8.230780 0.0 64.494321 6.165242 ... 64.494321 8.077077 0.0 64.494321 0.000000 0.0 64.494321 8.217966 0.0 64.494321
STARCH 0.000000 0.0 1000.000000 0.000000 0.0 0.000000 0.005126 0.0 0.285304 0.000000 ... 1000.000000 0.000000 0.0 0.000000 0.002569 0.0 0.232884 0.000000 0.0 0.000000
SUCROSE 0.000000 0.0 178.581341 0.000000 0.0 178.581341 7.881207 0.0 178.581341 31.139004 ... 178.581341 0.000000 0.0 178.581341 18.787893 0.0 178.581341 31.139004 0.0 178.581341
6.5 CIT 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 ... 44.869080 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 0.0 39.265593
Cl 45.604537 0.0 108.221407 51.357528 0.0 108.221407 29.258708 0.0 94.698182 18.117110 ... 108.221407 32.501177 0.0 108.221407 17.142857 0.0 94.698182 17.142857 0.0 86.978907
FRU 0.000000 0.0 216.442814 0.000000 0.0 216.442814 6.845391 0.0 216.442814 27.046449 ... 216.442814 1.365608 0.0 216.442814 16.318627 0.0 216.442814 27.046449 0.0 216.442814
GLC 1.101786 0.0 216.442814 0.000000 0.0 216.442814 27.046452 0.0 216.442814 27.046449 ... 216.442814 0.000000 0.0 216.442814 25.840051 0.0 216.442814 27.046449 0.0 216.442814
K 52.215252 0.0 152.876211 51.358083 0.0 152.876211 35.692816 0.0 138.971194 22.936568 ... 152.876211 38.815143 0.0 152.876211 24.381244 0.0 138.971194 23.566961 0.0 133.278620
MAL 17.518812 0.0 65.116825 17.835708 0.0 65.116825 9.293723 0.0 65.116825 6.961439 ... 65.116825 9.120173 0.0 65.116825 3.619193 0.0 65.116825 9.279256 0.0 62.386555
STARCH 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 ... 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.099952 0.000000 0.0 0.000000
SUCROSE 0.234594 0.0 216.442814 0.386397 0.0 216.442814 0.000000 0.0 216.442814 0.000000 ... 216.442814 1.230198 0.0 216.442814 0.000000 0.0 216.442814 0.000000 0.0 138.990025
18.0 CIT 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
Cl 16.524279 NaN NaN 21.908994 NaN NaN 12.672379 NaN NaN 0.000000 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.000000 NaN NaN 1.318969 NaN NaN 1.318558 ... NaN 0.000000 NaN NaN 1.906201 NaN NaN 1.979486 NaN NaN
GLC 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
K 24.135299 NaN NaN 21.909632 NaN NaN 20.080068 NaN NaN 5.548718 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
MAL 23.057480 NaN NaN 24.158273 NaN NaN 11.934624 NaN NaN 8.939601 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
STARCH 0.000559 NaN NaN 0.000000 NaN NaN 0.005126 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.002455 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.647915 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 1.275022 NaN NaN 16.881692 NaN NaN 29.159518 NaN NaN
24.0 CIT 1.431237 NaN NaN 0.000000 NaN NaN 1.555385 NaN NaN 1.555385 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
Cl 16.524279 NaN NaN 21.954453 NaN NaN 13.391074 NaN NaN 0.718695 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.458382 NaN NaN 0.659272 NaN NaN 0.658861 ... NaN 0.637511 NaN NaN 0.672046 NaN NaN 0.989743 NaN NaN
GLC 0.633834 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
K 24.135299 NaN NaN 21.955092 NaN NaN 20.798763 NaN NaN 6.267413 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
MAL 20.910624 NaN NaN 24.158592 NaN NaN 9.601548 NaN NaN 6.606525 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
STARCH 0.000244 NaN NaN 0.000000 NaN NaN 0.005126 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.002569 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.098507 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 0.637511 NaN NaN 18.115847 NaN NaN 30.149261 NaN NaN

40 rows × 36 columns

ordered_index = []
for phase in [0.0, 6.0, 6.5, 18.0, 24.0]:
    for met in ["K", "Cl", "SUCROSE", "GLC", "FRU", "MAL", "CIT", "STARCH"]:
        ordered_index.append((phase, met))
pd.MultiIndex.from_tuples(ordered_index)
met_df_mm = met_df_mm.reindex(ordered_index)
met_df_mm
Light blue ... nops
ATPase unconstrained constrained ... unconstrained constrained
Starch wt starchko wt starchko ... wt starchko wt starchko
Solution fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum fluxes ... maximum fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum
Phase Reaction
0.0 K 24.135299 NaN NaN 21.955092 NaN NaN 20.798763 NaN NaN 6.267413 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
Cl 16.524279 NaN NaN 21.954453 NaN NaN 13.391074 NaN NaN 0.718695 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.098507 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 0.637511 NaN NaN 18.115847 NaN NaN 30.149261 NaN NaN
GLC 0.633834 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.458382 NaN NaN 0.659272 NaN NaN 0.658861 ... NaN 0.637511 NaN NaN 0.672046 NaN NaN 0.989743 NaN NaN
MAL 20.910624 NaN NaN 24.158592 NaN NaN 9.601548 NaN NaN 6.606525 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
CIT 1.431237 NaN NaN 0.000000 NaN NaN 1.555385 NaN NaN 1.555385 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
STARCH 0.000244 NaN NaN 0.000000 NaN NaN 0.005126 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.002569 NaN NaN 0.000000 NaN NaN
6.0 K 24.135299 0.0 140.262888 22.233537 0.0 140.262888 21.356860 0.0 140.262888 6.670390 ... 140.262888 10.238928 0.0 140.262888 8.333669 0.0 140.262888 7.396173 0.0 133.708937
Cl 16.524279 0.0 89.290671 22.232898 0.0 89.290671 13.949171 0.0 89.290671 1.121672 ... 89.290671 2.969559 0.0 89.290671 0.000000 0.0 89.290671 0.000000 0.0 80.403347
SUCROSE 0.000000 0.0 178.581341 0.000000 0.0 178.581341 7.881207 0.0 178.581341 31.139004 ... 178.581341 0.000000 0.0 178.581341 18.787893 0.0 178.581341 31.139004 0.0 178.581341
GLC 1.268503 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 ... 178.581341 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 160.500964
FRU 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 ... 178.581341 1.275022 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 160.500964
MAL 19.251970 0.0 64.494321 24.158592 0.0 64.494321 8.230780 0.0 64.494321 6.165242 ... 64.494321 8.077077 0.0 64.494321 0.000000 0.0 64.494321 8.217966 0.0 64.494321
CIT 2.537007 0.0 49.435142 0.000000 0.0 49.435142 2.469230 0.0 49.435142 1.849573 ... 49.435142 2.423123 0.0 49.435142 2.777890 0.0 49.435142 2.465388 0.0 49.435142
STARCH 0.000000 0.0 1000.000000 0.000000 0.0 0.000000 0.005126 0.0 0.285304 0.000000 ... 1000.000000 0.000000 0.0 0.000000 0.002569 0.0 0.232884 0.000000 0.0 0.000000
6.5 K 52.215252 0.0 152.876211 51.358083 0.0 152.876211 35.692816 0.0 138.971194 22.936568 ... 152.876211 38.815143 0.0 152.876211 24.381244 0.0 138.971194 23.566961 0.0 133.278620
Cl 45.604537 0.0 108.221407 51.357528 0.0 108.221407 29.258708 0.0 94.698182 18.117110 ... 108.221407 32.501177 0.0 108.221407 17.142857 0.0 94.698182 17.142857 0.0 86.978907
SUCROSE 0.234594 0.0 216.442814 0.386397 0.0 216.442814 0.000000 0.0 216.442814 0.000000 ... 216.442814 1.230198 0.0 216.442814 0.000000 0.0 216.442814 0.000000 0.0 138.990025
GLC 1.101786 0.0 216.442814 0.000000 0.0 216.442814 27.046452 0.0 216.442814 27.046449 ... 216.442814 0.000000 0.0 216.442814 25.840051 0.0 216.442814 27.046449 0.0 216.442814
FRU 0.000000 0.0 216.442814 0.000000 0.0 216.442814 6.845391 0.0 216.442814 27.046449 ... 216.442814 1.365608 0.0 216.442814 16.318627 0.0 216.442814 27.046449 0.0 216.442814
MAL 17.518812 0.0 65.116825 17.835708 0.0 65.116825 9.293723 0.0 65.116825 6.961439 ... 65.116825 9.120173 0.0 65.116825 3.619193 0.0 65.116825 9.279256 0.0 62.386555
CIT 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 ... 44.869080 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 0.0 39.265593
STARCH 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 ... 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.099952 0.000000 0.0 0.000000
18.0 K 24.135299 NaN NaN 21.909632 NaN NaN 20.080068 NaN NaN 5.548718 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
Cl 16.524279 NaN NaN 21.908994 NaN NaN 12.672379 NaN NaN 0.000000 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.647915 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 1.275022 NaN NaN 16.881692 NaN NaN 29.159518 NaN NaN
GLC 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.000000 NaN NaN 1.318969 NaN NaN 1.318558 ... NaN 0.000000 NaN NaN 1.906201 NaN NaN 1.979486 NaN NaN
MAL 23.057480 NaN NaN 24.158273 NaN NaN 11.934624 NaN NaN 8.939601 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
CIT 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
STARCH 0.000559 NaN NaN 0.000000 NaN NaN 0.005126 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.002455 NaN NaN 0.000000 NaN NaN
24.0 K 24.135299 NaN NaN 21.955092 NaN NaN 20.798763 NaN NaN 6.267413 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
Cl 16.524279 NaN NaN 21.954453 NaN NaN 13.391074 NaN NaN 0.718695 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.098507 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 0.637511 NaN NaN 18.115847 NaN NaN 30.149261 NaN NaN
GLC 0.633834 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.458382 NaN NaN 0.659272 NaN NaN 0.658861 ... NaN 0.637511 NaN NaN 0.672046 NaN NaN 0.989743 NaN NaN
MAL 20.910624 NaN NaN 24.158592 NaN NaN 9.601548 NaN NaN 6.606525 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
CIT 1.431237 NaN NaN 0.000000 NaN NaN 1.555385 NaN NaN 1.555385 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
STARCH 0.000244 NaN NaN 0.000000 NaN NaN 0.005126 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.002569 NaN NaN 0.000000 NaN NaN

40 rows × 36 columns

met_df = get_metabolite_df(solutions_df, concentrations=False)
/tmp/ipykernel_5280/2463702405.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df = df.append(pd.concat([df.loc[24]], keys=[0]))
def plot_linkers(df, light):
    fig, axs = plt.subplots(2, 2, figsize=(10, 10), sharex=True, sharey=True)
    for x, atpase_condition in enumerate(["unconstrained", "constrained"]):
        for y, starch_condition in enumerate(["wt", "starchko"]):
            df.xs("fluxes", level="Solution", axis=1).loc[
                :, (light, atpase_condition, starch_condition)
            ].unstack().plot(title=f"{atpase_condition} {starch_condition}", ax=axs[x][y])
            starch_axis = axs[x][y].twinx()
            (
                df.xs("fluxes", level="Solution", axis=1)
                .loc[:, (light, atpase_condition, starch_condition)]
                .xs("STARCH", level="Reaction")
            ).plot(ax=starch_axis, ylim=(0, 0.01), color="black")
            axs[x][y].get_legend().remove()
    axs[1][0].legend(loc="center left", bbox_to_anchor=(2.3, 1.1))
for light in ["blue", "white"]:
    plot_linkers(met_df_mm, light)

Plotting before and after opening

def convert_starch_cols_to_per_gc(row_series, SuperModel, units):
    if row_series.name[1] == "STARCH":
        n_gcs = SuperModel.N_gcs
        row_series_moles = row_series * 10**-3
        moles_per_gc = row_series_moles / n_gcs
        row_series_converted_to_units = moles_per_gc * (1 / units)

        return row_series_converted_to_units

    else:
        return row_series
femtomoles = 10**-15
met_df_mm_starch_fmol_per_gc = met_df_mm.apply(
    convert_starch_cols_to_per_gc, args=[arabidopsis_supermodel, femtomoles], axis=1
)
met_df_mm_starch_fmol_per_gc
Light blue ... nops
ATPase unconstrained constrained ... unconstrained constrained
Starch wt starchko wt starchko ... wt starchko wt starchko
Solution fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum fluxes ... maximum fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum
Phase Reaction
0.0 K 24.135299 NaN NaN 21.955092 NaN NaN 20.798763 NaN NaN 6.267413 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
Cl 16.524279 NaN NaN 21.954453 NaN NaN 13.391074 NaN NaN 0.718695 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.098507 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 0.637511 NaN NaN 18.115847 NaN NaN 30.149261 NaN NaN
GLC 0.633834 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.458382 NaN NaN 0.659272 NaN NaN 0.658861 ... NaN 0.637511 NaN NaN 0.672046 NaN NaN 0.989743 NaN NaN
MAL 20.910624 NaN NaN 24.158592 NaN NaN 9.601548 NaN NaN 6.606525 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
CIT 1.431237 NaN NaN 0.000000 NaN NaN 1.555385 NaN NaN 1.555385 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
STARCH 0.420192 NaN NaN 0.000000 NaN NaN 8.837963 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 4.429523 NaN NaN 0.000000 NaN NaN
6.0 K 24.135299 0.0 1.402629e+02 22.233537 0.0 140.262888 21.356860 0.0 140.262888 6.670390 ... 1.402629e+02 10.238928 0.0 140.262888 8.333669 0.0 140.262888 7.396173 0.0 133.708937
Cl 16.524279 0.0 8.929067e+01 22.232898 0.0 89.290671 13.949171 0.0 89.290671 1.121672 ... 8.929067e+01 2.969559 0.0 89.290671 0.000000 0.0 89.290671 0.000000 0.0 80.403347
SUCROSE 0.000000 0.0 1.785813e+02 0.000000 0.0 178.581341 7.881207 0.0 178.581341 31.139004 ... 1.785813e+02 0.000000 0.0 178.581341 18.787893 0.0 178.581341 31.139004 0.0 178.581341
GLC 1.268503 0.0 1.785813e+02 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 ... 1.785813e+02 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 160.500964
FRU 0.000000 0.0 1.785813e+02 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 ... 1.785813e+02 1.275022 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 160.500964
MAL 19.251970 0.0 6.449432e+01 24.158592 0.0 64.494321 8.230780 0.0 64.494321 6.165242 ... 6.449432e+01 8.077077 0.0 64.494321 0.000000 0.0 64.494321 8.217966 0.0 64.494321
CIT 2.537007 0.0 4.943514e+01 0.000000 0.0 49.435142 2.469230 0.0 49.435142 1.849573 ... 4.943514e+01 2.423123 0.0 49.435142 2.777890 0.0 49.435142 2.465388 0.0 49.435142
STARCH 0.000000 0.0 1.724138e+06 0.000000 0.0 0.000000 8.837963 0.0 491.903125 0.000000 ... 1.724138e+06 0.000000 0.0 0.000000 4.429523 0.0 401.523796 0.000000 0.0 0.000000
6.5 K 52.215252 0.0 1.528762e+02 51.358083 0.0 152.876211 35.692816 0.0 138.971194 22.936568 ... 1.528762e+02 38.815143 0.0 152.876211 24.381244 0.0 138.971194 23.566961 0.0 133.278620
Cl 45.604537 0.0 1.082214e+02 51.357528 0.0 108.221407 29.258708 0.0 94.698182 18.117110 ... 1.082214e+02 32.501177 0.0 108.221407 17.142857 0.0 94.698182 17.142857 0.0 86.978907
SUCROSE 0.234594 0.0 2.164428e+02 0.386397 0.0 216.442814 0.000000 0.0 216.442814 0.000000 ... 2.164428e+02 1.230198 0.0 216.442814 0.000000 0.0 216.442814 0.000000 0.0 138.990025
GLC 1.101786 0.0 2.164428e+02 0.000000 0.0 216.442814 27.046452 0.0 216.442814 27.046449 ... 2.164428e+02 0.000000 0.0 216.442814 25.840051 0.0 216.442814 27.046449 0.0 216.442814
FRU 0.000000 0.0 2.164428e+02 0.000000 0.0 216.442814 6.845391 0.0 216.442814 27.046449 ... 2.164428e+02 1.365608 0.0 216.442814 16.318627 0.0 216.442814 27.046449 0.0 216.442814
MAL 17.518812 0.0 6.511682e+01 17.835708 0.0 65.116825 9.293723 0.0 65.116825 6.961439 ... 6.511682e+01 9.120173 0.0 65.116825 3.619193 0.0 65.116825 9.279256 0.0 62.386555
CIT 0.000000 0.0 4.486908e+01 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 ... 4.486908e+01 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 0.0 39.265593
STARCH 0.000000 0.0 0.000000e+00 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 ... 0.000000e+00 0.000000 0.0 0.000000 0.000000 0.0 172.331230 0.000000 0.0 0.000000
18.0 K 24.135299 NaN NaN 21.909632 NaN NaN 20.080068 NaN NaN 5.548718 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
Cl 16.524279 NaN NaN 21.908994 NaN NaN 12.672379 NaN NaN 0.000000 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.647915 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 1.275022 NaN NaN 16.881692 NaN NaN 29.159518 NaN NaN
GLC 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.000000 NaN NaN 1.318969 NaN NaN 1.318558 ... NaN 0.000000 NaN NaN 1.906201 NaN NaN 1.979486 NaN NaN
MAL 23.057480 NaN NaN 24.158273 NaN NaN 11.934624 NaN NaN 8.939601 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
CIT 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
STARCH 0.964062 NaN NaN 0.000000 NaN NaN 8.837963 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 4.231974 NaN NaN 0.000000 NaN NaN
24.0 K 24.135299 NaN NaN 21.955092 NaN NaN 20.798763 NaN NaN 6.267413 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
Cl 16.524279 NaN NaN 21.954453 NaN NaN 13.391074 NaN NaN 0.718695 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.098507 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 0.637511 NaN NaN 18.115847 NaN NaN 30.149261 NaN NaN
GLC 0.633834 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.458382 NaN NaN 0.659272 NaN NaN 0.658861 ... NaN 0.637511 NaN NaN 0.672046 NaN NaN 0.989743 NaN NaN
MAL 20.910624 NaN NaN 24.158592 NaN NaN 9.601548 NaN NaN 6.606525 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
CIT 1.431237 NaN NaN 0.000000 NaN NaN 1.555385 NaN NaN 1.555385 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
STARCH 0.420192 NaN NaN 0.000000 NaN NaN 8.837963 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 4.429523 NaN NaN 0.000000 NaN NaN

