Molecular and subcellular simulation
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.
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. 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.
Subcellular Simulation Webapp
An online tool for configuring and running compartmental subcellular simulations. This tool allows import of SBML model files from the subcellular model building and calibration toolset workflow or other external sources.
The tool allows users to setup and configure BioNetGen and STEPS simulations. Users can populate mesh models of spines and other neural structures, and run stochastic simulations of signalling pathways.
Related publications
- Santos, J.P.G., Pajo, K., Trpevski, D. et al. A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience. Neuroinform 20, 241–259 (2022). https://doi.org/10.1007/s12021-021-09546-3
- Olivia Eriksson, Alexandra Jauhiainen, Sara Maad Sasane, Andrei Kramer, Anu G Nair, Carolina Sartorius, Jeanette Hellgren Kotaleski, Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models, Bioinformatics, Volume 35, Issue 2, 15 January 2019, Pages 284–292, https://doi.org/10.1093/bioinformatics/bty607
- Timothy W Church, Parul Tewatia, Saad Hannan, João Antunes, Olivia Eriksson, Trevor G Smart, Jeanette Hellgren Kotaleski, Matthew G Gold (2021) AKAP79 enables calcineurin to directly suppress protein kinase A activity eLife 10:e68164 https://doi.org/10.7554/eLife.68164
- Olivia Eriksson, Upinder Singh Bhalla, Kim T Blackwell, Sharon M Crook, Daniel Keller, Andrei Kramer, Marja-Leena Linne, Ausra Saudargienė, Rebecca C Wade, Jeanette Hellgren Kotaleski (2022) Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows eLife 11:e69013. https://doi.org/10.7554/eLife.69013