MCell4 with BioNetGen: A Monte Carlo simulator of rule-based reaction-diffusion systems with Python interface
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1.
California Institute of Technology
Abstract
Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes, membranes, scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4’s Python interface opens up completely new possibilities for interfacing with external simulators to allow creation of sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support, implemented through a new open-source library libBNG (also introduced in this paper), provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, also in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison.
Copyright and License
© 2024 Husar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding
Funding for this research was provided by NIH MMBioS P41-GM103712 (TJS, TMB, AH, GCG, MO, JGY, JRF, AS), NIH CRCNS R01-MH115556 (TJS, TMB, MO, MBK), NIH CRCNS R01-MH129066 (TJS, TMB, MO, MBK), NSF NeuroNex DBI-1707356 (TJS, TMB, AH, GCG, MO, JGY), and NSF NeuroNex DBI-2014862 (TJS, TMB, AH, GCG, MO, JGY). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgement
The authors thank Dr. Padmini Rangamani for discussions on boundary conditions and the biophysics of diffusion near membranes. We heartily thank Dr. Markus Dittrich, Dr. Burak Kaynak, Dr. Oliver Ernst, Dr. Rex Kerr, Jacob Czech, Jed Wing, Don Spencer, and Robert Kuczewski whose insights on API design for discrete event simulation have guided the development of MCell over the years, laying the foundation for MCell4. We thank Jorge Aldana for his expert technical support of the computing infrastructure in the Computational Neurobiology Laboratory at Salk.
Conflict of Interest
The authors have declared that no competing interests exist.
Additional Information
This is a PLOS Computational Biology Software paper.
Code Availability
Easy installation package for all major computing platforms are available at mcell.org/download.html.
Source code is available at github.com/mcellteam/mcell github.
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Additional details
- Caltech groups
- Division of Biology and Biological Engineering
- Publication Status
- Published