40 rows × 36 columns

met_df_mm_starch_fmol_per_gc.xs("STARCH", level="Reaction").xs("fluxes", level="Solution", axis=1)
Light blue white nops
ATPase unconstrained constrained unconstrained constrained unconstrained constrained
Starch wt starchko wt starchko wt starchko wt starchko wt starchko wt starchko
Phase
0.0 0.420192 0.0 8.837963 0.0 0.080389 0.0 9.340372 0.0 0.0 0.0 4.429523 0.0
6.0 0.000000 0.0 8.837963 0.0 0.000000 0.0 9.340372 0.0 0.0 0.0 4.429523 0.0
6.5 0.000000 0.0 0.000000 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 0.000000 0.0
18.0 0.964062 0.0 8.837963 0.0 0.325796 0.0 9.340372 0.0 0.0 0.0 4.231974 0.0
24.0 0.420192 0.0 8.837963 0.0 0.080389 0.0 9.340372 0.0 0.0 0.0 4.429523 0.0 
for light in ["blue", "white", "nops"]:
    for atpase in ["unconstrained", "constrained"]:
        specific_df = met_df_mm_starch_fmol_per_gc.loc[:, (light, atpase)].xs("fluxes", level="Solution", axis=1)
        fig = get_totals_plot(specific_df)
        plot_name = f"{light}_{atpase}"
        fig.savefig(
            f"../outputs/total_opening_plots/{plot_name}.svg",
            format="svg",
            bbox_inches="tight",
        )
        fig.savefig(
            f"../outputs/total_opening_plots/{plot_name}.png",
            format="png",
            bbox_inches="tight",
            dpi=300,
        )
/tmp/ipykernel_5280/3835851778.py:3: PerformanceWarning: indexing past lexsort depth may impact performance.
  specific_df = met_df_mm_starch_fmol_per_gc.loc[:, (light, atpase)].xs("fluxes", level="Solution", axis=1)
/tmp/ipykernel_5280/3835851778.py:3: PerformanceWarning: indexing past lexsort depth may impact performance.
  specific_df = met_df_mm_starch_fmol_per_gc.loc[:, (light, atpase)].xs("fluxes", level="Solution", axis=1)
/tmp/ipykernel_5280/3835851778.py:3: PerformanceWarning: indexing past lexsort depth may impact performance.
  specific_df = met_df_mm_starch_fmol_per_gc.loc[:, (light, atpase)].xs("fluxes", level="Solution", axis=1)
/tmp/ipykernel_5280/3835851778.py:3: PerformanceWarning: indexing past lexsort depth may impact performance.
  specific_df = met_df_mm_starch_fmol_per_gc.loc[:, (light, atpase)].xs("fluxes", level="Solution", axis=1)
/tmp/ipykernel_5280/3835851778.py:3: PerformanceWarning: indexing past lexsort depth may impact performance.
  specific_df = met_df_mm_starch_fmol_per_gc.loc[:, (light, atpase)].xs("fluxes", level="Solution", axis=1)
/tmp/ipykernel_5280/3835851778.py:3: PerformanceWarning: indexing past lexsort depth may impact performance.
  specific_df = met_df_mm_starch_fmol_per_gc.loc[:, (light, atpase)].xs("fluxes", level="Solution", axis=1)

Flux maps

map_translation = {
    "": "Opening",
    "_1": "Night2",
    "_2": "Night1",
    "_3": "Day",
}
linkers_met_dict = {
    "SUCROSE": "Suc",
    "GLC": "Glc",
    "FRU": "Fru",
    "MAL": "Mal",
    "CIT": "Cit",
    "L_ASPARTATE": "Asp",
    "SER": "Ser",
}
linkers_df.loc[
    ([6, 6.5], ["SUCROSE", "GLC", "FRU", "MAL", "CIT", "L_ASPARTATE", "SER"]),
    ("blue", "constrained", "wt", "fluxes"),
].unstack(0)
Phase 6.0 6.5
Reaction Compartment
SUCROSE Cytoplasm 0.000000 0.000000
Vacuole 10.494283 0.000000
GLC Cytoplasm 0.000000 81.128761
Vacuole 0.000000 9.115034
FRU Cytoplasm 0.000000 0.000000
Vacuole 0.000000 9.115034
MAL Cytoplasm 0.000000 37.324187
Vacuole 10.959760 0.000000
CIT Cytoplasm 9.916585 0.000000
Vacuole 0.000000 0.000000
L_ASPARTATE Vacuole 0.000000 0.000000
SER Vacuole 0.000000 0.000000 
linkers_df.loc[
    ([6, 6.5], ["SUCROSE", "GLC", "FRU", "MAL", "CIT", "L_ASPARTATE", "SER"]),
    ("blue", "constrained", "starchko", "fluxes"),
].unstack(0)
Phase 6.0 6.5
Reaction Compartment
SUCROSE Cytoplasm 0.000000 0.000000
Vacuole 41.463387 0.000000
GLC Cytoplasm 0.000000 0.000000
Vacuole 0.000000 36.013914
FRU Cytoplasm 0.000000 0.000000
Vacuole 0.000000 36.013914
MAL Cytoplasm 0.000000 27.957588
Vacuole 8.209377 0.000000
CIT Cytoplasm 7.428003 0.000000
Vacuole 0.000000 0.000000
L_ASPARTATE Vacuole 57.476317 49.922287
SER Vacuole 0.000000 0.000000 
linkers_df.loc[
    ([6, 6.5], ["SUCROSE", "GLC", "FRU", "MAL", "CIT", "L_ASPARTATE", "SER"]),
    ("white", "constrained", "wt", "fluxes"),
].unstack(0)
Phase 6.0 6.5
Reaction Compartment
SUCROSE Cytoplasm 8.249604 0.000000
Vacuole 5.451526 0.000000
GLC Cytoplasm 0.000000 85.740649
Vacuole 0.000000 4.735040
FRU Cytoplasm 0.000000 15.763814
Vacuole 0.000000 4.735040
MAL Cytoplasm 0.000000 0.000000
Vacuole 0.000000 0.000000
CIT Cytoplasm 0.000000 0.000000
Vacuole 0.000000 0.000000
L_ASPARTATE Vacuole 0.000000 0.000000
SER Vacuole 0.000000 0.000000 
linkers_df.loc[
    ([6, 6.5], ["SUCROSE", "GLC", "FRU", "MAL", "CIT", "L_ASPARTATE", "SER"]),
    ("white", "constrained", "starchko", "fluxes"),
].unstack(0)
Phase 6.0 6.5
Reaction Compartment
SUCROSE Cytoplasm 0.000000 0.904148
Vacuole 41.118249 0.000000
GLC Cytoplasm 0.000000 0.000000
Vacuole 0.000000 35.714136
FRU Cytoplasm 0.000000 0.000000
Vacuole 0.000000 35.714136
MAL Cytoplasm 0.000000 0.000000
Vacuole 0.000000 0.000000
CIT Cytoplasm 0.000000 0.000000
Vacuole 0.000000 0.000000
L_ASPARTATE Vacuole 68.731554 59.698264
SER Vacuole 0.000000 0.000000 
linkers_df.loc[
    ([6, 6.5], ["SUCROSE", "GLC", "FRU", "MAL", "CIT", "L_ASPARTATE", "SER"]),
    ("nops", "constrained", "wt", "fluxes"),
].unstack(0)
Phase 6.0 6.5
Reaction Compartment
SUCROSE Cytoplasm 75.453384 0.000000
Vacuole 0.000000 0.000000
GLC Cytoplasm 0.000000 80.131249
Vacuole 0.000000 7.839374
FRU Cytoplasm 0.000000 41.892596
Vacuole 0.000000 7.839374
MAL Cytoplasm 0.000000 14.534914
Vacuole 0.000000 0.000000
CIT Cytoplasm 11.156184 0.000000
Vacuole 0.000000 0.000000
L_ASPARTATE Vacuole 0.000000 0.000000
SER Vacuole 0.000000 0.000000 
linkers_df.loc[
    ([6, 6.5], ["SUCROSE", "GLC", "FRU", "MAL", "CIT", "L_ASPARTATE", "SER"]),
    ("nops", "constrained", "starchko", "fluxes"),
].unstack(0)
Phase 6.0 6.5
Reaction Compartment
SUCROSE Cytoplasm 92.825784 0.000000
Vacuole 10.686263 0.000000
GLC Cytoplasm 0.000000 32.279604
Vacuole 0.000000 25.311355
FRU Cytoplasm 0.000000 32.279604
Vacuole 0.000000 25.311355
MAL Cytoplasm 0.000000 37.266089
Vacuole 10.942697 0.000000
CIT Cytoplasm 9.901168 0.000000
Vacuole 0.000000 0.000000
L_ASPARTATE Vacuole 0.000000 0.000000
SER Vacuole 0.000000 0.000000 
for light in ["blue", "white", "nops"]:
    for atpase in ["unconstrained", "constrained"]:
        for starch in ["wt", "starchko"]:
            print(f"{light}_{atpase}_{starch}")

            mets_to_include = [
                "SUCROSE",
                "GLC",
                "FRU",
                "MAL",
                "CIT",
                "L_ASPARTATE",
                "SER",
            ]
            test_df = linkers_df.loc[
                (
                    [6, 6.5],
                    mets_to_include,
                ),
                (light, atpase, starch, "fluxes"),
            ].unstack(0)
            test_df.columns = ["EoN", "30 mins"]
            test_df.index = pd.MultiIndex.from_tuples(
                [(linkers_met_dict[index[0]], index[1]) for index in test_df.index]
            )

            scaling = 0.4

            test_df = test_df * scaling

            if (test_df.loc[["Asp", "Ser"]] > 0.0000000001).sum().sum() > 0:
                aspser = True
                print(test_df)
            else:
                aspser = False
                print(test_df.loc[["Asp", "Ser"]])

            scale_conc = 50  # mM

            scaled_scale_conc = scale_conc * scaling

            map_name = f"{light}_{atpase}_{starch}"
            if starch == "wt":
                gc_path = getgcdiagram(
                    test_df,
                    map_name,
                    atpase,
                    starch,
                    scaled_scale_conc,
                    flipped=False,
                    #aspser=aspser,
                    aspser=False,
                    display_svg=True,
                    debug=False,
                    brokenaxis=False,
                )
            else:
                gc_path = getgcdiagram(
                    test_df,
                    map_name,
                    atpase,
                    starch,
                    scaled_scale_conc,
                    flipped=True,
                    #aspser=aspser,
                    aspser=False,
                    display_svg=True,
                    debug=False,
                    brokenaxis=False,
                )

            # doesn't seem to work with hatched
            # svg_code = open(gc_path, 'rt').read()
            # svg2png(bytestring=svg_code, write_to=f"../outputs/flux_maps/{map_name}.png", dpi=300)
blue_unconstrained_wt
             EoN  30 mins
Asp Vacuole  0.0      0.0
Ser Vacuole  0.0      0.0
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
blue_unconstrained_starchko
                     EoN    30 mins
Suc Cytoplasm   0.000000   0.620719
    Vacuole     0.000000   0.000000
Glc Cytoplasm   0.000000   0.000000
    Vacuole     0.000000   0.000000
Fru Cytoplasm   0.000000   0.000000
    Vacuole     0.000000   0.000000
Mal Cytoplasm   0.000000  28.651740
    Vacuole    12.867256   0.000000
Cit Cytoplasm   0.000000   0.000000
    Vacuole     0.000000   0.000000
Asp Vacuole    18.345473  15.934353
Ser Vacuole     0.000000   0.000000
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
blue_constrained_wt
             EoN  30 mins
Asp Vacuole  0.0      0.0
Ser Vacuole  0.0      0.0
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
blue_constrained_starchko
                     EoN    30 mins
Suc Cytoplasm   0.000000   0.000000
    Vacuole    16.585355   0.000000
Glc Cytoplasm   0.000000   0.000000
    Vacuole     0.000000  14.405565
Fru Cytoplasm   0.000000   0.000000
    Vacuole     0.000000  14.405565
Mal Cytoplasm   0.000000  11.183035
    Vacuole     3.283751   0.000000
Cit Cytoplasm   2.971201   0.000000
    Vacuole     0.000000   0.000000
Asp Vacuole    22.990527  19.968915
Ser Vacuole     0.000000   0.000000
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
white_unconstrained_wt
             EoN  30 mins
Asp Vacuole  0.0      0.0
Ser Vacuole  0.0      0.0
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
white_unconstrained_starchko
                     EoN    30 mins
Suc Cytoplasm   0.000000   1.362671
    Vacuole     0.000000   0.000000
Glc Cytoplasm   0.000000   0.000000
    Vacuole     0.000000   0.000000
Fru Cytoplasm   0.000000   0.000000
    Vacuole     0.000000   0.000000
Mal Cytoplasm   0.000000  28.404819
    Vacuole    12.756366   0.000000
Cit Cytoplasm   0.000000   0.000000
    Vacuole     0.000000   0.000000
Asp Vacuole     0.000000   0.000000
Ser Vacuole    36.990348  32.128760
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
white_constrained_wt
             EoN  30 mins
Asp Vacuole  0.0      0.0
Ser Vacuole  0.0      0.0
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
white_constrained_starchko
                     EoN    30 mins
Suc Cytoplasm   0.000000   0.361659
    Vacuole    16.447300   0.000000
Glc Cytoplasm   0.000000   0.000000
    Vacuole     0.000000  14.285654
Fru Cytoplasm   0.000000   0.000000
    Vacuole     0.000000  14.285654
Mal Cytoplasm   0.000000   0.000000
    Vacuole     0.000000   0.000000
Cit Cytoplasm   0.000000   0.000000
    Vacuole     0.000000   0.000000
Asp Vacuole    27.492621  23.879305
Ser Vacuole     0.000000   0.000000
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
nops_unconstrained_wt
             EoN  30 mins
Asp Vacuole  0.0      0.0
Ser Vacuole  0.0      0.0
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
nops_unconstrained_starchko
             EoN  30 mins
Asp Vacuole  0.0      0.0
Ser Vacuole  0.0      0.0
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
nops_constrained_wt
             EoN  30 mins
Asp Vacuole  0.0      0.0
Ser Vacuole  0.0      0.0
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template
nops_constrained_starchko
             EoN  30 mins
Asp Vacuole  0.0      0.0
Ser Vacuole  0.0      0.0
('Asp', 'Vacuole') not in template
('Ser', 'Vacuole') not in template

blue_phloem_tx_overall_df = solutions_df.loc["Phloem_tx_overall", "blue"].xs("fluxes", level="Solution", axis=1)
white_phloem_tx_overall_df = solutions_df.loc["Phloem_tx_overall", "white"].xs("fluxes", level="Solution", axis=1)
blue_phloem_tx_overall_df_wt_unconstrained_corrected = blue_phloem_tx_overall_df - blue_phloem_tx_overall_df.iloc[0, 0]
blue_phloem_tx_overall_df_wt_unconstrained_corrected = blue_phloem_tx_overall_df_wt_unconstrained_corrected.mask(
    blue_phloem_tx_overall_df_wt_unconstrained_corrected > -0.0000000001, 0
)
blue_phloem_tx_overall_df_wt_unconstrained_corrected_fmol = (
    blue_phloem_tx_overall_df_wt_unconstrained_corrected / parameters_df.loc["N_gcs", "Value"] * 10**12
)
white_phloem_tx_overall_df_wt_unconstrained_corrected = (
    white_phloem_tx_overall_df - white_phloem_tx_overall_df.iloc[0, 0]
)
white_phloem_tx_overall_df_wt_unconstrained_corrected = white_phloem_tx_overall_df_wt_unconstrained_corrected.mask(
    white_phloem_tx_overall_df_wt_unconstrained_corrected > -0.0000000001, 0
)
white_phloem_tx_overall_df_wt_unconstrained_corrected_fmol = (
    white_phloem_tx_overall_df_wt_unconstrained_corrected / parameters_df.loc["N_gcs", "Value"] * 10**12
)
combined_fmol = pd.concat(
    [
        blue_phloem_tx_overall_df_wt_unconstrained_corrected_fmol,
        white_phloem_tx_overall_df_wt_unconstrained_corrected_fmol,
    ],
    keys=["Blue", "White"],
)
combined_fmol.index = combined_fmol.index.droplevel(1)
combined_fmol
ATPase unconstrained constrained
Starch wt starchko wt starchko
Blue 0.0 -0.000124 -0.027500 -0.043059
White 0.0 -0.000036 -0.030068 -0.047952 
fig, ax = plt.subplots(figsize=(8, 6))
combined_fmol.plot.bar(ax=ax)
ax.legend(loc="center left", bbox_to_anchor=(1, 0.5))
ax.set_ylabel(
    "Reduction in overall phloem output compared to \n unconstrained WT in specified light(fmol gc$^{-1}$ h$^{-1}$)"
)

fig.set_tight_layout(True)

fig.savefig("../outputs/efficiency_comparisons/efficiency_comparisons_compared.svg")
fig.savefig("../outputs/efficiency_comparisons/efficiency_comparisons_compared.png")

from functools import reduce
def multiply_series_by_coefficient(series_object, model, metabolite_id):
    reaction_id = series_object.name[0]
    coefficient = model.reactions.get_by_id(f"{reaction_id}_1").get_coefficient(f"{metabolite_id}_1")
    return coefficient * series_object
def get_metabolite_budget(solution_df, metabolite, model):
    metabolite_reactions_df = solution_df.loc[
        (
            [reaction.id[:-2] for reaction in four_stage_GC_model.metabolites.get_by_id(f"{metabolite}_1").reactions],
            slice(None),
        ),
        :,
    ]
    metabolite_consumed_produced_df = metabolite_reactions_df.apply(
        multiply_series_by_coefficient, args=[model, metabolite], axis=1
    )
    return metabolite_consumed_produced_df
def get_relevant_reaction_fluxes(
    solution_df,
    scenarios_to_drop=[("nops", "unconstrained"), ("white", "unconstrained")],
):
    solution_df = solution_df.xs("fluxes", level="Solution", axis=1)
    solution_df = solution_df.drop(scenarios_to_drop, axis=1)  # only keep the scenarios that we are interested in
    solution_df = solution_df.loc[
        (abs(solution_df) > 0.00001).any(axis=1), :
    ]  # pick only reactions which have a flux in at least one scenario
    solution_df[abs(solution_df) < 0.00001] = 0  # ignore tiny fluxes from solver error

