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MCell-R: A Particle-Resolution Network-Free Spatial Modeling Framework
Jose-Juan Tapia1, Ali Sinan Saglam1, Jacob Czech2
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Methods in Molecular Biology (Clifton, N.J.)
|April 5, 2019
Summary
We developed MCell-R, a new framework for modeling complex biochemical networks. It efficiently simulates spatially resolved molecules, overcoming limitations of existing methods for combinatorial complexity in cell regulation.
Area of Science:
- Computational Biology
- Biochemistry
- Systems Biology
Background:
- Spatial heterogeneity significantly impacts cellular regulation and decision-making.
- Existing modeling methods struggle with the combinatorial complexity of multistate, multicomponent biochemical systems.
- Standard approaches for chemical reaction networks become computationally intractable for detailed biological models.
Purpose of the Study:
- To develop a computational framework that addresses the limitations of current methods for modeling complex spatial biochemical systems.
- To integrate rule-based modeling with particle-based simulation for enhanced accuracy and efficiency.
- To enable the simulation of spatially resolved molecules in biologically relevant contexts.
Main Methods:
- Extended the MCell (particle-based spatial Monte Carlo simulator) with BioNetGen and NFsim capabilities.
- Utilized BioNetGen syntax for specifying biomolecules as structured objects with states and binding properties.
- Employed NFsim's network-free algorithm for efficient simulation of rule-based models, even with large implicit networks.
Main Results:
- Developed MCell-R, a novel framework combining spatial simulation with rule-based modeling.
- Successfully addressed the challenge of combinatorial complexity in biochemical systems.
- Enabled efficient simulation of spatially resolved individual molecules over relevant biological scales.
Conclusions:
- MCell-R provides an efficient solution for simulating complex biochemical networks characterized by combinatorial complexity.
- The framework allows for detailed, spatially resolved modeling of molecular interactions in cellular systems.
- This advancement facilitates a deeper understanding of cell regulation and decision-making processes influenced by spatial factors.
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