Computational Modelling of Glucose Uptake in the Enterocyte

We describe an implemented model of glucose absorption in the enterocyte, as previously published by Afshar et al. (2019), The model used mechanistic descriptions of all the responsible transporters and was built in the CellML framework. It was validated against published experimental data and implemented in a modular structure which allows each individual transporter to be edited independently from the other transport protein models. The composite model was then used to study the role of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2), along with the requirement for the existence of the apical Glut2 transporter, especially in the presence of high luminal glucose loads, in order to enhance the absorption. Here we demonstrate the reproduction of the figures in the original paper by using the associated model. EDITOR'S NOTE (v3): Instructions within the manuscript changed, in order to properly execute the model files. Spelling of author's name corrected in filenames. (v4): Abstract fixes.

Computational Modelling of Glucose Uptake in the Enterocyte

We describe an implemented model of glucose absorption in the enterocyte, as previously published by Afshar et al. Afshar et al. (2019), The model used mechanistic descriptions of all the responsible transporters and was built in the CellML framework. It was validated against published experimental data and implemented in a modular structure which allows each individual transporter to be edited independently from the other transport protein models. The composite model was then used to study the role of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2), along with the requirement for the existence of the apical Glut2 transporter, especially in the presence of high luminal glucose loads, in order to enhance the absorption. Here we demonstrate the reproduction of the figures in the original paper by using the associated model. EDITOR'S NOTE (v2): Instructions within the manuscript changed, in order to properly execute the model files. Spelling of author's name corrected in filenames.

Mathematical model of excitation-contraction in a uterine smooth muscle cell

The Bursztyn et al. (2007) paper proposes a mathematical model of excitation-contraction in a myometrial smooth muscle cell (SMC). The model incorporates processes of intracellular Ca^2+ concentration control, myosin light chain (MLC) phosphorylation and stress production. We create a modularized CellML implementation of the model, which is able to simulate these processes against the original data.

Mathematical model of excitation-contraction in a uterine smooth muscle cell

The Bursztyn et al. (2007) paper proposes a mathematical model of excitation-contraction in a myometrial smooth muscle cell (SMC). The model incorporates processes of intracellular Ca^2+ concentration control, myosin light chain (MLC) phosphorylation and stress production. We create a modularized CellML implementation of the model, which is able to simulate these processes against the original data.

A Quantitative Model of Human Jejunal Smooth Muscle Cell Electrophysiology

The Poh et al. (2012) paper describes the first biophysically based computational model of human jejunal smooth muscle cell (hJSMC) electrophysiology. The ionic currents are described by either a traditional Hodgkin-Huxley (HH) formalism or a deterministic multi-state Markov (MM) formalism. We create a modularized CellML implementation of the model, which is able to reproduce clamping behaviours of individual currents and whole cell action potential traces. In addition, some inconsistencies have been uncovered and discussed in this paper.

A Quantitative Model of Human Jejunal Smooth Muscle Cell Electrophysiology

The Poh et al. (2012) paper describes the first biophysically based computational model of human jejunal smooth muscle cell (hJSMC) electrophysiology. The ionic currents are described by either a traditional Hodgkin-Huxley (HH) formalism or a deterministic multi-state Markov (MM) formalism. We create a modularized CellML implementation of the model, which is able to reproduce clamping behaviours of individual currents and whole cell action potential traces. In addition, some inconsistencies have been uncovered and discussed in this paper. EDITOR'S NOTE (v2): A typographical correction in the reproducibility report was made. (v3): This Abstract was appended with a note on changes made to versions.

A kinetic model of beta-adrenergic control in cardiac myocytes

The system of equations and figures presented in Saucerman et al. (2003) are verified and reproduced in this paper's curation effort. These checks are performed in MATLAB. With some parameter additions and modifications, we can reproduce all figures with small mismatches.

A kinetic model of beta-adrenergic control in cardiac myocytes

The system of equations and figures presented in Saucerman et al. (2003) are verified and reproduced in this paper's curation effort. These checks are performed in MATLAB. With some parameter additions and modifications, we can reproduce all figures with small mismatches.

Reproducibility study of the Fabbri et al. 2017 model of the human sinus node action potential

The sinoatrial node (SAN) is the natural pacemaker of the mammalian heart. It has been the subject of several mathematical studies, aimed at reproducing its electrical response under normal sinus rhythms, as well as under various conditions. Such studies were traditionally done using data from rabbit SAN cells. More recently, human SAN cell data have become available, resulting in the publication of a human SAN cell model (Fabbri et al., 2017), along with its CellML version. Here, we used the CellML file provided by the model authors, together with some SED-ML files and Python scripts that we created to reproduce the main results of the aforementioned modeling study. EDITOR'S NOTE (v2): this article and its OMEX archive are republished with technical changes made to the corresponding Python scripts to remove a run-time error message displayed when executing each simulation.

Reproducibility study of the Fabbri et al. 2017 model of the human sinus node action potential

The sinoatrial node (SAN) is the natural pacemaker of the mammalian heart. It has been the subject of several mathematical studies, aimed at reproducing its electrical response under normal sinus rhythms, as well as under various conditions. Such studies were traditionally done using data from rabbit SAN cells. More recently, human SAN cell data have become available, resulting in the publication of a human SAN cell model (Fabbri et al., 2017), along with its CellML version. Here, we used the CellML file provided by the model authors, together with some SED-ML files and Python scripts that we created to reproduce the main results of the aforementioned modeling study. EDITOR'S NOTE (v2): this article and its OMEX archive are republished with technical changes made to the corresponding Python scripts to remove a run-time error message displayed when executing each simulation.