    return solution_df
def get_budget_for_multiple_metabolites(solution_df, metabolites, fba_model):
    metabolite_dfs = [get_metabolite_budget(solution_df, metabolite, fba_model) for metabolite in metabolites]
    metabolite_total_df = reduce(lambda a, b: a.add(b, fill_value=0), metabolite_dfs)
    metabolite_total_df = get_relevant_reaction_fluxes(metabolite_total_df)

    return metabolite_total_df
atp_metabolites = [metabolite.id for metabolite in four_stage_GC_model.metabolites if "ATP" in metabolite.id]
gc_atp_metabolites = [metabolite[:-2] for metabolite in atp_metabolites if "gc_2" in metabolite]
gc_atp_metabolites = [e for e in gc_atp_metabolites if e not in ("DATP_p_gc", "PHOSPHORIBOSYL_ATP_p_gc", "aDATP_p_gc")]
atp_budget_df = get_budget_for_multiple_metabolites(solutions_df, gc_atp_metabolites, four_stage_GC_model)
/tmp/ipykernel_5280/3164465358.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  solution_df = solution_df.drop(scenarios_to_drop, axis=1)  # only keep the scenarios that we are interested in
nadh_nadph_metabolites = [
    metabolite.id
    for metabolite in four_stage_GC_model.metabolites
    if "NADH" in metabolite.id or "NADPH" in metabolite.id
]
gc_nadh_nadph_metabolites = [metabolite[:-2] for metabolite in nadh_nadph_metabolites if "gc_2" in metabolite]
nadh_nadph_budget_df = get_budget_for_multiple_metabolites(solutions_df, gc_nadh_nadph_metabolites, four_stage_GC_model)
/tmp/ipykernel_5280/3164465358.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  solution_df = solution_df.drop(scenarios_to_drop, axis=1)  # only keep the scenarios that we are interested in
nadh_nadph_budget_df
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
1_PERIOD_18_PERIOD_1_PERIOD_2_RXN_p_gc 2 0.000000 0.000000 0.000000 0.000000 0.004593 0.004593 0.000000 0.000000
3 0.004593 0.004593 0.004593 0.004593 0.004593 0.004593 0.000000 0.000000
1_PERIOD_2_PERIOD_1_PERIOD_13_RXN_p_gc 2 0.000000 0.000000 0.000000 0.000000 -0.002173 -0.002742 0.000000 0.000000
3 -0.004054 -0.004049 -0.003958 -0.003864 -0.003975 -0.003881 0.000000 0.000000
2OXOGLUTARATEDEH_RXN_m_gc 1 0.000000 0.000028 0.000012 0.000023 0.000028 0.000028 0.000030 0.000031
... ... ... ... ... ... ... ... ... ...
NADPHox_p_tx_gc 4 -0.000047 -0.000040 -0.000040 -0.000040 -0.000040 -0.000040 -0.000038 -0.000039
PYRUVDEH_RXN_m_gc 1 0.000047 0.000028 0.000045 0.000034 0.000028 0.000028 0.000030 0.000031
2 0.000238 0.000813 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000104 0.000109
4 0.000053 0.000027 0.000057 0.000057 0.000028 0.000028 0.000033 0.000031

62 rows × 8 columns

atp_ordered_by_most_likely_importance = (
    abs(atp_budget_df).groupby(by="Reaction").sum().sum(axis=1).sort_values(ascending=False)
)
atp_budget_df_reordered = atp_budget_df.reindex(atp_ordered_by_most_likely_importance.index, level="Reaction")
nadh_nadph_ordered_by_most_likely_importance = (
    abs(nadh_nadph_budget_df).groupby(by="Reaction").sum().sum(axis=1).sort_values(ascending=False)
)
nadh_nadph_budget_df_reordered = nadh_nadph_budget_df.reindex(
    nadh_nadph_ordered_by_most_likely_importance.index, level="Reaction"
)
def convert_fluxes_to_per_guard_cell(series, super_model, units):
    return series * 10**-3 / super_model.N_gcs * (1 / units)
units = 10**-15  # fmol
atp_budget_df_reordered_nmoles_gc = atp_budget_df_reordered.apply(
    convert_fluxes_to_per_guard_cell, args=[arabidopsis_supermodel, units], axis=1
)
nadh_nadph_budget_df_reordered_nmoles_gc = nadh_nadph_budget_df_reordered.apply(
    convert_fluxes_to_per_guard_cell, args=[arabidopsis_supermodel, units], axis=1
)
atp_budget_df_reordered_nmoles_gc
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
Mitochondrial_ATP_Synthase_m_gc 1 0.669224 0.670681 0.713536 0.689127 0.677538 0.677538 0.705604 0.731622
2 21.155086 22.782555 13.872291 10.198964 11.595905 8.652845 15.702031 13.849520
3 1.583322 1.535555 2.241434 2.760614 2.275743 2.763883 1.894895 2.208677
4 0.679838 0.639288 0.738808 0.738808 0.677538 0.677538 0.794213 0.731622
Plastidial_ATP_Synthase_p_gc 2 0.000000 0.000000 0.000000 0.000000 10.181458 10.181458 0.000000 0.000000
3 10.181458 10.181458 10.181458 10.181458 10.181458 10.181458 0.000000 0.000000
PROTON_ATPase_c_gc 1 0.000000 0.000000 -0.017673 0.000000 0.000000 0.000000 -0.042563 -0.053016
2 -13.672759 -14.020417 -7.500000 -7.500000 -7.500000 -7.500000 -7.500000 -7.500000
3 0.000000 0.000000 -0.260423 -0.265067 -0.203159 -0.244605 -0.501318 -0.535036
4 0.000000 0.000000 -0.022759 -0.022759 0.000000 0.000000 -0.078163 -0.053016
PROTONATP_rev_vc_gc 2 -7.207308 -6.355528 -7.483600 -3.339336 -9.297846 -3.125109 -10.075724 -7.457913
PHOSGLYPHOS_RXN_p_gc 2 0.000000 0.000000 0.000000 0.000000 -3.746772 -4.727792 0.000000 0.000000
3 -6.988877 -6.980481 -6.824133 -6.661311 -6.853493 -6.690779 0.133320 0.000000
ATPase_tx_gc 1 -0.765303 -0.765303 -0.765303 -0.765303 -0.765303 -0.765303 -0.765303 -0.765303
2 -0.765303 -0.765303 -0.765303 -0.765303 -0.920514 -0.920514 -0.765303 -0.765303
3 -0.920514 -0.920514 -0.920514 -0.920514 -0.920514 -0.920514 -0.765303 -0.765303
4 -0.765303 -0.765303 -0.765303 -0.765303 -0.765303 -0.765303 -0.765303 -0.765303
PHOSPHORIBULOKINASE_RXN_p_gc 2 0.000000 0.000000 0.000000 0.000000 -1.873386 -2.363896 0.000000 0.000000
3 -3.542150 -3.550111 -3.432465 -3.345935 -3.426746 -3.345390 0.000000 0.000000
SUCCCOASYN_RXN_m_gc 1 0.000000 0.048600 0.020251 0.039811 0.049097 0.049097 0.051131 0.053016
2 2.337709 1.401572 1.876615 1.405675 0.000000 0.000000 2.111197 1.873695
3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.087425 0.106920
4 0.000000 0.046333 0.000000 0.000000 0.049097 0.049097 0.057552 0.053016
UDPKIN_RXN_c_gc 2 -0.205269 -0.338098 0.000000 0.000000 1.561155 -0.196991 0.000000 0.000000
3 -0.229408 -0.265907 0.000000 -0.763874 0.000000 -0.767206 -0.056512 -0.428496
4 0.000000 0.034796 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
FRUCTOKINASE_RXN_c_gc 1 0.000000 -0.035270 -0.041754 -0.041728 -0.041715 -0.041715 -0.042563 -0.062684
3 0.000000 0.000000 -0.216839 -0.985371 -0.241082 -0.976847 -0.557830 -0.963532
4 0.000000 0.000000 -0.041781 -0.041781 -0.041715 -0.041715 -0.078163 -0.062684
PHOSGLYPHOS_RXN_c_gc 1 0.045450 0.048600 0.049189 0.049127 0.049097 0.049097 0.051131 0.053016
2 -0.821078 -1.352391 0.000000 0.000000 0.000000 0.000000 1.055598 0.000000
3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.188385
4 0.045323 0.046326 0.049254 0.049254 0.049097 0.049097 0.041089 0.053016
PEPCARBOXYKIN_RXN_c_gc 2 -0.975561 -1.352391 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
GLUC1PADENYLTRANS_RXN_p_gc 3 -0.083832 0.000000 -0.768519 0.000000 -0.812206 0.000000 -0.367998 0.000000
4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.032925 0.000000
PEPDEPHOS_RXN_c_gc 1 0.045375 0.048600 0.049189 0.049127 0.049097 0.049097 0.051131 0.053016
2 0.154483 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.188385
4 0.040143 0.046326 0.049254 0.049254 0.049097 0.049097 0.041089 0.053016
6PFRUCTPHOS_RXN_c_gc 2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.527799 0.000000
4 0.000000 -0.040561 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
PEPDEPHOS_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.133320 0.000000

Carbon movement df generation

apoplastic_linker_reactions = [
    reaction.id[:-2] for reaction in four_stage_GC_model.reactions if "a_Linker" in reaction.id
]
get_relevant_reaction_fluxes(solutions_df.loc[(apoplastic_linker_reactions, slice(None)), :])
/tmp/ipykernel_5280/3164465358.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  solution_df = solution_df.drop(scenarios_to_drop, axis=1)  # only keep the scenarios that we are interested in
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
SUCROSE_a_Linker 3 0.00159 0.001729 0.0 0.0 0.0 0.0 0.000420 0.000369
4 0.00000 0.000000 0.0 0.0 0.0 0.0 0.000148 0.000184 
transfer_reactions = [reaction.id[:-2] for reaction in four_stage_GC_model.reactions if "ae_gc" in reaction.id]
transfer_reactions = [e for e in transfer_reactions if e not in ("WATER_ae_gc", "K_ae_gc", "Cl_ae_gc", "NITRATE_ae_gc")]
transfer_reactions = solutions_df.loc[(transfer_reactions, slice(None)), :]
transfer_reactions = get_relevant_reaction_fluxes(transfer_reactions)
transfer_reactions.loc[("GLC_ae_gc", 2), :] = 0
transfer_reactions
/tmp/ipykernel_5280/3164465358.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  solution_df = solution_df.drop(scenarios_to_drop, axis=1)  # only keep the scenarios that we are interested in
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
SUCROSE_ae_gc 1 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000025 0.000031
3 -0.000138 -0.00015 0.000151 0.000154 0.000118 0.000142 0.000291 0.000310
4 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000045 0.000031
GLC_ae_gc 3 -0.000024 0.00000 -0.000597 -0.000597 -0.000550 -0.000592 -0.000570 -0.000597
2 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 
transfer_reactions_me = [reaction.id[:-2] for reaction in four_stage_GC_model.reactions if "ae_me" in reaction.id]
transfer_reactions_me = [
    e
    for e in transfer_reactions_me
    if e
    not in (
        "WATER_ae_me",
        "K_ae_me",
        "Cl_ae_me",
        "NITRATE_ae_me",
        "SULFATE_ae_me",
        "AMMONIUM_ae_me",
    )
]
transfer_reactions_me = get_relevant_reaction_fluxes(solutions_df.loc[(transfer_reactions_me, slice(None)), :])
transfer_reactions_me
/tmp/ipykernel_5280/3164465358.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  solution_df = solution_df.drop(scenarios_to_drop, axis=1)  # only keep the scenarios that we are interested in
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
SUCROSE_ae_me 3 0.000000 0.000000 -0.000151 -0.000154 -0.000118 -0.000142 -0.000327 -0.000342
4 0.000265 0.000288 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
GLC_ae_me 3 0.000024 0.000000 0.000597 0.000597 0.000550 0.000592 0.000570 0.000597 
def adjust_for_hexose_equivalents(series_object):
    hexose_equivalents = {
        "GLC": 1,
        "SUCROSE": 2,
        "FRU": 1,
    }

    metabolite = series_object.name[0].split("_")[0]
    hexose_equivalent = hexose_equivalents[metabolite]

    return series_object * hexose_equivalent
from mmon_gcm.analysing import adjust_for_phases
transfer_reactions_me
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
SUCROSE_ae_me 3 0.000000 0.000000 -0.000151 -0.000154 -0.000118 -0.000142 -0.000327 -0.000342
4 0.000265 0.000288 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
GLC_ae_me 3 0.000024 0.000000 0.000597 0.000597 0.000550 0.000592 0.000570 0.000597 
me_carbon_production_consumption = (
    transfer_reactions_me.apply(adjust_for_phases, args=[four_stage_GC_model], axis=1)
    .apply(adjust_for_hexose_equivalents, axis=1)
    .sum()
    * -1
)
me_carbon_production_consumption
Light  ATPase         Starch  
blue   unconstrained  wt         -0.003459
                      starchko   -0.003458
       constrained    wt         -0.003389
                      starchko   -0.003327
white  constrained    wt         -0.003610
                      starchko   -0.003543
nops   constrained    wt          0.000971
                      starchko    0.001012
dtype: float64
gc_carbon_production_consumption = (
    transfer_reactions.loc[["GLC_ae_gc", "SUCROSE_ae_gc"], :]
    .apply(adjust_for_phases, args=[four_stage_GC_model], axis=1)
    .apply(adjust_for_hexose_equivalents, axis=1)
    * -1
)
gc_carbon_production_consumption
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
GLC_ae_gc 3 0.000280 -0.000000 0.006863 0.006863 0.00632 0.006806 0.006557 0.006863
2 -0.000000 -0.000000 -0.000000 -0.000000 -0.00000 -0.000000 -0.000000 -0.000000
SUCROSE_ae_gc 1 -0.000000 -0.000000 -0.000000 -0.000000 -0.00000 -0.000000 -0.000296 -0.000369
3 0.003179 0.003458 -0.003474 -0.003536 -0.00271 -0.003263 -0.006688 -0.007137
4 -0.000000 -0.000000 -0.000000 -0.000000 -0.00000 -0.000000 -0.000544 -0.000369 
((0.006863-0.003474)+(0.006863-0.003536))/2
0.003358
((0.00632-0.00271)+(0.006806-0.003263))/2
0.0035765000000000003
0.0035765000000000003/0.003358
1.065068493150685
(0.006863-0.003474)/(0.006863-0.003536)
1.0186354072738202
(0.00632-0.00271)/(0.006806-0.003263)
1.0189105278012982
gc_carbon_production_consumption_nmoles_gc = gc_carbon_production_consumption.apply(
    convert_fluxes_to_per_guard_cell, args=[arabidopsis_supermodel, units], axis=1
)
atp_colours = {
    "6PFRUCTPHOS_RXN_c_gc": [6, 6],  # phosphofructokinase glycolysis
    "FRUCTOKINASE_RXN_c_gc": [6, 6],  # fructokinase glycolysis
    "GLUC1PADENYLTRANS_RXN_p_gc": [8, 8],  # glc1p -> adp glc (starch synthesis)
    "GLY3KIN_RXN_p_gc": [6, 6],  # g3p -> glycerate (carbon degradation)
    "Mitochondrial_ATP_Synthase_m_gc": [7, 7],  # mitochondrial atp synthase
    "PEPCARBOXYKIN_RXN_c_gc": [6, 6],  # OAA -> pyruvate (mal deg)
    "PEPDEPHOS_RXN_c_gc": [6, 6],  # PEP -> pyruvate
    "PEPDEPHOS_RXN_p_gc": [6, 6],  # PEP -> pyruvate
    "PHOSGLYPHOS_RXN_c_gc": [2, 6],  # DPG -> G3p (glycolysis)
    "PHOSGLYPHOS_RXN_p_gc": [4, 4],  # DPG -> G3p (glycolysis)
    "PHOSPHORIBULOKINASE_RXN_p_gc": [4, 4],  # calvin cycle
    "PROTONATP_rev_vc_gc": [0, 0],  # tonoplastic proton pump
    "PROTON_ATPase_c_gc": [1, 1],  # plasma membrane proton pump
    "Plastidial_ATP_Synthase_p_gc": [3, 3],  # plastidial atp_synthase
    "SUCCCOASYN_RXN_m_gc": [6, 6],  # TCA cycle
    "UDPKIN_RXN_c_gc": [2, 2],  # atp->utp, sucrose synthesis
    "GLUCOKIN_RXN_c_gc": [9, 9],
    "ATP_ADP_mc_gc": [9, 9],  # ATP shuttle
    "ATPase_tx_gc": [9, 9],  # maintenance
    "PYRUVATEORTHOPHOSPHATE_DIKINASE_RXN_c_gc": [9, 9],
    "ADENYL_KIN_RXN_m_gc": [9, 9],
    "6PFRUCTPHOS_RXN_p_gc": [9, 9],
}

nadh_nadph_colours = {
    "1_PERIOD_18_PERIOD_1_PERIOD_2_RXN_p_gc": [3, 3],  # ps
    "1_PERIOD_2_PERIOD_1_PERIOD_13_RXN_p_gc": [4, 4],  # ps
    "2OXOGLUTARATEDEH_RXN_m_gc": [6, 6],  # tca
    "GAPOXNPHOSPHN_RXN_c_gc": [2, 6],  # glycolysis or gluconeogenesis
    "GAPOXNPHOSPHN_RXN_p_gc": [4, 4],  # calvin cycle but nadh
    "HYDROXYPYRUVATE_REDUCTASE_RXN_NAD_x_gc": [
        5,
        5,
    ],  # OH_PYR_x -> Glycerate_x to do with serine/aspartate metabolism
    "ISOCITRATE_DEHYDROGENASE_NAD_RXN_m_gc": [6, 6],  # tca
    "MALATE_DEHYDROGENASE_NADPs_RXN_p_gc": [9, 9],  # shuttle
    "MALATE_DEH_RXN_c_gc": [
        9,
        6,
    ],  # malate degradation when forward but also shuttle in reverse
    "MALATE_DEH_RXN_m_gc": [9, 9],  # can be tca but mainly shuttle
    "MALATE_DEH_RXN_p_gc": [9, 9],  # shuttle
    "MALATE_DEH_RXN_x_gc": [9, 9],  # shuttle
    "MALIC_NAD_RXN_m_gc": [6, 6],  # malate degradation (al->pyr)
    "NADH_DEHYDROG_A_RXN_mi_gc": [7, 7],  # complex 1 (TC)
    #"PGLYCDEHYDROG_RXN_p_gc": [5, 5],  # ser_synthesis from g3p
    "PYRUVDEH_RXN_m_gc": [6, 6],  # pyruvate degradation
    "ISOCITDEH_RXN_c_gc": [6, 6],  # tca nadp
    "6PGLUCONDEHYDROG_RXN_p_gc": [9, 9],  # PPP
    "NADPHox_c_tx_gc": [9, 9],
    "NADPHox_p_tx_gc": [9, 9],
    "GLU6PDEHYDROG_RXN_c_gc": [9, 9],
    "6PGLUCONDEHYDROG_RXN_c_gc": [9, 9],
    "GLU6PDEHYDROG_RXN_p_gc": [9, 9],
    "MALIC_NADP_RXN_c_gc": [9, 9],
}

