Introduction

The subcellular modeling and simulation services consist of tools for building and simulating subcellular level models. Such models often describe molecular signaling pathways within the cell. The service contains two different projects. The subcellular model building and calibration tool set (1) is focused on model calibration (parameter estimation) using simpler, one compartmental, models. The subcellular simulation webapp (2), allows the user to construct more detailed compartmental models using the STEPs or BioNetGen simulators. The tools are interoperable so that e.g. models can be constructed and calibrated using (1) and then simulated with more details in (2). The calibration toolset also allows uncertainty quantification and sensitivity analysis.

1. Subcellular model building and calibration tool set

Toolset for data-driven building of subcellular biochemical signaling pathway models. The toolset includes interoperable modules for: model building, calibration (parameter estimation) and model analysis. All information needed to perform these tasks are stored in a structured, human- and machine-readable file format based on SBtab (Lubitz et al. 2016). This information includes: models, experimental calibration data and prior assumptions on parameter distributions. The toolset enables simulations of the same model in simulators with different characteristics, e.g. STEPS, NEURON, MATLAB’s Simbiology and R via automatic code generation. The parameter estimation is done by optimization or Bayesian approaches. Model analysis includes global sensitivity analysis and functionality for analyzing thermodynamic constraints and conserved moieties.

[1] Lubitz, T., Hahn, J., Bergmann, F.T., Noor, E.,. Klipp, E, Liebermeister, W. (2016). SBtab: A flexible table format for data exchange in systems biology. Bioinformatics, 15;32(16), 2559-61.

Golden paragraph 

Source Code

Source code for model processing and Bayesian parameter estimation (https://github.com/icpm-kth/uqsa/, mirrored );

Documentation 

Documentation for modelling workflow with Bayesian parameter estimation (https://github.com/icpm-kth/)

Examples

Jupyter notebook example of modelling workflow with Bayesian parameter estimation (https://github.com/icpm-kth/ )

Publications

Santos, Pajo, et al (2021), Neuroinformatics; 20, 241–259, https://doi.org/10.1007/s12021-021-09546-3 ;

Eriksson, et al (2019), Bioinformatics, 35(2), 284-292, https://doi.org/10.1093/bioinformatics/bty607; 

Church, et al (2021), eLife 10:e68164, https://doi.org/10.7554/eLife.68164