Experiments

DoseResponseExperiment

Model

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

Datasets

• epinephrine.tsv
• glucagon.tsv
• insulin.tsv

Figures

• DoseResponseExperiment_fig1.svg

fig1

Code

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

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

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
from sbmlsim.pkpd import pkpd


class DoseResponseExperiment(SimulationExperiment):
    """Hormone dose-response curves."""

    @property
    def datasets(self) -> Dict[str, DataSet]:
        dsets = {}

        # dose-response data for hormones
        for hormone_key in ['Epinephrine', 'Glucagon', 'Insulin']:
            df = self.load_data(f"DoseResponse_Tab{hormone_key}")
            df = df[df.condition == "normal"]  # only healthy controls
            epi_normal_studies = [
                "Degn2004", "Lerche2009", "Mitrakou1991",
                "Levy1998", "Israelian2006", "Jones1998",
                "Segel2002"
            ]
            glu_normal_studies = [
                "Butler1991", "Cobelli2010", "Fery1993"
                                             "Gerich1993", "Henkel2005",
                "Mitrakou1991"
                "Basu2009", "Mitrakou1992",
                "Degn2004", "Lerche2009",
                "Levy1998", "Israelian2006",
                "Segel2002"
            ]
            ins_normal_studies = [
                'Ferrannini1988', 'Fery1993', 'Gerich1993', 'Basu2009',
                'Lerche2009', 'Henkel2005', 'Butler1991', 'Knop2007',
                'Cobelli2010', 'Mitrakou1992',
            ]
            # filter studies
            if hormone_key == "Epinephrine":
                df = df[df.reference.isin(epi_normal_studies)]
            elif hormone_key == "Glucagon":
                df = df[df.reference.isin(glu_normal_studies)]
                # correct glucagon data for insulin suppression
                # (hyperinsulinemic clamps)
                insulin_supression = 3.4
                glu_clamp_studies = [
                    "Degn2004", "Lerche2009", "Levy1998", "Israelian2006",
                    "Segel2002"
                ]
                df.loc[df.reference.isin(
                    glu_clamp_studies), 'mean'] = insulin_supression * df[
                    df.reference.isin(glu_clamp_studies)]['mean']
                df.loc[df.reference.isin(
                    glu_clamp_studies), 'se'] = insulin_supression * df[
                    df.reference.isin(glu_clamp_studies)]['se']

            elif hormone_key == "Insulin":
                df = df[df.reference.isin(ins_normal_studies)]

            udict = {
                'glc': df["glc_unit"].unique()[0],
                'mean': df["unit"].unique()[0],
            }
            dsets[hormone_key.lower()] = DataSet.from_df(df, udict=udict,
                                                         ureg=self.ureg)

        return dsets

    @property
    def scans(self) -> Dict[str, TimecourseScan]:
        """Scanning dose-response curves of hormones and gamma function.

        Vary external glucose concentrations (boundary condition).
        """
        Q_ = self.ureg.Quantity
        glc_scan = TimecourseScan(
            tcsim=TimecourseSim([
                Timecourse(start=0, end=1, steps=1, changes={})
            ]),
            scan={'[glc_ext]': Q_(np.linspace(2, 20, num=100), 'mM')},
        )
        return {
            "glc_scan": glc_scan
        }

    @property
    def figures(self) -> Dict[str, Figure]:
        xunit = "mM"
        yunit_hormone = "pmol/l"
        yunit_gamma = "dimensionless"

        f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10, 10))
        f.subplots_adjust(wspace=.3, hspace=.3)
        axes = (ax1, ax2, ax3, ax4)

        # process scan results
        tcscan = self.scans["glc_scan"]
        glc_vec = tcscan.scan['[glc_ext]']
        results = self.scan_results["glc_scan"]  # type: Result
        dose_response = {k: list() for k in ["glu", "epi", "ins", "gamma"]}
        for k, glc_ext in enumerate(glc_vec):
            s = results.frames[k]
            # store results
            for key in dose_response:
                value = s[key].values[0]  # initial value calculated
                dose_response[key].append(value)

        dose_response['[glc_ext]'] = glc_vec
        df = pd.DataFrame(dose_response)
        dset = DataSet.from_df(df, udict=self.udict, ureg=self.ureg)

        # plot scan results
        kwargs = {
            'linewidth': 2,
            'linestyle': '-',
            'marker': 'None',
            'color': 'black'
        }
        add_data(ax1, dset, xid="[glc_ext]", yid="glu",
                 xunit=xunit, yunit=yunit_hormone, **kwargs)
        add_data(ax2, dset, xid="[glc_ext]", yid="epi",
                 xunit=xunit, yunit=yunit_hormone, **kwargs)
        add_data(ax3, dset, xid="[glc_ext]", yid="ins",
                 xunit=xunit, yunit=yunit_hormone, **kwargs)
        add_data(ax4, dset, xid="[glc_ext]", yid="gamma",
                 xunit=xunit, yunit=yunit_gamma, **kwargs)

        # plot experimental data
        kwargs = {
            'color': "black",
            'linestyle': "None",
            'alpha': 0.6,
        }
        add_data(ax1, self.datasets["glucagon"], xid="glc", yid="mean",
                 yid_se="se",
                 xunit=xunit, yunit=yunit_hormone, label="Glucagon", **kwargs)
        add_data(ax2, self.datasets["epinephrine"], xid="glc", yid="mean",
                 yid_se="se",
                 xunit=xunit, yunit=yunit_hormone, label="Epinephrine",
                 **kwargs)
        add_data(ax3, self.datasets["insulin"], xid="glc", yid="mean",
                 yid_se="se",
                 xunit=xunit, yunit=yunit_hormone, label="Insulin", **kwargs)

        ax1.set_ylabel(f'glucagon [{yunit_hormone}]')
        ax1.set_ylim(0, 200)
        ax2.set_ylabel(f'epinephrine [{yunit_hormone}]')
        ax2.set_ylim(0, 7000)
        ax3.set_ylabel(f'insulin [{yunit_hormone}]')
        ax3.set_ylim(0, 800)
        ax4.set_ylabel(f'gamma [{yunit_gamma}]')
        ax4.set_ylim(0, 1)

        for ax in axes:
            ax.set_xlabel(f'glucose [{xunit}]')
            ax.set_xlim(2, 20)

        ax2.set_xlim(2, 8)

        return {
            'fig1': f
        }