reaction_colours = {**atp_colours, **nadh_nadph_colours}
for reaction, colour_number in reaction_colours.items():
    for i, reverse_forward in enumerate(colour_number):
        if reverse_forward == 9:
            reaction_colours[reaction][i] = "#DDDDDD"
        else:
            reaction_colours[reaction][i] = colours[reverse_forward]
colours_legend = {
    "Tonoplast membrane ATPase": 0,
    "Plasma membrane ATPase": 1,
    "Sucrose/Glucose synthesis": 2,
    "Plastid/Photosynthesis": 3,
    "Carbon fixation": 4,
    #"Serine Synthesis": 5,
    "Carbon degradation\n(Glycolysis, TCA etc.)": 6,
    "ETC/Mitochondrial\nATP Synthase": 7,
    "Starch synthesis": 8,
    "Other e.g. shuttles, maintenance": 9,
}
for category, colour_number in colours_legend.items():
    if colour_number == 9:
        colours_legend[category] = "#DDDDDD"
    else:
        colours_legend[category] = colours[colour_number]
carbon_colours = {
    "SUCROSE_ae_gc": colours[2],
    "GLC_ae_gc": colours[6],
    "FRU_ae_gc": colours[4],
}
legend_colours = {
    "Glucose": colours[6],
    "Sucrose": colours[2],
}
atp_budget_df_reordered_nmoles_gc.index
MultiIndex([('Mitochondrial_ATP_Synthase_m_gc', 1),
            ('Mitochondrial_ATP_Synthase_m_gc', 2),
            ('Mitochondrial_ATP_Synthase_m_gc', 3),
            ('Mitochondrial_ATP_Synthase_m_gc', 4),
            (   'Plastidial_ATP_Synthase_p_gc', 2),
            (   'Plastidial_ATP_Synthase_p_gc', 3),
            (             'PROTON_ATPase_c_gc', 1),
            (             'PROTON_ATPase_c_gc', 2),
            (             'PROTON_ATPase_c_gc', 3),
            (             'PROTON_ATPase_c_gc', 4),
            (            'PROTONATP_rev_vc_gc', 2),
            (           'PHOSGLYPHOS_RXN_p_gc', 2),
            (           'PHOSGLYPHOS_RXN_p_gc', 3),
            (                   'ATPase_tx_gc', 1),
            (                   'ATPase_tx_gc', 2),
            (                   'ATPase_tx_gc', 3),
            (                   'ATPase_tx_gc', 4),
            (   'PHOSPHORIBULOKINASE_RXN_p_gc', 2),
            (   'PHOSPHORIBULOKINASE_RXN_p_gc', 3),
            (            'SUCCCOASYN_RXN_m_gc', 1),
            (            'SUCCCOASYN_RXN_m_gc', 2),
            (            'SUCCCOASYN_RXN_m_gc', 3),
            (            'SUCCCOASYN_RXN_m_gc', 4),
            (                'UDPKIN_RXN_c_gc', 2),
            (                'UDPKIN_RXN_c_gc', 3),
            (                'UDPKIN_RXN_c_gc', 4),
            (          'FRUCTOKINASE_RXN_c_gc', 1),
            (          'FRUCTOKINASE_RXN_c_gc', 3),
            (          'FRUCTOKINASE_RXN_c_gc', 4),
            (           'PHOSGLYPHOS_RXN_c_gc', 1),
            (           'PHOSGLYPHOS_RXN_c_gc', 2),
            (           'PHOSGLYPHOS_RXN_c_gc', 3),
            (           'PHOSGLYPHOS_RXN_c_gc', 4),
            (         'PEPCARBOXYKIN_RXN_c_gc', 2),
            (     'GLUC1PADENYLTRANS_RXN_p_gc', 3),
            (     'GLUC1PADENYLTRANS_RXN_p_gc', 4),
            (             'PEPDEPHOS_RXN_c_gc', 1),
            (             'PEPDEPHOS_RXN_c_gc', 2),
            (             'PEPDEPHOS_RXN_c_gc', 3),
            (             'PEPDEPHOS_RXN_c_gc', 4),
            (           '6PFRUCTPHOS_RXN_c_gc', 2),
            (           '6PFRUCTPHOS_RXN_c_gc', 4),
            (             'PEPDEPHOS_RXN_p_gc', 3)],
           names=['Reaction', 'Phase'])
nadh_nadph_budget_df_reordered_nmoles_gc
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
1_PERIOD_18_PERIOD_1_PERIOD_2_RXN_p_gc 2 0.000000 0.000000 0.000000 0.000000 7.918912 7.918912 0.000000 0.000000
3 7.918912 7.918912 7.918912 7.918912 7.918912 7.918912 0.000000 0.000000
1_PERIOD_2_PERIOD_1_PERIOD_13_RXN_p_gc 2 0.000000 0.000000 0.000000 0.000000 -3.746772 -4.727792 0.000000 0.000000
3 -6.988877 -6.980481 -6.824133 -6.661311 -6.853493 -6.690779 0.000000 0.000000
NADH_DEHYDROG_A_RXN_mi_gc 1 -0.216814 -0.194400 -0.225695 -0.205822 -0.196388 -0.196388 -0.204523 -0.212064
... ... ... ... ... ... ... ... ... ...
6PGLUCONDEHYDROG_RXN_p_gc 1 0.034942 0.034417 0.034319 0.034329 0.034334 0.034334 0.033995 0.033681
3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.020297 0.024697
4 0.040143 0.034796 0.034308 0.034308 0.034334 0.034334 0.032925 0.033681
MALATE_DEH_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.133320 0.000000
GAPOXNPHOSPHN_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.133320 0.000000

62 rows × 8 columns

color_order = [
    [nadh_nadph_colours[reaction][1], reaction] for reaction in nadh_nadph_budget_df_reordered_nmoles_gc.index.levels[0]
]
nadh_nadph_budget_df_reordered_nmoles_gc = nadh_nadph_budget_df_reordered_nmoles_gc.loc[
    pd.DataFrame(color_order).sort_values(by=[0, 1]).loc[:, 1].values
]
color_order = [[atp_colours[reaction][1], reaction] for reaction in atp_budget_df_reordered_nmoles_gc.index.levels[0]]
atp_budget_df_reordered_nmoles_gc = atp_budget_df_reordered_nmoles_gc.loc[
    pd.DataFrame(color_order).sort_values(by=[0, 1]).loc[:, 1].values
]
atp_budget_df_reordered_nmoles_gc.drop("nops", axis=1).loc[("Plastidial_ATP_Synthase_p_gc", 3)].mean()
/tmp/ipykernel_5280/544428024.py:1: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
  atp_budget_df_reordered_nmoles_gc.drop("nops", axis=1).loc[("Plastidial_ATP_Synthase_p_gc", 3)].mean()
10.181457681507931
(abs(nadh_nadph_budget_df_reordered_nmoles_gc) / 2).sum()
Light  ATPase         Starch  
blue   unconstrained  wt          16.205902
                      starchko    17.776325
       constrained    wt          13.499895
                      starchko    12.706483
white  constrained    wt          21.462877
                      starchko    20.644570
nops   constrained    wt           7.282741
                      starchko     5.903313
dtype: float64
nadh_nadph_budget_df_reordered_nmoles_gc
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
1_PERIOD_18_PERIOD_1_PERIOD_2_RXN_p_gc 2 0.000000 0.000000 0.000000 0.000000 7.918912 7.918912 0.000000 0.000000
3 7.918912 7.918912 7.918912 7.918912 7.918912 7.918912 0.000000 0.000000
NADH_DEHYDROG_A_RXN_mi_gc 1 -0.216814 -0.194400 -0.225695 -0.205822 -0.196388 -0.196388 -0.204523 -0.212064
2 -5.649070 -6.753242 -3.498128 -2.556250 -3.865302 -2.884282 -3.967292 -3.492290
3 -0.527774 -0.511852 -0.747145 -0.920205 -0.758581 -0.921294 -0.579177 -0.672074
... ... ... ... ... ... ... ... ... ...
NADPHox_c_tx_gc 4 -0.174815 -0.185509 -0.186485 -0.186485 -0.186433 -0.186433 -0.189251 -0.187739
NADPHox_p_tx_gc 1 -0.069884 -0.068834 -0.068637 -0.068658 -0.068668 -0.068668 -0.067990 -0.067362
2 0.000000 0.000000 0.000000 0.000000 -0.306838 -0.306838 0.000000 0.000000
3 -0.306838 -0.306838 -0.306838 -0.306838 -0.306838 -0.306838 -0.040594 -0.049394
4 -0.080286 -0.069592 -0.068616 -0.068616 -0.068668 -0.068668 -0.065850 -0.067362

62 rows × 8 columns

(abs(nadh_nadph_budget_df_reordered_nmoles_gc.loc[(slice(None), 3), :])/2).sum()
Light  ATPase         Starch  
blue   unconstrained  wt          8.446685
                      starchko    8.430763
       constrained    wt          8.666056
                      starchko    8.839116
white  constrained    wt          8.677492
                      starchko    8.840206
nops   constrained    wt          0.967598
                      starchko    1.115560
dtype: float64
nadh_nadph_budget_df_reordered_nmoles_gc.loc[(slice(None), 3), :]
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
1_PERIOD_18_PERIOD_1_PERIOD_2_RXN_p_gc 3 7.918912 7.918912 7.918912 7.918912 7.918912 7.918912 0.000000 0.000000
NADH_DEHYDROG_A_RXN_mi_gc 3 -0.527774 -0.511852 -0.747145 -0.920205 -0.758581 -0.921294 -0.579177 -0.672074
1_PERIOD_2_PERIOD_1_PERIOD_13_RXN_p_gc 3 -6.988877 -6.980481 -6.824133 -6.661311 -6.853493 -6.690779 0.000000 0.000000
GAPOXNPHOSPHN_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.133320 0.000000
2OXOGLUTARATEDEH_RXN_m_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.087425 0.106920
GAPOXNPHOSPHN_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.188385
ISOCITDEH_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.087425 0.106920
MALATE_DEH_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.188385
PYRUVDEH_RXN_m_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.179216 0.188385
6PGLUCONDEHYDROG_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.040594 0.049394
6PGLUCONDEHYDROG_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.020297 0.024697
GLU6PDEHYDROG_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.040594 0.049394
GLU6PDEHYDROG_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.020297 0.024697
MALATE_DEHYDROGENASE_NADPs_RXN_p_gc 3 -0.623197 -0.631593 -0.787941 -0.950763 -0.758581 -0.921294 0.000000 0.000000
MALATE_DEH_RXN_m_gc 3 0.527774 0.511852 0.747145 0.920205 0.758581 0.921294 0.312536 0.376769
MALATE_DEH_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.133320 0.000000
MALIC_NADP_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.045896 0.000000
NADPHox_c_tx_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.214507 -0.205707
NADPHox_p_tx_gc 3 -0.306838 -0.306838 -0.306838 -0.306838 -0.306838 -0.306838 -0.040594 -0.049394 
((0.984348+1.115560)/2) / ((8.446685+8.430763+8.666056+8.839116+8.677492+8.840206)/6)
0.12138122159482721
((0.984348+1.115560)/2) / ((8.666056+8.839116+8.677492+8.840206)/4)
0.11991638606430598
nops_nadph_3 = nadh_nadph_budget_df_reordered_nmoles_gc.loc[(slice(None), 3), "nops"].index
colours_legend_iv = {v: k for k, v in colours_legend.items()}
nops_nadph_3_df = nadh_nadph_budget_df_reordered_nmoles_gc.loc[(slice(None), 3), "nops"]
nops_nadph_3
MultiIndex([('1_PERIOD_18_PERIOD_1_PERIOD_2_RXN_p_gc', 3),
            (             'NADH_DEHYDROG_A_RXN_mi_gc', 3),
            ('1_PERIOD_2_PERIOD_1_PERIOD_13_RXN_p_gc', 3),
            (                'GAPOXNPHOSPHN_RXN_p_gc', 3),
            (             '2OXOGLUTARATEDEH_RXN_m_gc', 3),
            (                'GAPOXNPHOSPHN_RXN_c_gc', 3),
            (                    'ISOCITDEH_RXN_c_gc', 3),
            (                   'MALATE_DEH_RXN_c_gc', 3),
            (                     'PYRUVDEH_RXN_m_gc', 3),
            (             '6PGLUCONDEHYDROG_RXN_c_gc', 3),
            (             '6PGLUCONDEHYDROG_RXN_p_gc', 3),
            (                'GLU6PDEHYDROG_RXN_c_gc', 3),
            (                'GLU6PDEHYDROG_RXN_p_gc', 3),
            (   'MALATE_DEHYDROGENASE_NADPs_RXN_p_gc', 3),
            (                   'MALATE_DEH_RXN_m_gc', 3),
            (                   'MALATE_DEH_RXN_p_gc', 3),
            (                   'MALIC_NADP_RXN_c_gc', 3),
            (                       'NADPHox_c_tx_gc', 3),
            (                       'NADPHox_p_tx_gc', 3)],
           names=['Reaction', 'Phase'])
nops_nadph_3_reactions = [i[0] for i in nops_nadph_3]
descriptions = []
for reaction in nops_nadph_3_reactions:
    descriptions.append(colours_legend_iv[nadh_nadph_colours[reaction][0]].replace("\n", ""))
nops_nadph_3_df.loc[:, "Description"] = descriptions
nops_nadph_3_df
ATPase constrained Description
Starch wt starchko
Reaction Phase
1_PERIOD_18_PERIOD_1_PERIOD_2_RXN_p_gc 3 0.000000 0.000000 Plastid/Photosynthesis
NADH_DEHYDROG_A_RXN_mi_gc 3 -0.579177 -0.672074 ETC/MitochondrialATP Synthase
1_PERIOD_2_PERIOD_1_PERIOD_13_RXN_p_gc 3 0.000000 0.000000 Carbon fixation
GAPOXNPHOSPHN_RXN_p_gc 3 0.133320 0.000000 Carbon fixation
2OXOGLUTARATEDEH_RXN_m_gc 3 0.087425 0.106920 Carbon degradation(Glycolysis, TCA etc.)
GAPOXNPHOSPHN_RXN_c_gc 3 0.000000 0.188385 Sucrose/Glucose synthesis
ISOCITDEH_RXN_c_gc 3 0.087425 0.106920 Carbon degradation(Glycolysis, TCA etc.)
MALATE_DEH_RXN_c_gc 3 0.000000 -0.188385 Other e.g. shuttles, maintenance
PYRUVDEH_RXN_m_gc 3 0.179216 0.188385 Carbon degradation(Glycolysis, TCA etc.)
6PGLUCONDEHYDROG_RXN_c_gc 3 0.040594 0.049394 Other e.g. shuttles, maintenance
6PGLUCONDEHYDROG_RXN_p_gc 3 0.020297 0.024697 Other e.g. shuttles, maintenance
GLU6PDEHYDROG_RXN_c_gc 3 0.040594 0.049394 Other e.g. shuttles, maintenance
GLU6PDEHYDROG_RXN_p_gc 3 0.020297 0.024697 Other e.g. shuttles, maintenance
MALATE_DEHYDROGENASE_NADPs_RXN_p_gc 3 0.000000 0.000000 Other e.g. shuttles, maintenance
MALATE_DEH_RXN_m_gc 3 0.312536 0.376769 Other e.g. shuttles, maintenance
MALATE_DEH_RXN_p_gc 3 -0.133320 0.000000 Other e.g. shuttles, maintenance
MALIC_NADP_RXN_c_gc 3 0.045896 0.000000 Other e.g. shuttles, maintenance
NADPHox_c_tx_gc 3 -0.214507 -0.205707 Other e.g. shuttles, maintenance
NADPHox_p_tx_gc 3 -0.040594 -0.049394 Other e.g. shuttles, maintenance 
atp_budget_df_reordered_nmoles_gc.columns
MultiIndex([( 'blue', 'unconstrained',       'wt'),
            ( 'blue', 'unconstrained', 'starchko'),
            ( 'blue',   'constrained',       'wt'),
            ( 'blue',   'constrained', 'starchko'),
            ('white',   'constrained',       'wt'),
            ('white',   'constrained', 'starchko'),
            ( 'nops',   'constrained',       'wt'),
            ( 'nops',   'constrained', 'starchko')],
           names=['Light', 'ATPase', 'Starch'])
gc_carbon_production_consumption_nmoles_gc = gc_carbon_production_consumption_nmoles_gc.loc[:, gc_carbon_production_consumption_nmoles_gc.columns.get_level_values(1) != 'unconstrained']
gc_carbon_production_consumption_nmoles_gc
Light blue white nops
ATPase constrained constrained constrained
Starch wt starchko wt starchko wt starchko
Reaction Phase
GLC_ae_gc 3 11.832823 11.832822 10.896129 11.734326 11.305022 11.832822
2 -0.000000 -0.000000 -0.000000 -0.000000 -0.000000 -0.000000
SUCROSE_ae_gc 1 -0.000000 -0.000000 -0.000000 -0.000000 -0.510755 -0.636193
3 -5.989718 -6.096542 -4.672668 -5.625923 -11.530315 -12.305836
4 -0.000000 -0.000000 -0.000000 -0.000000 -0.937958 -0.636193 
metabolites = ["ATP", "NAD(P)H"]
phases = ["Opening", "Day"]
hatches = {"wt": None, "starchko": "//"}
atpase_labelling = {"unconstrained": "–", "constrained": "+"}

fig, ax = plt.subplots(1, 1, sharex=True, sharey="row")
plt.subplots_adjust(hspace=0.1)

colour_rectangles_thickness = 4 / (75 * 2)

y_lim_for_hexose = 49

for x1, x2, colour in zip([-0.5, 1.5, 3.5], [1.5, 3.5, 5.5], ["#004488", "white", "#DDDDDD"]):
    ax.fill_betweenx(
        [30 - colour_rectangles_thickness * 30 * 2, 30],
        x1,
        x2,
        alpha=1,
        color=colour,
        linewidth=0.5,
        edgecolor="black",
    )
for i in range(6):
    if i % 2 == 0:
        ax.text(i-0.2, 30-4, "WT")
    else:
        ax.text(i-0.2, 30-4, "KO")

