Experiments

PathwaySSExperiment

Model

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

Datasets

Figures

• PathwaySSExperiment_fig1.svg

fig1

Code

https://github.com/matthiaskoenig/exsimo/tree/master/pyexsimo/experiments/hgp_gng_ss.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 plt


class PathwaySSExperiment(SimulationExperiment):
    """Steady state pathway contributions."""
    @property
    def datasets(self) -> Dict[str, DataSet]:
        return {}

    @property
    def scans(self) -> Dict[str, TimecourseScan]:
        Q_ = self.ureg.Quantity
        ss_scan = TimecourseScan(
            tcsim=TimecourseSim([
                Timecourse(start=0, end=1000, steps=1)
            ]),
            scan={
                '[glc_ext]': Q_(np.linspace(2, 14, num=40), 'mM'),
                '[glyglc]': Q_(np.linspace(0, 500, num=40), 'mM')
            },
        )
        return {
            "ss_scan": ss_scan
        }

    @property
    def figures(self) -> Dict[str, Figure]:
        import matplotlib
        import matplotlib.cm as cm
        matplotlib.rcParams['contour.negative_linestyle'] = 'solid'

        f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))
        f.subplots_adjust(hspace=0.3, wspace=0.3)
        axes = (ax1, ax2, ax3)
        for ax in axes:
            ax.set_xlabel('glucose [mM]')
            ax.set_ylabel('glycogen [mM]')

        # data processing
        result = self.scan_results['ss_scan']
        tcscan = self.scans['ss_scan']
        glc_ext = tcscan.scan['[glc_ext]']
        glyglc = tcscan.scan['[glyglc]']

        n0 = len(result.vecs[0])
        n1 = len(result.vecs[1])
        HGP = np.zeros(shape=(n0, n1))
        GNG = np.zeros(shape=(n0, n1))
        GLY = np.zeros(shape=(n0, n1))

        for k, df in enumerate(result.frames):
            index_list = result.indices[k]
            HGP[index_list] = df.HGP.values[-1]
            GNG[index_list] = df.GNG.values[-1]
            GLY[index_list] = df.GLY.values[-1]

        if result.keys[0] == '[glyglc]':
            # change order of axes
            HGP = HGP.transpose()
            GNG = GNG.transpose()
            GLY = GLY.transpose()

        # HGP
        ax1.set_title("HGU/HGP")
        ax2.set_title("glycolysis/gluconeogenesis")
        ax3.set_title("glycogen synthesis/glycogenolysis")

        for ax, data in {ax1: HGP, ax2: GNG, ax3: GLY}.items():
            _ = ax.imshow(data.transpose(), interpolation='bilinear',
                           cmap=cm.seismic, origin="lower",
                           extent=[2, 14, 0, 500],
                           aspect="auto")

            CS = ax.contour(glc_ext, glyglc, (data.transpose()),
                            np.linspace(-28, 28, num=15),
                            colors='k')
            ax.clabel(CS, inline=1, fontsize=10)

        return {
            'fig1': f
        }