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MCell-R: A Particle-Resolution Network-Free Spatial Modeling Framework

  • Jose-Juan Tapia
  • Ali Sinan Saglam
  • Jacob Czech
  • Robert Kuczewski
  • Thomas M. Bartol
  • Terrence J. Sejnowski
  • James R. FaederEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1945)

Abstract

Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making. For this reason, a number of methods have been developed to model spatial heterogeneity and incorporated into widely used modeling platforms. Unfortunately, the standard approaches for specifying and simulating chemical reaction networks become untenable when dealing with multistate, multicomponent systems that are characterized by combinatorial complexity. To address this issue, we developed MCell-R, a framework that extends the particle-based spatial Monte Carlo simulator, MCell, with the rule-based model specification and simulation capabilities provided by BioNetGen and NFsim. The BioNetGen syntax enables the specification of biomolecules as structured objects whose components can have different internal states that represent such features as covalent modification and conformation and which can bind components of other molecules to form molecular complexes. The network-free simulation algorithm used by NFsim enables efficient simulation of rule-based models even when the size of the network implied by the biochemical rules is too large to enumerate explicitly, which frequently occurs in detailed models of biochemical signaling. The result is a framework that can efficiently simulate systems characterized by combinatorial complexity at the level of spatially resolved individual molecules over biologically relevant time and length scales.

Key words

Rule-based modeling Spatial modeling Particle-based modeling Stochastic simulation Network-free simulation Compartmental modeling 

Notes

Acknowledgments

This work was supported in part by the US National Institutes of Health grants P41GM103712 and R01GM115805.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jose-Juan Tapia
    • 1
  • Ali Sinan Saglam
    • 1
  • Jacob Czech
    • 2
  • Robert Kuczewski
    • 3
  • Thomas M. Bartol
    • 3
  • Terrence J. Sejnowski
    • 3
  • James R. Faeder
    • 1
    Email author
  1. 1.Department of Computational and Systems BiologyUniversity of PittsburghPittsburghUSA
  2. 2.Pittsburgh Supercomputing CenterCarnegie Mellon UniversityPittsburghUSA
  3. 3.Howard Hughes Medical InstituteThe Salk Institute for Biological StudiesLa JollaUSA

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