phase = 3
for i, scenario in enumerate(gc_carbon_production_consumption_nmoles_gc.columns):
    positive_bottom = 0
    negative_top = 0
    scenario_df = gc_carbon_production_consumption_nmoles_gc.xs(phase, level="Phase").loc[:, scenario]
    for reaction in scenario_df.index:
        value = scenario_df.loc[reaction]
        if value > 0.00001:
            ax.bar(
                i,
                value,
                bottom=positive_bottom,
                color=carbon_colours[reaction],
                linewidth=0.1,
                hatch=hatches[scenario[2]],
                align="edge",
                width=-0.4,
            )
            positive_bottom += value
        elif value < 0.00001:
            ax.bar(
                i,
                value,
                bottom=negative_top,
                color=carbon_colours[reaction],
                linewidth=0.1,
                hatch=hatches[scenario[2]],
                align="edge",
                width=0.4,
            )
            negative_top += abs(value)
    if positive_bottom == 0:
        ax.bar(
            i,
            0.3,
            bottom=0,
            color=carbon_colours[reaction],
            linewidth=0.1,
            hatch=hatches[scenario[2]],
            align="edge",
            width=-0.4,
        )
    if negative_top == 0:
        ax.bar(
            i,
            0.3,
            bottom=0,
            color=carbon_colours[reaction],
            linewidth=0.1,
            hatch=hatches[scenario[2]],
            align="edge",
            width=0.4,
        )
    if positive_bottom > 0:
        ax.text(
            i - 0.2,
            positive_bottom + 0.5,
            f"+{round(positive_bottom, 2)}",
            ha="center",
            va="bottom",
            size="small",
            #rotation=90,
        )
    if negative_top > 0:
        ax.text(
            i + 0.2,
            - negative_top - 2,
            f"–{round(negative_top, 2)}",
            ha="center",
            va="bottom",
            size="small",
            #rotation=90,
        )
    if positive_bottom > 0 and negative_top > 0:
        net_value = round(positive_bottom - negative_top, 2)
        if net_value < 0:
            ax.text(
                i - 0.0,
                22,
                f"–{abs(net_value)}",
                ha="center",
                va="center",
                size="small",
            )
        else:
            ax.text(
                i - 0.0,
                22,
                f"+{net_value}",
                ha="center",
                va="center",
                size="small",
            )
    ax.text(-0.45, 22, "Net:", ha="center", va="center", size="small")


patchList = []
for key in legend_colours:
    data_key = mpatches.Patch(color=legend_colours[key], label=key)
    patchList.append(data_key)

ax.legend(handles=patchList, loc="center left", bbox_to_anchor=(1.1, 0.5))


ax.set_xlim(-0.7, 5.7)
ax.set_ylim(-15, 30)
ax.spines["bottom"].set_visible(False)
ax.spines["left"].set_bounds(-15, 30)
ax.set_xticks([])  # range(len(budget_df.columns)))
ax.set_xticklabels([])  # [atpase_labelling[scenario[1]] for scenario in budget_df.columns])

ax.hlines(0, -0.5, 7.5, color="black", linewidth=0.5)

ax.set_ylabel(
    "Hexose equivalents \n exported (+) or imported (–) \n by GC during the day phase \n" r"(fmol$\cdot$GC$^{-1}$)",
    labelpad=15,
)

#ax.set_title('Day', pad=10)

# fig.text(-0.02,0.25, "Hexose equivalents imported (+) \n or exported (-) by GC " r"(nmoles$\cdot$GC$^{-1}$)", rotation=90, va="center", ha="center", size="large")

fig.savefig(f"../plant_cell_paper/figure_6.svg", bbox_inches="tight")
fig.savefig(f"../plant_cell_paper/figure_6.png", dpi=300, bbox_inches="tight")

atp_budget_df_reordered_nmoles_gc = atp_budget_df_reordered_nmoles_gc.loc[:, atp_budget_df_reordered_nmoles_gc.columns.get_level_values(1) != 'unconstrained']
nadh_nadph_budget_df_reordered_nmoles_gc = nadh_nadph_budget_df_reordered_nmoles_gc.loc[:, nadh_nadph_budget_df_reordered_nmoles_gc.columns.get_level_values(1) != 'unconstrained']
metabolites = ["ATP", "NAD(P)H"]
phases = ["Opening", "Day"]
hatches = {"wt": None, "starchko": "//"}
atpase_labelling = {"unconstrained": "–", "constrained": "+"}

fig, axs = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey="row")
plt.subplots_adjust(hspace=0.1)

colour_rectangles_thickness = 4 / (75 * 2)

for y, metabolite in enumerate([atp_budget_df_reordered_nmoles_gc, nadh_nadph_budget_df_reordered_nmoles_gc]):
    for x, phase in enumerate([2, 3]):
        budget_df = metabolite.xs(phase, level="Phase")
        ax = axs[y][x]
   
        if y == 0:
            for x1, x2, colour in zip([-0.5, 1.5, 3.5], [1.5, 3.5, 5.5], ["#004488", "white", "#DDDDDD"]):
                ax.fill_betweenx(
                    [30 - colour_rectangles_thickness * 30 * 2, 30],
                    x1,
                    x2,
                    alpha=1,
                    color=colour,
                    linewidth=0.5,
                    edgecolor="black",
                )
            for i in range(6):
                if i % 2 == 0:
                    ax.text(i-0.3, 30-5, "WT")
                else:
                    ax.text(i-0.3, 30-5, "KO")
        if y == 1:
            for x1, x2, colour in zip([-0.5, 1.5, 3.5], [1.5, 3.5, 5.5], ["#004488", "white", "#DDDDDD"]):
                ax.fill_betweenx(
                    [15 - colour_rectangles_thickness * 15 * 2, 15],
                    x1,
                    x2,
                    alpha=1,
                    color=colour,
                    linewidth=0.5,
                    edgecolor="black",
                )
            for i in range(6):
                if i % 2 == 0:
                    ax.text(i-0.3, 15-5/2, "WT")
                else:
                    ax.text(i-0.3, 15-5/2, "KO")
        for i, scenario in enumerate(budget_df.columns):
            positive_bottom = 0
            negative_top = 0
            scenario_df = budget_df.loc[:, scenario]
            for reaction in scenario_df.index:
                value = scenario_df.loc[reaction]
                if value > 0.00001:
                    ax.bar(
                        i,
                        value,
                        bottom=positive_bottom,
                        color=reaction_colours[reaction][1],
                        linewidth=0.1,
                        hatch=hatches[scenario[2]],
                    )
                    positive_bottom += value
                elif value < 0.00001:
                    ax.bar(
                        i,
                        value,
                        bottom=negative_top,
                        color=reaction_colours[reaction][0],
                        linewidth=0.1,
                        hatch=hatches[scenario[2]],
                    )
                    negative_top += value

        ax.set_xticks([])  # range(len(budget_df.columns)))
        ax.set_xticklabels([])  # [atpase_labelling[scenario[1]] for scenario in budget_df.columns])

        if y == 0:
            ax.set_title(phases[x], pad=10)
        if x == 0:
            ax.set_ylabel(metabolites[y])

        ax.set_xlim(-0.70, 5.7)
        if y == 0:
            ax.set_ylim(-30, 30)
            ax.spines["bottom"].set_visible(False)
            ax.spines["left"].set_bounds(-30, 30)
            ax.yaxis.set_major_locator(MultipleLocator(10))
        if y == 1:
            ax.set_ylim(-15, 15)
            ax.spines["bottom"].set_visible(False)
            ax.spines["left"].set_bounds(-15, 15)
            ax.yaxis.set_major_locator(MultipleLocator(5))

        ax.hlines(0, -0.5, 5.5, color="black", linewidth=0.5)
        

        
#starch_condition = {"wt": "WT", "starchko": "KO"}[scenario_df.name[2]]
#ax.text(i, -1.5, starch_condition, ha="center", va="center", size="small")

fig.text(
    -0.02,
    0.5,
    r"Rate of production/consumption of energy" "\n" "and reducing power (fmol$\cdot$GC$^{-1}\cdot$h$^{-1}$)",
    rotation=90,
    va="center",
    ha="center",
    size="large",
)

patchList = []
for key in colours_legend:
    data_key = mpatches.Patch(color=colours_legend[key], label=key)
    patchList.append(data_key)

fig.legend(handles=patchList, loc="center left", bbox_to_anchor=(0.925, 0.53))

i = 0
for y in range(2):
    for x in range(2):
        ax = axs[y][x]
        letter = string.ascii_uppercase[i]
        if x == 0:
            ax.text(-0.3, 1, letter, size="x-large", weight="bold", transform=ax.transAxes)
        elif x == 1:
            ax.text(-0.1, 1, letter, size="x-large", weight="bold", transform=ax.transAxes)
        i += 1

fig.savefig(f"../plant_cell_paper/figure_5.svg", bbox_inches="tight")
fig.savefig(f"../plant_cell_paper/figure_5.png", dpi=300, bbox_inches="tight")

budget_df.columns[2]
('white', 'constrained', 'wt')
carbon_rank = gc_carbon_production_consumption_nmoles_gc.sum().rank(ascending=False)
atp_2_rank = abs(atp_budget_df_reordered_nmoles_gc).xs(2, level="Phase").sum().rank(ascending=False)
atp_3_rank = abs(atp_budget_df_reordered_nmoles_gc).xs(3, level="Phase").sum().rank(ascending=False)
atp_rank = (
    (
        abs(atp_budget_df_reordered_nmoles_gc).xs(3, level="Phase") * 0.5
        + abs(atp_budget_df_reordered_nmoles_gc).xs(3, level="Phase") * 11.5
    )
    .sum()
    .rank(ascending=False)
)
nad_2_rank = abs(nadh_nadph_budget_df_reordered_nmoles_gc).xs(2, level="Phase").sum().rank(ascending=False)
nad_3_rank = abs(nadh_nadph_budget_df_reordered_nmoles_gc).xs(3, level="Phase").sum().rank(ascending=False)
nad_rank = (
    (
        abs(nadh_nadph_budget_df_reordered_nmoles_gc).xs(3, level="Phase") * 0.5
        + abs(nadh_nadph_budget_df_reordered_nmoles_gc).xs(3, level="Phase") * 11.5
    )
    .sum()
    .rank(ascending=False)
)
pd.concat(
    [carbon_rank, atp_2_rank, atp_3_rank, atp_rank, nad_2_rank, nad_3_rank, nad_rank],
    axis=1,
    keys=["Carbon", "ATP 2", "ATP 3", "ATP Day", "NAD 2", "NAD 3", "NAD Day"],
).sort_values("Carbon")
Carbon ATP 2 ATP 3 ATP Day NAD 2 NAD 3 NAD Day
Light ATPase Starch
white constrained wt 1.0 1.0 3.0 3.0 1.0 3.0 3.0
starchko 2.0 3.0 1.0 1.0 2.0 1.0 1.0
blue constrained wt 3.0 4.0 4.0 4.0 4.0 4.0 4.0
starchko 4.0 6.0 2.0 2.0 6.0 2.0 2.0
nops constrained wt 5.0 2.0 6.0 6.0 3.0 6.0 6.0
starchko 6.0 5.0 5.0 5.0 5.0 5.0 5.0
  • Starch KO leads to lower carbon consumption and higher ATP and NAD turnover
  • Constraining ATPase leads to lower efficiency and higher ATP and NAD turnover
  • PS is more complicated because it’s both phases, so won’t include
    • Removing it vs blue eads to

Bits and bobs for paper

blue_constrained_wt_osmolytes = met_df_mm.loc[:, ("blue", "constrained", "wt", "fluxes")].unstack()
os_fraction = (
    (blue_constrained_wt_osmolytes.loc[6.5] - blue_constrained_wt_osmolytes.loc[6.0])
    / (blue_constrained_wt_osmolytes.loc[6.5] - blue_constrained_wt_osmolytes.loc[6.0]).sum()
    * 100
)
os_fraction
Reaction
CIT        -4.552019
Cl         28.223095
FRU        12.619461
GLC        49.860068
K          26.428299
MAL         1.959532
STARCH     -0.009450
SUCROSE   -14.528986
dtype: float64
blue_constrained_wt_osmolytes
Reaction CIT Cl FRU GLC K MAL STARCH SUCROSE
Phase
0.0 1.555385 13.391074 0.659272 0.000000 20.798763 9.601548 0.005126 7.881207
6.0 2.469230 13.949171 0.000000 0.000000 21.356860 8.230780 0.005126 7.881207
6.5 0.000000 29.258708 6.845391 27.046452 35.692816 9.293723 0.000000 0.000000
18.0 0.000000 12.672379 1.318969 0.000000 20.080068 11.934624 0.005126 7.881207
24.0 1.555385 13.391074 0.659272 0.000000 20.798763 9.601548 0.005126 7.881207

Percentage of osmotic pressure from KCl in blue light WT

os_fraction.loc["Cl"] + os_fraction.loc["K"]
54.651393996518046

Changes in concentrations of KCl and citrate in blue light WT

(blue_constrained_wt_osmolytes.loc[6.5] - blue_constrained_wt_osmolytes.loc[6.0]).loc["Cl"] + (
    blue_constrained_wt_osmolytes.loc[6.5] - blue_constrained_wt_osmolytes.loc[6.0]
).loc["K"]
29.645493064091305
(blue_constrained_wt_osmolytes.loc[6.5] - blue_constrained_wt_osmolytes.loc[6.0]).loc["CIT"]
-2.469229739192378
solutions_df
Light blue ... nops
ATPase unconstrained constrained ... unconstrained constrained
Starch wt starchko wt starchko ... wt starchko wt starchko
Solution fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum fluxes ... maximum fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum
Reaction Phase
EX_X_pi_t_me 1 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
2 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
3 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
4 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
EX_X_pi_t_gc 1 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Cl_total_pseudolinker 4 0.003642 NaN NaN 0.004839 NaN NaN 0.002951 NaN NaN 0.000158 ... NaN 0.000654 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
CIT_total_pseudolinker 1 0.000559 0.0 0.010896 0.000000 0.0 0.010896 0.000544 0.0 0.010896 0.000408 ... 0.010896 0.000534 0.0 0.010896 0.000612 0.0 0.010896 0.000543 0.0 0.010896
2 0.000000 0.0 0.011386 0.000000 0.0 0.011386 0.000000 0.0 0.011386 0.000000 ... 0.011386 0.000000 0.0 0.011386 0.000000 0.0 0.011386 0.000000 0.0 0.009964
3 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000534 NaN NaN 0.000612 NaN NaN 0.000543 NaN NaN
4 0.000315 NaN NaN 0.000000 NaN NaN 0.000343 NaN NaN 0.000343 ... NaN 0.000534 NaN NaN 0.000612 NaN NaN 0.000543 NaN NaN

7101 rows × 36 columns

Import same amount of KCl from apoplast between wt and starchko for constrained ATPase

(
    solutions_df.xs("fluxes", level="Solution", axis=1).loc[("K_ec_gc", 2), :]
    + solutions_df.xs("fluxes", level="Solution", axis=1).loc[("Cl_PROTON_ec_gc", 2), :]
)
Light  ATPase         Starch  
blue   unconstrained  wt          0.023791
                      starchko    0.024396
       constrained    wt          0.013050
                      starchko    0.013050
white  unconstrained  wt          0.024203
                      starchko    0.024209
       constrained    wt          0.013050
                      starchko    0.013050
nops   unconstrained  wt          0.022778
                      starchko    0.022778
       constrained    wt          0.013050
                      starchko    0.013050
dtype: float64
blue_constrained_starchko_osmolytes = met_df_mm.loc[:, ("blue", "constrained", "starchko", "fluxes")].unstack()
os_fraction = (
    (blue_constrained_wt_osmolytes.loc[6.5] - blue_constrained_wt_osmolytes.loc[6.0])
    / (blue_constrained_wt_osmolytes.loc[6.5] - blue_constrained_wt_osmolytes.loc[6.0]).sum()
    * 100
)
os_fraction
Reaction
CIT        -4.552019
Cl         28.223095
FRU        12.619461
GLC        49.860068
K          26.428299
MAL         1.959532
STARCH     -0.009450
SUCROSE   -14.528986
dtype: float64

Proportion of sucrose accumulation overnight between WT and starchko in blue light

blue_constrained_starchko_osmolytes.loc[6.0].loc['SUCROSE']/blue_constrained_wt_osmolytes.loc[6.0].loc['SUCROSE']
3.951044937318112

Concentration of glucose & fructose in blue starch KO (all in v, no c)

blue_constrained_starchko_osmolytes.loc[6.5].loc['GLC'] + blue_constrained_starchko_osmolytes.loc[6.5].loc['FRU']
54.09289836861832
blue_constrained_wt_osmolytes.loc[[6, 6.5]]
Reaction CIT Cl FRU GLC K MAL STARCH SUCROSE
Phase
6.0 2.46923 13.949171 0.000000 0.000000 21.356860 8.230780 0.005126 7.881207
6.5 0.00000 29.258708 6.845391 27.046452 35.692816 9.293723 0.000000 0.000000

Compare sucrose accumulation between starch KO and WT in blue light

blue_constrained_starchko_osmolytes.loc[6.0].loc['SUCROSE']/blue_constrained_wt_osmolytes.loc[6.0].loc['SUCROSE']
3.951044937318112
white_constrained_wt_osmolytes = met_df_mm.loc[:, ("white", "constrained", "wt", "fluxes")].unstack()
white_constrained_starchko_osmolytes = met_df_mm.loc[:, ("white", "constrained", "starchko", "fluxes")].unstack()
white_constrained_wt_osmolytes.loc[[6, 6.5]]
Reaction CIT Cl FRU GLC K MAL STARCH SUCROSE
Phase
6.0 0.0 4.731174 0.000000 0.000000 4.731174 0.0 0.005417 6.148247
6.5 0.0 21.252220 7.481205 24.905437 21.252220 0.0 0.000000 0.000000 
nops_constrained_wt_osmolytes = met_df_mm.loc[:, ("nops", "constrained", "wt", "fluxes")].unstack()
nops_constrained_starchko_osmolytes = met_df_mm.loc[:, ("nops", "constrained", "starchko", "fluxes")].unstack()
nops_constrained_wt_osmolytes.loc[[6, 6.5]]
Reaction CIT Cl FRU GLC K MAL STARCH SUCROSE
Phase
6.0 2.77789 0.000000 0.000000 0.000000 8.333669 0.000000 0.002569 18.787893
6.5 0.00000 17.142857 16.318627 25.840051 24.381244 3.619193 0.000000 0.000000 
nops_constrained_starchko_osmolytes.loc[[6, 6.5]]
Reaction CIT Cl FRU GLC K MAL STARCH SUCROSE
Phase
6.0 2.465388 0.000000 0.000000 0.000000 7.396173 8.217966 0.0 31.139004
6.5 0.000000 17.142857 27.046449 27.046449 23.566961 9.279256 0.0 0.000000

