Reproducibility Study of Computational Modelling of Glucose Uptake by SGLT1 and GLUT2 in the Enterocyte

ORIGINAL Abstract Afshar et al. (2021) generated a computational model of non-isotonic glucose uptake by small intestinal epithelial cells. The model incorporates apical uptake via SGLT1 and GLUT2, basolateral eﬄux into the blood via GLUT2 and cellular volume changes in response to non-isotonic conditions. The results explain more about the role of apical GLUT2 in intestinal cell glucose absorption. Here, we used the CellML ﬁle provided by the model authors, together with SED-ML ﬁles and Python scripts


Introduction
Glucose absorption through epithelial cells in the small intestine is known to be the main source of energy generation in humans and other species. Despite the number of investigations in recent decades (McCance and Madders, 1930;Wertheimer, 1934), the absorption mechanism and transporters involved under different conditions and in different species are still not clear enough and are being debated (Karasov, 2017).
In 2019, Afshar et al. implemented a glucose absorption model in the enterocyte that contains all responsible transporters and was built using the CellML framework. Their model was used to study the role of SGLT1 and apical GLUT2, especially in the presence of a high glucose concentration in the intestinal lumen (Afshar et al., 2019). They later extended their model to a non-isotonic glucose uptake considering water transport and changes in cell volume during the absorption process (Afshar et al., 2021).
A CellML version (Cuellar et al., 2003) of their model can be found in the Physiome Model Repository (Yu et al., 2011). However, the CellML file itself is not sufficient to reproduce all the predictions presented in the primary article. Therefore, some SED-ML files (Waltemath et al., 2011) and Python scripts were created and used with the aforementioned CellML file to reproduce the main results of Afshar et al. (2021). No modifications were made to the mathematical equations or parameters of the CellML file. All the equations and parameters can be found in the original paper. In the primary article, a validated computational model was proposed. The main goal of this paper is to show that the figures in the primary paper can be reproduced by using the correlated model in the PMR. Here, we introduce a quick instruction to reproduce each figure in the original paper.

Model description
The model contains several relevant transporters on either side of the intestinal cell, which has three different compartments (mucosal or intestinal lumen, cell and blood or serosal). The movement of transcellular and paracellular water was based on the osmolarity difference between the two compartments involved, leading to changes in cell volume. Ions and glucose move into and out of the cell through specific transporters. Using the mathematical model, it is possible to predict membrane potentials, intracellular concentrations of glucose and electrolytes (Na + , K + , HCO − 3 , Cl − ), and the fluxes of these species. All the equations for ions' fluxes and concentrations are described in the supplementary material in the primary paper.
The model was developed in CellML. Simulations were run until the concentration and fluxes reached steady state. A CellML encoded version of the model is available at https://models. physiomeproject.org/workspace/841. The simulation results presented here were produced using the 2021-10-05 snapshot of OpenCOR (Garny and Hunter, 2015) together with various Python scripts that rely on a SED-ML file to configure (the solver to use, the duration of the simulation, the model parameters to track, etc.) and run a given simulation using the model encoded in CellML. Python scripts are also used to generate figures using Matplotlib (Hunter, 2007). All CellML, SED-ML and Python scripts can be found in https://models.physiomeproject.org/workspace/840.  Figure 6 in the primary paper and can be reproduced using Figure04.py.

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Figure 5. Blood glucose concentration, intracellular glucose concentration, apical GLUT2 flux, SGLT1 flux, and basolateral GLUT2 flux under different conditions. The first row is the model response to different blood flow rates. Q bl ood = 10 −17 m 3 /s is the baseline value of the blood flow rate (A-E). The second row shows the response to variations in the inlet blood glucose concentration (F-J). Rows three and four consider two different scenarios for GLUT2 translocation to the apical membrane. Row three shows simulations run under the assumption that apical GLUT2 is translocated from basolateral GLUT2. Different ratios between apical and basolateral GLUT2 are considered with the total number fixed (K-O). Row four shows simulations run under the assumption that apical GLUT2 is translocated from intracellular vesicles. The number of basolateral GLUT2 is fixed and labels represent the fraction of total GLUT2 in the apical and basolateral membranes (P-T). This figure corresponds to Figure 7 in the primary paper and can be reproduced using   Figure 9 in the primary paper and can be reproduced using Figure07.py.

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There are details/documentation on how the source code was compiled There are details on how to run the code in the provided documentation The initial conditions are provided for each of the simulations Details for creating reported graphical results from the simulation results Source code: a declarative language is used (e.g. SBML, CellML, NeuroML) The algorithms used are defined or cited in previous articles The algorithm parameters are defined Post-processing of the results are described in sufficient detail

Executable model provided:
The model is executable without source (e.g. desktop application, compiled code, online service) There are sufficient details to repeat the required simulation experiments