BuildACell/InSilicoCell

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This is the top level page for models, simulations, and systems level analysis in the Build-A-Cell project. The main goal of this page is to unify different modelling frameworks, provide easy access to existing models, and more easily connect models to existing data for parameter identification and model evaluation.

Modelling and Simulation Software

SBML: Systems Biology Markup Language (SBML) is a standard format for sharing biological models. It is a one of the most widely used ways of saving, sharing, and cross validating models on different pieces of software and/or between labs.

BioSCRAPE: Fast Chemical Reaction Network (CRN) Simulator written in Cython (Python + C). Supports: deterministic, stochastic, and single-cell simulation. Many propensity types including massaction, hill functions, and general expressions. Rule-based modelling. Modelling cell growth, division and death (Lineages Package). Easy-to-use parameter inference wrappers. Build-a-cell contact: wpoole at caltech dot edu.

BioCRNpyler: Synthetic and systems biology focused Python package for rapidly assembly large chemical reaction network (CRN) models. Simulated laboratory setting for modelling cell-free experiments (CRNlab). Exports files as SBML and also directly links with BioSCRAPE for simulation. Build-a-cell contact: wpoole at caltech dot edu and apandey at caltech dot edu.

SubSBML: Sub-SBML is a Python based toolbox to create, edit, combine, and model interactions among multiple Systems Biology Markup Language (SBML) models. Sub-SBML works with a “subsystem” architecture of modeling where a single SBML model can be contained within a subsystem. Three major functionalities are developed in Sub-SBML that take advantage of this subsystem framework of modeling – creating subsystems, combining multiple subsystems, and modeling interactions such as transport of molecules and input-output relationships among multiple subsystems. Build-a-cell contact: apandey at caltech dot edu.

Modeling and Simulation Opportunities

Existing Models, Data Sets, and Parameters