Compare sucrose and starch between white and blue light in WT

(white_constrained_wt_osmolytes.loc[6.0].loc['SUCROSE']/blue_constrained_wt_osmolytes.loc[6.0].loc['SUCROSE'])-1
-0.2198850653158203
(white_constrained_wt_osmolytes.loc[6.0].loc['STARCH']/blue_constrained_wt_osmolytes.loc[6.0].loc['STARCH'])-1
0.0568466742650342

Amount of C fixed in photosynthesis vs starch accumulation in WT white light

white_wt_rubisco = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RIBULOSE_BISPHOSPHATE_CARBOXYLASE_RXN_p_gc", 2), :].loc['white'].loc['constrained'].loc['wt']
white_ko_rubisco = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RIBULOSE_BISPHOSPHATE_CARBOXYLASE_RXN_p_gc", 2), :].loc['white'].loc['constrained'].loc['starchko']
white_wt_rubisco/(white_constrained_wt_osmolytes.loc[6.0].loc['STARCH']*6)
0.0334281119955182

Compare K and Cl import into vacuole in white light WT vs starch KO

white_wt_k_cv = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("K_PROTON_cv_gc", 2), :].loc['white'].loc['constrained'].loc['wt']
white_ko_k_cv = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("K_PROTON_cv_gc", 2), :].loc['white'].loc['constrained'].loc['starchko']
white_wt_cl_cv = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("Cl_cv_gc", 2), :].loc['white'].loc['constrained'].loc['wt']
white_ko_cl_cv = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("Cl_cv_gc", 2), :].loc['white'].loc['constrained'].loc['starchko']
(white_ko_k_cv+white_ko_cl_cv)/(white_wt_k_cv+white_wt_cl_cv) - 1
-0.5473616726045913

Compare vacuolar sucrose degradation in white light WT vs starch KO

(white_constrained_starchko_osmolytes.loc[6.0].loc['SUCROSE']-white_constrained_starchko_osmolytes.loc[6.5].loc['SUCROSE'])/(white_constrained_wt_osmolytes.loc[6.0].loc['SUCROSE']-white_constrained_wt_osmolytes.loc[6.5].loc['SUCROSE'])
4.985920298831731

Compare C fixation in white light WT vs starch KO

white_ko_rubisco/white_wt_rubisco - 1
0.2618307571821368

Source of glucose in No PS WT

nops_wt_glc_c = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("GLC_c_gc_Linker", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_glc_v = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("GLC_v_gc_Linker", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_glc_c/(nops_wt_glc_c+nops_wt_glc_v)
0.7721610472841978
nops_wt_suc_c = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("SUCROSE_c_gc_Linker", 1), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_suc_v = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("SUCROSE_v_gc_Linker", 1), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_starch_c = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("STARCH_p_gc_Linker", 1), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_starch_c / nops_wt_glc_c
0.5074317369423402
nops_wt_glc_from_suc = nops_wt_glc_c + nops_wt_glc_v - nops_wt_starch_c
1 - nops_wt_glc_v / nops_wt_glc_from_suc
0.6253763916642779
nops_wt_inv_c = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1461_c_gc", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_inv_v = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1461_v_gc", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_inv_c / (nops_wt_inv_c + nops_wt_inv_v)
0.6392239144307732

Maltose brakdown in No PS WT

nops_wt_starch_deg = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1827_p_gc", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_maltose_deg = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("MALTODEG_RXN_c_gc", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_maltose_deg / nops_wt_starch_deg
0.11915483203166444

Proportional of K+ imported in No PS starch KO remains in cytosol during Opening

nops_ko_k_v_2 = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("K_v_gc_Linker", 2), :].loc['nops'].loc['constrained'].loc['starchko']
nops_ko_k_c_2 = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("K_c_gc_Linker", 2), :].loc['nops'].loc['constrained'].loc['starchko']
nops_ko_k_v_1 = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("K_v_gc_Linker", 1), :].loc['nops'].loc['constrained'].loc['starchko']
nops_ko_k_c_1 = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("K_c_gc_Linker", 1), :].loc['nops'].loc['constrained'].loc['starchko']
kv = (nops_ko_k_v_2 - nops_ko_k_v_1)
kc = (nops_ko_k_c_2 - nops_ko_k_c_1)
kc / (kc+kv)
0.7078404702955794

Invertase in starch KO vs WT in No PS

nops_wt_inv2_v = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1781_v_gc", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_inv_v = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1461_v_gc", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_ko_inv2_v = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1781_v_gc", 2), :].loc['nops'].loc['constrained'].loc['starchko']
nops_ko_inv_v = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1461_v_gc", 2), :].loc['nops'].loc['constrained'].loc['starchko']
(nops_ko_inv_v + nops_ko_inv2_v) / (nops_wt_inv_v + nops_wt_inv2_v)
3.2287468012696294
#nops_wt_inv2_c = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1781_c_gc", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_wt_inv_c = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1461_c_gc", 2), :].loc['nops'].loc['constrained'].loc['wt']
#nops_ko_inv2_c = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1781_c_gc", 2), :].loc['nops'].loc['constrained'].loc['starchko']
nops_ko_inv_c = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1461_c_gc", 2), :].loc['nops'].loc['constrained'].loc['starchko']
#(nops_ko_inv_c + nops_ko_inv2_c) / (nops_wt_inv_c + nops_wt_inv2_c)
nops_ko_inv_c/nops_wt_inv_c
0.770532431827499
nops_wt_hatp_v = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PROTONATP_rev_vc_gc", 2), :].loc['nops'].loc['constrained'].loc['wt']
nops_ko_hatp_v = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PROTONATP_rev_vc_gc", 2), :].loc['nops'].loc['constrained'].loc['starchko']
(nops_ko_hatp_v - nops_wt_hatp_v) / nops_wt_hatp_v
-0.25981376726817995
blue_constrained_starchko_osmolytes.loc[[6, 6.5]]
Reaction CIT Cl FRU GLC K MAL STARCH SUCROSE
Phase
6.0 1.849573 1.121672 0.000000 0.000000 6.670390 6.165242 0.0 31.139004
6.5 0.000000 18.117110 27.046449 27.046449 22.936568 6.961439 0.0 0.000000 
white_constrained_starchko_osmolytes.loc[[6, 6.5]]
Reaction CIT Cl FRU GLC K MAL STARCH SUCROSE
Phase
6.0 0.0 0.658664 0.000000 0.000000 0.658664 0.0 0.0 30.879805
6.5 0.0 17.714954 26.821316 26.821316 17.714954 0.0 0.0 0.225133 
met_df.loc[:, ("white", "constrained")].xs("fluxes", level="Solution", axis=1).loc[([6.0, 6.5], "STARCH"), :]
/tmp/ipykernel_5280/1419630875.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.
  met_df.loc[:, ("white", "constrained")].xs("fluxes", level="Solution", axis=1).loc[([6.0, 6.5], "STARCH"), :]
Starch wt starchko
Phase Reaction
6.0 STARCH 0.005417 0.0
6.5 STARCH 0.000000 0.0 
(0.005417 * 10**-3 / arabidopsis_supermodel.N_gcs) * 10**15
9.339655172413792
27.828905 * 2
55.65781
met_df.loc[:, ("blue", "constrained")].xs("fluxes", level="Solution", axis=1).loc[([6.0, 6.5], "STARCH"), :]
/tmp/ipykernel_5280/2596957767.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.
  met_df.loc[:, ("blue", "constrained")].xs("fluxes", level="Solution", axis=1).loc[([6.0, 6.5], "STARCH"), :]
Starch wt starchko
Phase Reaction
6.0 STARCH 0.005126 0.0
6.5 STARCH 0.000000 0.0 
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PROTON_ATPase_c_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.007930
                      starchko    0.008132
       constrained    wt          0.004350
                      starchko    0.004350
white  unconstrained  wt          0.008068
                      starchko    0.008070
       constrained    wt          0.004350
                      starchko    0.004350
nops   unconstrained  wt          0.008348
                      starchko    0.008348
       constrained    wt          0.004350
                      starchko    0.004350
Name: (PROTON_ATPase_c_gc, 2), dtype: float64
pm_proton_pump = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PROTONATP_rev_vc_gc", 2), :]
for value in pm_proton_pump.values:
    print("{0:.10f}".format(value))
0.0041802388
0.0036862060
0.0043404882
0.0019368152
0.0032286510
0.0032122774
0.0053927508
0.0018125633
0.0052209173
0.0052208545
0.0058439202
0.0043255893
tonoplast_proton_pump = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PROTON_PPi_rev_vc_gc", 2), :]
tonoplast_proton_pump
Light  ATPase         Starch  
blue   unconstrained  wt          0.000357
                      starchko    0.000588
       constrained    wt          0.000000
                      starchko    0.000000
white  unconstrained  wt          0.001561
                      starchko    0.001568
       constrained    wt          0.000000
                      starchko    0.001257
nops   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000000
                      starchko    0.000000
Name: (PROTON_PPi_rev_vc_gc, 2), dtype: float64
(pm_proton_pump + tonoplast_proton_pump).plot.bar()

solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PHOSGLYPHOS_RXN_c_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.000476
                      starchko    0.000784
       constrained    wt          0.000000
                      starchko    0.000000
white  unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000000
                      starchko    0.000000
nops   unconstrained  wt         -0.000262
                      starchko   -0.000262
       constrained    wt         -0.000612
                      starchko    0.000000
Name: (PHOSGLYPHOS_RXN_c_gc, 2), dtype: float64
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("2PGADEHYDRAT_RXN_c_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt         -0.000476
                      starchko   -0.000784
       constrained    wt          0.000000
                      starchko    0.000000
white  unconstrained  wt         -0.000499
                      starchko   -0.001138
       constrained    wt          0.000000
                      starchko    0.000000
nops   unconstrained  wt          0.000262
                      starchko    0.000262
       constrained    wt          0.000612
                      starchko    0.000000
Name: (2PGADEHYDRAT_RXN_c_gc, 2), dtype: float64

MALATE_DEH_RXN_c_gc## Fraction is ATPase vs PPi for tonoplast

pm_proton_pump / (pm_proton_pump + tonoplast_proton_pump) * 100
Light  ATPase         Starch  
blue   unconstrained  wt           92.128349
                      starchko     86.237209
       constrained    wt          100.000000
                      starchko    100.000000
white  unconstrained  wt           67.407580
                      starchko     67.194792
       constrained    wt          100.000000
                      starchko     59.053306
nops   unconstrained  wt          100.000000
                      starchko    100.000000
       constrained    wt          100.000000
                      starchko    100.000000
dtype: float64
plastidatp_reactions = [
    reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.ATP_p_gc_2.reactions
]
plastidatp_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(plastidatp_reactions, 2), :]
plastidatp_df = plastidatp_df.loc[~(abs(plastidatp_df) < 0.0000000001).all(axis=1)]
plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
    [("white", "unconstrained"), ("nops", "unconstrained")], axis=1
)
/tmp/ipykernel_5280/2746888614.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
Plastidial_ATP_Synthase_p_gc 2 0.0 0.0 0.0 0.0 0.001968 0.001968 0.0 0.0
PHOSGLYPHOS_RXN_p_gc 2 0.0 0.0 0.0 0.0 0.002173 0.002742 0.0 0.0
PHOSPHORIBULOKINASE_RXN_p_gc 2 0.0 0.0 0.0 0.0 0.001087 0.001371 0.0 0.0
ATP_ADP_Pi_pc_gc 2 0.0 0.0 0.0 0.0 0.002646 0.001792 0.0 0.0 
plastidatp_reactions = [
    reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.ATP_p_gc_2.reactions
]
plastidatp_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(plastidatp_reactions, 3), :]
plastidatp_df = plastidatp_df.loc[~(abs(plastidatp_df) < 0.0000000001).all(axis=1)]
plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
    [("white", "unconstrained"), ("nops", "unconstrained")], axis=1
)
/tmp/ipykernel_5280/3411159466.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
GLUC1PADENYLTRANS_RXN_p_gc 3 0.000049 0.000000 0.000446 0.000000 0.000471 0.000000 0.000213 0.0
PEPDEPHOS_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000077 0.0
Plastidial_ATP_Synthase_p_gc 3 0.001968 0.001968 0.001968 0.001968 0.001968 0.001968 0.000000 0.0
PHOSGLYPHOS_RXN_p_gc 3 0.004054 0.004049 0.003958 0.003864 0.003975 0.003881 -0.000077 0.0
PHOSPHORIBULOKINASE_RXN_p_gc 3 0.002054 0.002059 0.001991 0.001941 0.001988 0.001940 0.000000 0.0
ATP_ADP_Pi_pc_gc 3 -0.000251 -0.000202 -0.000489 0.000101 -0.000528 0.000084 -0.000059 0.0 
plastidatp_reactions = [
    reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.ATP_c_gc_3.reactions
]
plastidatp_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(plastidatp_reactions, 3), :]
plastidatp_df = plastidatp_df.loc[~(abs(plastidatp_df) < 0.0000000001).all(axis=1)]
plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
    [("white", "unconstrained"), ("nops", "unconstrained")], axis=1
)
/tmp/ipykernel_5280/3582217826.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
ATP_ADP_Pi_pc_gc 3 -0.000251 -0.000202 -0.000489 0.000101 -0.000528 0.000084 -0.000059 0.000000
ATPase_tx_gc 3 0.000534 0.000534 0.000534 0.000534 0.000534 0.000534 0.000444 0.000444
ATP_ADP_mc_gc 3 0.000918 0.000891 0.001300 0.001601 0.001320 0.001603 0.001150 0.001343
PYRUVATEORTHOPHOSPHATE_DIKINASE_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
PROTONATP_rev_vc_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
6PFRUCTPHOS_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
ATP_AMP_mc_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
PEPDEPHOS_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000109
PROTON_ATPase_c_gc 3 0.000000 0.000000 0.000151 0.000154 0.000118 0.000142 0.000291 0.000310
UDPKIN_RXN_c_gc 3 0.000133 0.000154 0.000000 0.000443 0.000000 0.000445 0.000033 0.000249
PHOSGLYPHOS_RXN_c_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -0.000109
FRUCTOKINASE_RXN_c_gc 3 0.000000 0.000000 0.000126 0.000572 0.000140 0.000567 0.000324 0.000559 
plastidatp_reactions = [
    reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.NADPH_p_gc_2.reactions
]
plastidatp_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(plastidatp_reactions, 3), :]
plastidatp_df = plastidatp_df.loc[~(abs(plastidatp_df) < 0.0000000001).all(axis=1)]
plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
    [("white", "unconstrained"), ("nops", "unconstrained")], axis=1
)
/tmp/ipykernel_5280/2111125671.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
GLU6PDEHYDROG_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000012 0.000014
6PGLUCONDEHYDROG_RXN_p_gc 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000012 0.000014
1_PERIOD_18_PERIOD_1_PERIOD_2_RXN_p_gc 3 0.004593 0.004593 0.004593 0.004593 0.004593 0.004593 0.000000 0.000000
MALATE_DEHYDROGENASE_NADPs_RXN_p_gc 3 0.000361 0.000366 0.000457 0.000551 0.000440 0.000534 0.000000 0.000000
NADPHox_p_tx_gc 3 0.000178 0.000178 0.000178 0.000178 0.000178 0.000178 0.000024 0.000029
1_PERIOD_2_PERIOD_1_PERIOD_13_RXN_p_gc 3 0.004054 0.004049 0.003958 0.003864 0.003975 0.003881 0.000000 0.000000 
solutions_df
Light blue ... nops
ATPase unconstrained constrained ... unconstrained constrained
Starch wt starchko wt starchko ... wt starchko wt starchko
Solution fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum fluxes ... maximum fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum
Reaction Phase
EX_X_pi_t_me 1 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
2 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
3 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
4 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
EX_X_pi_t_gc 1 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Cl_total_pseudolinker 4 0.003642 NaN NaN 0.004839 NaN NaN 0.002951 NaN NaN 0.000158 ... NaN 0.000654 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
CIT_total_pseudolinker 1 0.000559 0.0 0.010896 0.000000 0.0 0.010896 0.000544 0.0 0.010896 0.000408 ... 0.010896 0.000534 0.0 0.010896 0.000612 0.0 0.010896 0.000543 0.0 0.010896
2 0.000000 0.0 0.011386 0.000000 0.0 0.011386 0.000000 0.0 0.011386 0.000000 ... 0.011386 0.000000 0.0 0.011386 0.000000 0.0 0.011386 0.000000 0.0 0.009964
3 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000534 NaN NaN 0.000612 NaN NaN 0.000543 NaN NaN
4 0.000315 NaN NaN 0.000000 NaN NaN 0.000343 NaN NaN 0.000343 ... NaN 0.000534 NaN NaN 0.000612 NaN NaN 0.000543 NaN NaN

