Experiments

GlycogenExperiment

Model

• SBML: models/liver_glucose.xml
• HTML: models/liver_glucose.html

Datasets

• Magnusson1992.tsv
• Radziuk2001.tsv
• Rothman1991.tsv
• Taylor1996.tsv

Figures

• GlycogenExperiment_fig1.svg

fig1

Code

https://github.com/matthiaskoenig/exsimo/tree/master/pyexsimo/experiments/glycogen.py

from typing import Dict
from matplotlib.pyplot import Figure
import numpy as np

from sbmlsim.experiment import SimulationExperiment
from sbmlsim.data import DataSet
from sbmlsim.timecourse import Timecourse, TimecourseSim, TimecourseScan
from sbmlsim.plotting_matplotlib import add_data, add_line, plt


class GlycogenExperiment(SimulationExperiment):
    """Simulation of glycogenolysis and glycogen synthesis.

    Setting various glucose concentration and running
    timecourses of glycogen storage and glycogenolysis.
    """
    @property
    def datasets(self) -> Dict[str, DataSet]:
        dsets = {}
        for study_id in ['Magnusson1992', 'Rothman1991', 'Radziuk2001',
                         'Taylor1996']:
            dset_id = f"Glycogen_Tab{study_id}"
            df = self.load_data(dset_id)
            df = df[df.condition == "normal"]  # only healthy controls
            udict = {key: df[f"{key}_unit"].unique()[0] for key in
                     ["time", "gly"]}
            dsets[study_id] = DataSet.from_df(df, udict=udict, ureg=self.ureg)

        return dsets

    @property
    def scans(self) -> Dict[str, TimecourseScan]:
        """Scanning glycogen synthesis and glycogenolysis.

        Simulate glycogenolysis under various external glucose concentrations
        supporting glycogen degration.
        Simulate glycogen synthesis under various external glucose
        concentrations supporting glycogen storage.
        """
        Q_ = self.ureg.Quantity
        gly_scan = TimecourseScan(
            tcsim=TimecourseSim([
                Timecourse(start=0, end=65 * 60, steps=500, changes={
                    '[glyglc]': Q_(500, 'mM')
                })
            ]),
            scan={'[glc_ext]': Q_(np.linspace(3.6, 5.0, num=8), 'mM')},
        )
        gs_scan = TimecourseScan(
            tcsim=TimecourseSim([
                Timecourse(start=0, end=300, steps=500, changes={
                    '[glyglc]': Q_(200, 'mM')
                })
            ]),
            scan={'[glc_ext]': Q_(np.linspace(5.5, 8.0, num=6), 'mM')},
        )
        return {
            "gly_scan": gly_scan,
            "gs_scan": gs_scan,
        }

    @property
    def figures(self) -> Dict[str, Figure]:
        """Glycogenolysis and glycogen synthesis figure."""
        xunit_ax1 = "hr"
        xunit_ax2 = "min"
        yunit_gly = "mM"

        f, ((ax1, ax2)) = plt.subplots(2, 1, figsize=(5, 10))
        f.subplots_adjust(wspace=.3, hspace=.3)
        axes = (ax1, ax2)

        # simulations
        kwargs = {
            'color': "black",
            'linestyle': "-",
            'linewidth': "2",
        }
        result = self.scan_results['gly_scan']
        add_line(ax1, result, xid="time", yid="[glyglc]", xunit=xunit_ax1,
                 yunit=yunit_gly,
                 all_lines=True, **kwargs)
        result = self.scan_results['gs_scan']
        add_line(ax2, result, xid="time", yid="[glyglc]", xunit=xunit_ax2,
                 yunit=yunit_gly,
                 all_lines=True, **kwargs)

        # experimental data
        kwargs = {
            'color': "black",
            'linestyle': "--",
            'alpha': 0.6,
        }
        dset = self.datasets["Magnusson1992"]
        add_data(ax1, dset, xid="time", yid="gly",
                 yid_sd="gly_sd",
                 xunit=xunit_ax1, yunit=yunit_gly, label="Glycogen", **kwargs)

        dset = self.datasets["Rothman1991"]
        for subject_id in dset.subject.unique():
            add_data(ax1, dset[dset.subject == subject_id], xid="time",
                     yid="gly",
                     xunit=xunit_ax1, yunit=yunit_gly,
                     label=f"Glycogen {subject_id}", **kwargs)

        dset = self.datasets["Radziuk2001"]
        for ref_id in dset.reference.unique():
            add_data(ax2, dset[dset.reference == ref_id], xid="time",
                     yid="gly",
                     xunit=xunit_ax2, yunit=yunit_gly,
                     label=f"Glycogen {ref_id}", **kwargs)

        dset = self.datasets["Taylor1996"]
        add_data(ax2, dset, xid="time", yid="gly",
                 yid_sd="gly_sd",
                 xunit=xunit_ax2, yunit=yunit_gly, label="Glycogen", **kwargs)

        ax1.set_xlabel(f'time [{xunit_ax1}]')
        ax2.set_xlabel(f'time [{xunit_ax2}]')
        for ax in axes:
            ax.set_ylabel('glycogen [mM]')

        ax1.set_ylim(0, 500)
        ax1.set_xlim(0, 65)
        ax2.set_ylim(180, 340)

        return {
            'fig1': f
        }