7101 rows × 36 columns

plastidatp_reactions = [
    reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.NADH_c_gc_2.reactions
]
plastidatp_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(plastidatp_reactions, 2), :]
plastidatp_df = plastidatp_df.loc[~(abs(plastidatp_df) < 0.0000000001).all(axis=1)]
plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
    [("white", "unconstrained"), ("nops", "unconstrained")], axis=1
)
/tmp/ipykernel_5280/1101100446.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  plastidatp_df.mask(abs(plastidatp_df) < 0.0000000001, 0).drop(
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
MALATE_DEH_RXN_c_gc 2 0.000476 0.000784 0.0 0.0 0.0 0.0 -0.000612 0.0
GAPOXNPHOSPHN_RXN_c_gc 2 -0.000476 -0.000784 0.0 0.0 0.0 0.0 0.000612 0.0 
0.003478 - 0.002659
0.0008190000000000003
0.020457 + 0.010742
0.031198999999999998
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PROTON_ATPase_c_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.007930
                      starchko    0.008132
       constrained    wt          0.004350
                      starchko    0.004350
white  unconstrained  wt          0.008068
                      starchko    0.008070
       constrained    wt          0.004350
                      starchko    0.004350
nops   unconstrained  wt          0.008348
                      starchko    0.008348
       constrained    wt          0.004350
                      starchko    0.004350
Name: (PROTON_ATPase_c_gc, 2), dtype: float64
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("MALATE_DEHYDROGENASE_NADPs_RXN_p_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000000
                      starchko    0.000000
white  unconstrained  wt          0.002010
                      starchko    0.002001
       constrained    wt          0.002242
                      starchko    0.001673
nops   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000000
                      starchko    0.000000
Name: (MALATE_DEHYDROGENASE_NADPs_RXN_p_gc, 2), dtype: float64
fig, ax = plt.subplots()

tonoplast_proton_transport_df = (
    solutions_df.xs("fluxes", level="Solution", axis=1)
    .loc[(["PROTON_PPi_rev_vc_gc", "PROTONATP_rev_vc_gc"], 2), :]
    .drop([("white", "unconstrained"), ("nops", "unconstrained")], axis=1)
)
tonoplast_proton_transport_df.index = ["PPi Pump", "ATPase Pump"]
tonoplast_proton_transport_df.T.plot(kind="bar", stacked=True, ax=ax)

ax.set_ylabel("Flux (mmoles$\cdot$m$^{-2}$$\cdot$h$^{-1}$)")
/tmp/ipykernel_5280/377450841.py:4: PerformanceWarning: indexing past lexsort depth may impact performance.
  solutions_df.xs("fluxes", level="Solution", axis=1)
Text(0, 0.5, 'Flux (mmoles$\\cdot$m$^{-2}$$\\cdot$h$^{-1}$)')

tonoplast_proton_transport_df
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
PPi Pump 0.000357 0.000588 0.00000 0.000000 0.000000 0.001257 0.000000 0.000000
ATPase Pump 0.004180 0.003686 0.00434 0.001937 0.005393 0.001813 0.005844 0.004326 
solutions_df.xs("fluxes", level="Solution", axis=1).loc[(["PROTON_PPi_rev_vc_gc", "PROTONATP_rev_vc_gc"], 2), :].drop(
    [("white", "unconstrained"), ("nops", "unconstrained")], axis=1
).sum().plot.bar()
/tmp/ipykernel_5280/1652154496.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.
  solutions_df.xs("fluxes", level="Solution", axis=1).loc[(["PROTON_PPi_rev_vc_gc", "PROTONATP_rev_vc_gc"], 2), :].drop(

ppi_reactions = [reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.PPI_c_gc_2.reactions]
ppi_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(ppi_reactions, 2), :]
ppi_df = ppi_df.loc[~(abs(ppi_df) < 0.0000000001).all(axis=1)]
ppi_df.mask(abs(ppi_df) < 0.0000000001, 0).drop([("white", "unconstrained"), ("nops", "unconstrained")], axis=1)
/tmp/ipykernel_5280/4224157383.py:4: PerformanceWarning: indexing past lexsort depth may impact performance.
  ppi_df.mask(abs(ppi_df) < 0.0000000001, 0).drop([("white", "unconstrained"), ("nops", "unconstrained")], axis=1)
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
PROTON_PPi_rev_vc_gc 2 0.000357 0.000588 0.0 0.0 0.000000 0.001257 0.0 0.0
PYRUVATEORTHOPHOSPHATE_DIKINASE_RXN_c_gc 2 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.0 0.0
GLUC1PURIDYLTRANS_RXN_c_gc 2 0.000119 0.000196 0.0 0.0 -0.000905 0.000114 0.0 0.0
2_PERIOD_7_PERIOD_1_PERIOD_90_RXN_c_gc 2 -0.000238 -0.000392 0.0 0.0 -0.000905 -0.001143 0.0 0.0 
ppi_reactions = [reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.PPI_p_gc_2.reactions]
ppi_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(ppi_reactions, 2), :]
ppi_df = ppi_df.loc[~(abs(ppi_df) < 0.0000000001).all(axis=1)]
ppi_df.mask(abs(ppi_df) < 0.0000000001, 0).drop([("white", "unconstrained"), ("nops", "unconstrained")], axis=1)
/tmp/ipykernel_5280/1298146970.py:4: PerformanceWarning: indexing past lexsort depth may impact performance.
  ppi_df.mask(abs(ppi_df) < 0.0000000001, 0).drop([("white", "unconstrained"), ("nops", "unconstrained")], axis=1)
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase 
boundary_reactions = [
    reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.reactions if "_tx" in reaction.id
]
nitrate_uptake = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("NITRATE_a_tx", slice(None)), :]
nitrate_uptake
Light blue white nops
ATPase unconstrained constrained unconstrained constrained unconstrained constrained
Starch wt starchko wt starchko wt starchko wt starchko wt starchko wt starchko
Reaction Phase
NITRATE_a_tx 1 0.017802 0.017802 0.017802 0.017802 0.018684 0.018684 0.018684 0.018684 0.018683 0.018683 0.018683 0.018683
2 0.053405 0.053405 0.053405 0.053405 0.236743 0.236743 0.236743 0.236742 0.236733 0.236733 0.236732 0.236732
3 0.288754 0.288754 0.288754 0.288754 0.295351 0.295351 0.295350 0.295350 0.295338 0.295338 0.295337 0.295337
4 0.012081 0.012081 0.012081 0.012081 0.012419 0.012419 0.012419 0.012419 0.012418 0.012418 0.012418 0.012418 
from mmon_gcm.analysing import get_phase_lengths
phase_lengths = get_phase_lengths(arabidopsis_supermodel.fba_model)
phase_lengths
[6.0, 0.5, 11.5, 6.0]
for i, phase_length in zip([1, 2, 3, 4], phase_lengths):
    nitrate_uptake.loc[("NITRATE_a_tx", i), :] = nitrate_uptake.loc[("NITRATE_a_tx", i), :] * phase_length
nitrate_uptake
Light blue white nops
ATPase unconstrained constrained unconstrained constrained unconstrained constrained
Starch wt starchko wt starchko wt starchko wt starchko wt starchko wt starchko
Reaction Phase
NITRATE_a_tx 1 0.106810 0.106810 0.106810 0.106810 0.112106 0.112106 0.112106 0.112106 0.112101 0.112101 0.112101 0.112101
2 0.026703 0.026703 0.026703 0.026703 0.118371 0.118371 0.118371 0.118371 0.118366 0.118366 0.118366 0.118366
3 3.320676 3.320676 3.320673 3.320670 3.396533 3.396533 3.396529 3.396526 3.396386 3.396386 3.396377 3.396376
4 0.072484 0.072484 0.072484 0.072484 0.074511 0.074511 0.074511 0.074511 0.074508 0.074508 0.074508 0.074508 
nitrate_uptake_day = nitrate_uptake.loc[(slice(None), [2, 3]), :].sum()
nitrate_uptake_night = nitrate_uptake.loc[(slice(None), [1, 4]), :].sum()
nitrate_uptake_day
Light  ATPase         Starch  
blue   unconstrained  wt          3.347379
                      starchko    3.347379
       constrained    wt          3.347375
                      starchko    3.347373
white  unconstrained  wt          3.514904
                      starchko    3.514904
       constrained    wt          3.514900
                      starchko    3.514897
nops   unconstrained  wt          3.514752
                      starchko    3.514752
       constrained    wt          3.514743
                      starchko    3.514742
dtype: float64
nitrate_uptake_night
Light  ATPase         Starch  
blue   unconstrained  wt          0.179294
                      starchko    0.179294
       constrained    wt          0.179294
                      starchko    0.179294
white  unconstrained  wt          0.186617
                      starchko    0.186617
       constrained    wt          0.186617
                      starchko    0.186617
nops   unconstrained  wt          0.186609
                      starchko    0.186609
       constrained    wt          0.186609
                      starchko    0.186608
dtype: float64
nitrate_uptake_day / nitrate_uptake_night
Light  ATPase         Starch  
blue   unconstrained  wt          18.669741
                      starchko    18.669741
       constrained    wt          18.669741
                      starchko    18.669741
white  unconstrained  wt          18.834845
                      starchko    18.834845
       constrained    wt          18.834845
                      starchko    18.834845
nops   unconstrained  wt          18.834845
                      starchko    18.834845
       constrained    wt          18.834845
                      starchko    18.834845
dtype: float64
nitrate_uptake = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("NITRATE_a_Linker", slice(None)), :]
nitrate_uptake
Light blue white nops
ATPase unconstrained constrained unconstrained constrained unconstrained constrained
Starch wt starchko wt starchko wt starchko wt starchko wt starchko wt starchko
Reaction Phase
NITRATE_a_Linker 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
nitrate_uptake = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("NITRATE_ae_me", slice(None)), :]
nitrate_uptake
Light blue white nops
ATPase unconstrained constrained unconstrained constrained unconstrained constrained
Starch wt starchko wt starchko wt starchko wt starchko wt starchko wt starchko
Reaction Phase
NITRATE_ae_me 1 0.017802 0.017802 0.017802 0.017802 0.018684 0.018684 0.018684 0.018684 0.018683 0.018683 0.018683 0.018683
2 0.053405 0.053405 0.053405 0.053405 0.236743 0.236743 0.236743 0.236742 0.236733 0.236733 0.236732 0.236732
3 0.288754 0.288754 0.288754 0.288754 0.295351 0.295351 0.295350 0.295350 0.295338 0.295338 0.295337 0.295337
4 0.012081 0.012081 0.012081 0.012081 0.012419 0.012419 0.012419 0.012419 0.012418 0.012418 0.012418 0.012418 
arabidopsis_supermodel.fba_model.metabolites.NITRATE_e_gc_2
Metabolite identifier NITRATE_e_gc_2
Name NITRATE_gc_2
Memory address 0x7562383d04c0
Formula N1O3
Compartment e
In 2 reaction(s) NITRATE_ae_gc_2, Nitrate_ec_gc_2 
filtered_reactions = [
    reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.NADH_c_gc_2.reactions
]
filtered_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(filtered_reactions, 2), :]
filtered_df = filtered_df.loc[~(abs(filtered_df) < 0.0000000001).all(axis=1)]
filtered_df.mask(abs(filtered_df) < 0.0000000001, 0).drop(
    [("white", "unconstrained"), ("nops", "unconstrained")], axis=1
)
/tmp/ipykernel_5280/2808846299.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  filtered_df.mask(abs(filtered_df) < 0.0000000001, 0).drop(
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
MALATE_DEH_RXN_c_gc 2 0.000476 0.000784 0.0 0.0 0.0 0.0 -0.000612 0.0
GAPOXNPHOSPHN_RXN_c_gc 2 -0.000476 -0.000784 0.0 0.0 0.0 0.0 0.000612 0.0 
filtered_reactions = [
    reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.ATP_c_gc_2.reactions
]
filtered_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(filtered_reactions, 2), :]
filtered_df = filtered_df.loc[~(abs(filtered_df) < 0.0000000001).all(axis=1)]
filtered_df.mask(abs(filtered_df) < 0.0000000001, 0).drop(
    [("white", "unconstrained"), ("nops", "unconstrained")], axis=1
)
/tmp/ipykernel_5280/3469620016.py:6: PerformanceWarning: indexing past lexsort depth may impact performance.
  filtered_df.mask(abs(filtered_df) < 0.0000000001, 0).drop(
Light blue white nops
ATPase unconstrained constrained constrained constrained
Starch wt starchko wt starchko wt starchko wt starchko
Reaction Phase
ATPase_tx_gc 2 0.000444 0.000444 0.000444 0.000444 0.000534 0.000534 0.000444 0.000444
ATP_ADP_mc_gc 2 0.013626 0.014027 0.009134 0.006731 0.006726 0.005019 0.010332 0.009119
PYRUVATEORTHOPHOSPHATE_DIKINASE_RXN_c_gc 2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
6PFRUCTPHOS_RXN_c_gc 2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000306 0.000000
ATP_AMP_mc_gc 2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
PEPCARBOXYKIN_RXN_c_gc 2 0.000566 0.000784 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
PROTONATP_rev_vc_gc 2 0.004180 0.003686 0.004340 0.001937 0.005393 0.001813 0.005844 0.004326
PEPDEPHOS_RXN_c_gc 2 0.000090 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
PROTON_ATPase_c_gc 2 0.007930 0.008132 0.004350 0.004350 0.004350 0.004350 0.004350 0.004350
UDPKIN_RXN_c_gc 2 0.000119 0.000196 0.000000 0.000000 -0.000905 0.000114 0.000000 0.000000
PHOSGLYPHOS_RXN_c_gc 2 0.000476 0.000784 0.000000 0.000000 0.000000 0.000000 -0.000612 0.000000
ATP_ADP_Pi_pc_gc 2 0.000000 0.000000 0.000000 0.000000 0.002646 0.001792 0.000000 0.000000 
filtered_reactions = [
    reaction.id[:-2] for reaction in arabidopsis_supermodel.fba_model.metabolites.ATP_c_gc_2.reactions
]
filtered_df = solutions_df.xs("fluxes", level="Solution", axis=1).loc[(filtered_reactions, 2), :]
filtered_df = filtered_df.loc[~(abs(filtered_df) < 0.0000000001).all(axis=1)]
filtered_df.mask(abs(filtered_df) < 0.0000000001, 0).loc[:, ("white", "constrained", "wt")]
Reaction                                  Phase
ATPase_tx_gc                              2        0.000534
ATP_ADP_mc_gc                             2        0.006726
PYRUVATEORTHOPHOSPHATE_DIKINASE_RXN_c_gc  2        0.000000
6PFRUCTPHOS_RXN_c_gc                      2        0.000000
ATP_AMP_mc_gc                             2        0.000000
PEPCARBOXYKIN_RXN_c_gc                    2        0.000000
PROTONATP_rev_vc_gc                       2        0.005393
PEPDEPHOS_RXN_c_gc                        2        0.000000
PROTON_ATPase_c_gc                        2        0.004350
UDPKIN_RXN_c_gc                           2       -0.000905
PHOSGLYPHOS_RXN_c_gc                      2        0.000000
ATP_ADP_Pi_pc_gc                          2        0.002646
Name: (white, constrained, wt), dtype: float64
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PEPCARBOXYKIN_RXN_c_gc", 2), :].drop(
    [("white", "unconstrained"), ("nops", "unconstrained")]
)
/tmp/ipykernel_5280/42660494.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.
  solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PEPCARBOXYKIN_RXN_c_gc", 2), :].drop(
Light  ATPase         Starch  
blue   unconstrained  wt          0.000566
                      starchko    0.000784
       constrained    wt          0.000000
                      starchko    0.000000
white  constrained    wt          0.000000
                      starchko    0.000000
nops   constrained    wt          0.000000
                      starchko    0.000000
Name: (PEPCARBOXYKIN_RXN_c_gc, 2), dtype: float64
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PEPCARBOXYKIN_RXN_c_gc", 2), :].drop(
    [("white", "unconstrained"), ("nops", "unconstrained")]
)
/tmp/ipykernel_5280/42660494.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.
  solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PEPCARBOXYKIN_RXN_c_gc", 2), :].drop(
Light  ATPase         Starch  
blue   unconstrained  wt          0.000566
                      starchko    0.000784
       constrained    wt          0.000000
                      starchko    0.000000
white  constrained    wt          0.000000
                      starchko    0.000000
nops   constrained    wt          0.000000
                      starchko    0.000000
Name: (PEPCARBOXYKIN_RXN_c_gc, 2), dtype: float64
phloem_outputs = solutions_df.xs("fluxes", level="Solution", axis=1).loc[("Phloem_tx_overall", np.nan), :]
phloem_outputs
Light  ATPase         Starch  
blue   unconstrained  wt          14.258841
                      starchko    14.258841
       constrained    wt          14.258825
                      starchko    14.258816
white  unconstrained  wt          14.965776
                      starchko    14.965776
       constrained    wt          14.965758
                      starchko    14.965748
nops   unconstrained  wt          14.965131
                      starchko    14.965131
       constrained    wt          14.965090
                      starchko    14.965084
Name: (Phloem_tx_overall, nan), dtype: float64
phloem_outputs_dict = {}
for name, value in phloem_outputs.items():
    phloem_outputs_dict[name] = (phloem_outputs / value * 100).round(3)
phloem_outputs_comparison_table = pd.DataFrame.from_dict(phloem_outputs_dict)
phloem_outputs_comparison_table.to_csv("../outputs/efficiency_comparisons/phloem_outputs_compare.csv")
phloem_outputs_comparison_table
blue white nops
unconstrained constrained unconstrained constrained unconstrained constrained
wt starchko wt starchko wt starchko wt starchko wt starchko wt starchko
Light ATPase Starch
blue unconstrained wt 100.000 100.000 100.000 100.000 95.276 95.276 95.276 95.277 95.280 95.280 95.281 95.281
starchko 100.000 100.000 100.000 100.000 95.276 95.276 95.276 95.276 95.280 95.280 95.281 95.281
constrained wt 100.000 100.000 100.000 100.000 95.276 95.276 95.276 95.276 95.280 95.280 95.281 95.281
starchko 100.000 100.000 100.000 100.000 95.276 95.276 95.276 95.276 95.280 95.280 95.281 95.281
white unconstrained wt 104.958 104.958 104.958 104.958 100.000 100.000 100.000 100.000 100.004 100.004 100.005 100.005
starchko 104.958 104.958 104.958 104.958 100.000 100.000 100.000 100.000 100.004 100.004 100.005 100.005
constrained wt 104.958 104.958 104.958 104.958 100.000 100.000 100.000 100.000 100.004 100.004 100.004 100.005
starchko 104.958 104.958 104.958 104.958 100.000 100.000 100.000 100.000 100.004 100.004 100.004 100.004
nops unconstrained wt 104.953 104.953 104.953 104.954 99.996 99.996 99.996 99.996 100.000 100.000 100.000 100.000
starchko 104.953 104.953 104.953 104.954 99.996 99.996 99.996 99.996 100.000 100.000 100.000 100.000
constrained wt 104.953 104.953 104.953 104.953 99.995 99.995 99.996 99.996 100.000 100.000 100.000 100.000
starchko 104.953 104.953 104.953 104.953 99.995 99.995 99.995 99.996 100.000 100.000 100.000 100.000 
100 - 99.980
0.01999999999999602
phloem_outputs_blue = phloem_outputs.loc["blue"]
phloem_outputs_blue / phloem_outputs_blue.iloc[0] * 100
ATPase         Starch  
unconstrained  wt          100.000000
               starchko     99.999999
constrained    wt           99.999888
               starchko     99.999825
Name: (Phloem_tx_overall, nan), dtype: float64
phloem_outputs_white = phloem_outputs.drop("blue")
phloem_outputs_white = phloem_outputs_white / phloem_outputs_white.iloc[0] * 100
phloem_outputs_white
/tmp/ipykernel_5280/3118140255.py:1: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
  phloem_outputs_white = phloem_outputs.drop("blue")
Light  ATPase         Starch  
white  unconstrained  wt          100.000000
                      starchko    100.000000
       constrained    wt           99.999883
                      starchko     99.999814
nops   unconstrained  wt           99.995690
                      starchko     99.995690
       constrained    wt           99.995422
                      starchko     99.995379
Name: (Phloem_tx_overall, nan), dtype: float64
phloem_outputs.drop("blue")
/tmp/ipykernel_5280/4124221444.py:1: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
  phloem_outputs.drop("blue")
Light  ATPase         Starch  
white  unconstrained  wt          14.965776
                      starchko    14.965776
       constrained    wt          14.965758
                      starchko    14.965748
nops   unconstrained  wt          14.965131
                      starchko    14.965131
       constrained    wt          14.965090
                      starchko    14.965084
Name: (Phloem_tx_overall, nan), dtype: float64
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("Phloem_tx_overall", np.nan), :].plot.bar()

arabidopsis_supermodel.get_prop_gc()
0.0030263085626407423
5.17 * arabidopsis_supermodel.get_prop_gc() * 0.3
0.004693804580655791
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("Cl_cv_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.023144
                      starchko    0.026064
       constrained    wt          0.014849
                      starchko    0.008494
white  unconstrained  wt          0.025841
                      starchko    0.025940
       constrained    wt          0.010786
                      starchko    0.004882
nops   unconstrained  wt          0.016494
                      starchko    0.016494
       constrained    wt          0.008700
                      starchko    0.008700
Name: (Cl_cv_gc, 2), dtype: float64
0.004882/0.010786
0.45262377155572037
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("K_PROTON_cv_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.008718
                      starchko    0.007961
       constrained    wt          0.008681
                      starchko    0.003874
white  unconstrained  wt          0.008018
                      starchko    0.007993
       constrained    wt          0.010786
                      starchko    0.004882
nops   unconstrained  wt          0.010442
                      starchko    0.010442
       constrained    wt          0.008700
                      starchko    0.002542
Name: (K_PROTON_cv_gc, 2), dtype: float64
0.004882/0.010786
0.45262377155572037
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_1461_v_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.003474
                      starchko    0.013726
white  unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.001805
                      starchko    0.013612
nops   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.002988
                      starchko    0.009647
Name: (RXN_1461_v_gc, 2), dtype: float64
0.013612/0.001805
7.541274238227148
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("K_ec_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.015860
                      starchko    0.016264
       constrained    wt          0.008700
                      starchko    0.008700
white  unconstrained  wt          0.016135
                      starchko    0.016140
       constrained    wt          0.008700
                      starchko    0.008700
nops   unconstrained  wt          0.015185
                      starchko    0.015185
       constrained    wt          0.008700
                      starchko    0.008700
Name: (K_ec_gc, 2), dtype: float64
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("ISOCITDEH_RXN_c_gc", 3), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000000
                      starchko    0.000000
white  unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000000
                      starchko    0.000000
nops   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000051
                      starchko    0.000062
Name: (ISOCITDEH_RXN_c_gc, 3), dtype: float64
met_df_mm
Light blue ... nops
ATPase unconstrained constrained ... unconstrained constrained
Starch wt starchko wt starchko ... wt starchko wt starchko
Solution fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum fluxes ... maximum fluxes minimum maximum fluxes minimum maximum fluxes minimum maximum
Phase Reaction
0.0 K 24.135299 NaN NaN 21.955092 NaN NaN 20.798763 NaN NaN 6.267413 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
Cl 16.524279 NaN NaN 21.954453 NaN NaN 13.391074 NaN NaN 0.718695 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.098507 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 0.637511 NaN NaN 18.115847 NaN NaN 30.149261 NaN NaN
GLC 0.633834 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.458382 NaN NaN 0.659272 NaN NaN 0.658861 ... NaN 0.637511 NaN NaN 0.672046 NaN NaN 0.989743 NaN NaN
MAL 20.910624 NaN NaN 24.158592 NaN NaN 9.601548 NaN NaN 6.606525 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
CIT 1.431237 NaN NaN 0.000000 NaN NaN 1.555385 NaN NaN 1.555385 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
STARCH 0.000244 NaN NaN 0.000000 NaN NaN 0.005126 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.002569 NaN NaN 0.000000 NaN NaN
6.0 K 24.135299 0.0 140.262888 22.233537 0.0 140.262888 21.356860 0.0 140.262888 6.670390 ... 140.262888 10.238928 0.0 140.262888 8.333669 0.0 140.262888 7.396173 0.0 133.708937
Cl 16.524279 0.0 89.290671 22.232898 0.0 89.290671 13.949171 0.0 89.290671 1.121672 ... 89.290671 2.969559 0.0 89.290671 0.000000 0.0 89.290671 0.000000 0.0 80.403347
SUCROSE 0.000000 0.0 178.581341 0.000000 0.0 178.581341 7.881207 0.0 178.581341 31.139004 ... 178.581341 0.000000 0.0 178.581341 18.787893 0.0 178.581341 31.139004 0.0 178.581341
GLC 1.268503 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 ... 178.581341 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 160.500964
FRU 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 178.581341 0.000000 ... 178.581341 1.275022 0.0 178.581341 0.000000 0.0 178.581341 0.000000 0.0 160.500964
MAL 19.251970 0.0 64.494321 24.158592 0.0 64.494321 8.230780 0.0 64.494321 6.165242 ... 64.494321 8.077077 0.0 64.494321 0.000000 0.0 64.494321 8.217966 0.0 64.494321
CIT 2.537007 0.0 49.435142 0.000000 0.0 49.435142 2.469230 0.0 49.435142 1.849573 ... 49.435142 2.423123 0.0 49.435142 2.777890 0.0 49.435142 2.465388 0.0 49.435142
STARCH 0.000000 0.0 1000.000000 0.000000 0.0 0.000000 0.005126 0.0 0.285304 0.000000 ... 1000.000000 0.000000 0.0 0.000000 0.002569 0.0 0.232884 0.000000 0.0 0.000000
6.5 K 52.215252 0.0 152.876211 51.358083 0.0 152.876211 35.692816 0.0 138.971194 22.936568 ... 152.876211 38.815143 0.0 152.876211 24.381244 0.0 138.971194 23.566961 0.0 133.278620
Cl 45.604537 0.0 108.221407 51.357528 0.0 108.221407 29.258708 0.0 94.698182 18.117110 ... 108.221407 32.501177 0.0 108.221407 17.142857 0.0 94.698182 17.142857 0.0 86.978907
SUCROSE 0.234594 0.0 216.442814 0.386397 0.0 216.442814 0.000000 0.0 216.442814 0.000000 ... 216.442814 1.230198 0.0 216.442814 0.000000 0.0 216.442814 0.000000 0.0 138.990025
GLC 1.101786 0.0 216.442814 0.000000 0.0 216.442814 27.046452 0.0 216.442814 27.046449 ... 216.442814 0.000000 0.0 216.442814 25.840051 0.0 216.442814 27.046449 0.0 216.442814
FRU 0.000000 0.0 216.442814 0.000000 0.0 216.442814 6.845391 0.0 216.442814 27.046449 ... 216.442814 1.365608 0.0 216.442814 16.318627 0.0 216.442814 27.046449 0.0 216.442814
MAL 17.518812 0.0 65.116825 17.835708 0.0 65.116825 9.293723 0.0 65.116825 6.961439 ... 65.116825 9.120173 0.0 65.116825 3.619193 0.0 65.116825 9.279256 0.0 62.386555
CIT 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 ... 44.869080 0.000000 0.0 44.869080 0.000000 0.0 44.869080 0.000000 0.0 39.265593
STARCH 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 ... 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.099952 0.000000 0.0 0.000000
18.0 K 24.135299 NaN NaN 21.909632 NaN NaN 20.080068 NaN NaN 5.548718 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
Cl 16.524279 NaN NaN 21.908994 NaN NaN 12.672379 NaN NaN 0.000000 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.647915 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 1.275022 NaN NaN 16.881692 NaN NaN 29.159518 NaN NaN
GLC 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.000000 NaN NaN 1.318969 NaN NaN 1.318558 ... NaN 0.000000 NaN NaN 1.906201 NaN NaN 1.979486 NaN NaN
MAL 23.057480 NaN NaN 24.158273 NaN NaN 11.934624 NaN NaN 8.939601 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
CIT 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
STARCH 0.000559 NaN NaN 0.000000 NaN NaN 0.005126 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.002455 NaN NaN 0.000000 NaN NaN
24.0 K 24.135299 NaN NaN 21.955092 NaN NaN 20.798763 NaN NaN 6.267413 ... NaN 10.238928 NaN NaN 8.333669 NaN NaN 7.396173 NaN NaN
Cl 16.524279 NaN NaN 21.954453 NaN NaN 13.391074 NaN NaN 0.718695 ... NaN 2.969559 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
SUCROSE 0.000000 NaN NaN 0.098507 NaN NaN 7.881207 NaN NaN 31.139004 ... NaN 0.637511 NaN NaN 18.115847 NaN NaN 30.149261 NaN NaN
GLC 0.633834 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.000000 NaN NaN 0.000000 NaN NaN
FRU 0.000000 NaN NaN 0.458382 NaN NaN 0.659272 NaN NaN 0.658861 ... NaN 0.637511 NaN NaN 0.672046 NaN NaN 0.989743 NaN NaN
MAL 20.910624 NaN NaN 24.158592 NaN NaN 9.601548 NaN NaN 6.606525 ... NaN 8.077077 NaN NaN 0.000000 NaN NaN 8.217966 NaN NaN
CIT 1.431237 NaN NaN 0.000000 NaN NaN 1.555385 NaN NaN 1.555385 ... NaN 2.423123 NaN NaN 2.777890 NaN NaN 2.465388 NaN NaN
STARCH 0.000244 NaN NaN 0.000000 NaN NaN 0.005126 NaN NaN 0.000000 ... NaN 0.000000 NaN NaN 0.002569 NaN NaN 0.000000 NaN NaN

40 rows × 36 columns

solutions_df.xs("fluxes", level="Solution", axis=1).loc["Phloem_tx_overall"]
Light blue white nops
ATPase unconstrained constrained unconstrained constrained unconstrained constrained
Starch wt starchko wt starchko wt starchko wt starchko wt starchko wt starchko
Phase
NaN 14.258841 14.258841 14.258825 14.258816 14.965776 14.965776 14.965758 14.965748 14.965131 14.965131 14.96509 14.965084 
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("RXN_2141_p_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.005126
                      starchko    0.000000
white  unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.005417
                      starchko    0.000000
nops   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.002263
                      starchko    0.000000
Name: (RXN_2141_p_gc, 2), dtype: float64
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("SUCROSE_SYNTHASE_RXN_c_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000000
                      starchko    0.000000
white  unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.001992
                      starchko    0.000000
nops   unconstrained  wt          0.000131
                      starchko    0.000131
       constrained    wt          0.000000
                      starchko    0.000000
Name: (SUCROSE_SYNTHASE_RXN_c_gc, 2), dtype: float64
0.001992/0.005417
0.36773121654052054
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PEPCARBOX_RXN_c_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000000
                      starchko    0.000000
white  unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000000
                      starchko    0.000000
nops   unconstrained  wt          0.000000
                      starchko    0.000000
       constrained    wt          0.000612
                      starchko    0.000000
Name: (PEPCARBOX_RXN_c_gc, 2), dtype: float64
solutions_df.xs("fluxes", level="Solution", axis=1).loc[("PROTONATP_rev_vc_gc", 2), :]
Light  ATPase         Starch  
blue   unconstrained  wt          0.004180
                      starchko    0.003686
       constrained    wt          0.004340
                      starchko    0.001937
white  unconstrained  wt          0.003229
                      starchko    0.003212
       constrained    wt          0.005393
                      starchko    0.001813
nops   unconstrained  wt          0.005221
                      starchko    0.005221
       constrained    wt          0.005844
                      starchko    0.004326
Name: (PROTONATP_rev_vc_gc, 2), dtype: float64

Analysis on Guard Cell Photon Scan

gc_photon_scan = pd.read_csv("../outputs/scans/gc_photon_scan.csv")
gc_photon_scan.set_index("Unnamed: 0", inplace=True)
glycolysis_c = gc_photon_scan.loc['3PGAREARR_RXN_c_gc_2']
glycolysis_p = gc_photon_scan.loc['3PGAREARR_RXN_p_gc_2']
glycolysis = glycolysis_c + glycolysis_p
sum(abs(gc_photon_scan.loc['3PGAREARR_RXN_p_gc_2'])>1e-7)
0
sum(abs(gc_photon_scan.loc['3PGAREARR_RXN_p_me_2'])>1e-7)
151
sum(abs(gc_photon_scan.loc['3PGAREARR_RXN_c_me_2'])>1e-7)
0
glycolysis_nmoles_gc = glycolysis.apply(
    convert_fluxes_to_per_guard_cell, args=[arabidopsis_supermodel, units]
)
glycolysis_nmoles_gc
0      1.055598e+00
1      1.043212e+00
2      1.030826e+00
3      1.018441e+00
4      1.006055e+00
           ...     
146    0.000000e+00
147    2.046696e-14
148    9.912093e-15
149   -1.465877e-13
150    0.000000e+00
Length: 151, dtype: float64
(abs(glycolysis) < 1e-7).idxmax() # First PPFD with 0 glycolytic flux
'39'
parameters_df = pd.read_csv("../inputs/arabidopsis_parameters.csv", index_col=0)
pm = parameters_df.loc[:, "Value"]
V_l = pm.T_l * pm.A_l  # volume of leaf is area x thickness
V_l = V_l * 10**3  # (Total leaf volume) m3 -> dm3 = 10**3

V_gc = pm.V_gc_ind * pm.N_gcs  # total volume of gc in leaf

# volume of meosphyll is leaf that isn't epidermis or air
V_me = V_l * (1 - pm.L_epidermis) * (1 - pm.L_air)

v_prop_gc = V_gc / V_me  # volume of gc is negligable
me_photon = gc_photon_scan.loc['Photon_tx_me_2']
me_photon_per_vol = me_photon / V_me
gc_photon = gc_photon_scan.loc['Photon_tx_gc_2']
gc_photon_per_vol = gc_photon / V_gc
gc_me_photon_percent_per_vol = gc_photon_per_vol / me_photon_per_vol * 100
# GC photosynthesis as a % of MC photosynthesis per cell volume
gc_me_photon_percent_per_vol.iloc[int((abs(glycolysis) < 1e-7).idxmax())]
0.3247833564048949
plt.plot(gc_me_photon_percent_per_vol, glycolysis_nmoles_gc, color='b', linestyle='-')

# Add axis labels
plt.xlabel('Guard cell photosynthesis as a percentage of\nmesophyll photosynthesis per cell volume (%)')
plt.ylabel('Guard cell glycolytic flux\nduring opening (fmol$\cdot$GC$^{-1}\cdot$h$^{-1}$)')

# Set x and y axis limits to start from 0
plt.xlim(left=0)
plt.ylim(bottom=0)

plt.savefig(f"../plant_cell_paper/supplemental_figure_4.svg", bbox_inches="tight")
plt.savefig(f"../plant_cell_paper/supplemental_figure_4.png", dpi=300, bbox_inches="tight")

# Show the plot
plt.show()

Analysis on blue light photon

o_photon_scan = pd.read_csv("../outputs/scans/opening_photon_scan.csv")
o_photon_scan.set_index("Unnamed: 0", inplace=True)
gc_glycolysis_c = o_photon_scan.loc['3PGAREARR_RXN_c_gc_2']
me_glycolysis_c = o_photon_scan.loc['3PGAREARR_RXN_c_me_2']
gc_glycolysis_p = o_photon_scan.loc['3PGAREARR_RXN_p_gc_2']
me_glycolysis_p = o_photon_scan.loc['3PGAREARR_RXN_p_me_2']

gc_glycolysis = gc_glycolysis_c + gc_glycolysis_p
me_glycolysis = me_glycolysis_c + me_glycolysis_p

gc_glycolysis_rel = gc_glycolysis/gc_glycolysis.iloc[-1]
me_glycolysis_rel = me_glycolysis/me_glycolysis.iloc[-1]
print(sum(abs(o_photon_scan.loc['3PGAREARR_RXN_p_gc_2'])>1e-7))
print(sum(abs(o_photon_scan.loc['3PGAREARR_RXN_c_gc_2'])>1e-7))
print(sum(abs(o_photon_scan.loc['3PGAREARR_RXN_p_me_2'])>1e-7))
print(sum(abs(o_photon_scan.loc['3PGAREARR_RXN_c_me_2'])>1e-7))
0
0
129
23
gc_photon = o_photon_scan.loc['Photon_tx_gc_2']/o_photon_scan.loc['Photon_tx_gc_2'].iloc[-1]
me_photon = o_photon_scan.loc['Photon_tx_me_2']/o_photon_scan.loc['Photon_tx_me_2'].iloc[-1]
gc_glycolysis_nmoles_gc = gc_glycolysis.apply(
    convert_fluxes_to_per_guard_cell, args=[arabidopsis_supermodel, units]
)
plt.plot(me_photon, me_glycolysis_rel, color='b', linestyle='-')

# Add axis labels
plt.xlabel('Mesophyll cell photon influx in opening phase\nrelative to white light scenario')
plt.ylabel('Mesophyll glycolytic flux\nrelative to white light scenario')

# Set x and y axis limits to start from 0
plt.xlim(left=0)
plt.ylim(bottom=0)

#plt.savefig(f"../plant_cell_paper/supplemental_figure_4.svg", bbox_inches="tight")
#plt.savefig(f"../plant_cell_paper/supplemental_figure_4.png", dpi=300, bbox_inches="tight")

# Show the plot
plt.show()

plt.plot(gc_photon, gc_glycolysis_nmoles_gc, color='b', linestyle='-')

# Add axis labels
plt.xlabel('Guard cell photon influx during opening\nrelative to white light scenario')
plt.ylabel('Guard cell glycolytic flux\nduring opening (fmol$\cdot$GC$^{-1}\cdot$h$^{-1}$)')

# Set x and y axis limits to start from 0
plt.xlim(left=0)
plt.ylim(-0.1, 1)

plt.savefig(f"../plant_cell_paper/supplemental_figure_5.svg", bbox_inches="tight")
plt.savefig(f"../plant_cell_paper/supplemental_figure_5.png", dpi=300, bbox_inches="tight")

# Show the plot
plt.show()