Lattice microbes: High-performance stochastic simulation method for the reaction-diffusion master equation
Open accessAbstract
Spatial stochastic simulation is a valuable technique for studying reactions in biological systems. With the availability of high‐performance computing (HPC), the method is poised to allow integration of data from structural, single‐molecule and biochemical studies into coherent computational models of cells. Here, we introduce the Lattice Microbes software package for simulating such cell models on HPC systems. The software performs either well‐stirred or spatially resolved stochastic simulations with approximated cytoplasmic crowding in a fast and efficient manner. Our new algorithm efficiently samples the reaction‐diffusion master equation using NVIDIA graphics processing units and is shown to be two orders of magnitude faster than exact sampling for large systems while maintaining an accuracy of ∼0.1%. Display of cell models and animation of reaction trajectories involving millions of molecules is facilitated using a plug‐in to the popular VMD visualization platform. The Lattice Microbes software is open source and available for download at http://www.scs.illinois.edu/schulten/lm © 2012 Wiley Periodicals, Inc.
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Lattice Microbes simulations use time steps of the order of microseconds, which allows for a potential feedback loop between these computational approaches.
“Besides, Lattice Microbes simulations of the Luthey-Schulten group (Roberts et al., 2013) use time steps of the order of microseconds, which allows for a potential feedback loop between these computational approaches.”
From Molecular dynamics simulation of an entire cell (2023) · cites this paper as Roberts et al., 2013
Results and Discussion §3.1 reports time steps of 3.0 × 10⁻³ to 3.0 × 10⁻⁴ seconds for the simulations tested—hundreds to thousands of microseconds, rather than steps on the order of microseconds. The proposed feedback loop is the citing authors’ inference, not a finding attributed to this source. (LLM, conf=0.88)
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“By combining these with hybrid stochastic-deterministic methods leveraging GPU-accelerated simulation software (Roberts et al., 2012; Hallock et al., 2014; Bianchi et al., 2018), a well-stirred WCM (WS-WCM) and 3D spatially resolved WCM (4D-WCM) that predict time-dependent Syn3A cell states were…”
Self-citationMolecular Level Origin of Ion Dynamics in Highly Concentrated Electrolytes
Shigenobu, K., Tsuzuki, S., Philippi, F., Sudoh, T., Ugata, Y., Dokko, K., Watanabe, M., Ueno, K., & Shinoda, W. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c05864 · Cited by 18 · see citing works
Fu, Y., Yogurtcu, O. N., Kothari, R., Thorkelsdottir, G., Sodt, A. J., & Johnson, M. E. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5120516 · Cited by 18 · see citing works
Multiscale Modeling of Diffusion in a Crowded Environment
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Mechanical approach to chemical transport
Kocherginsky, N., & Gruebele, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600866113 · Cited by 17 · see citing works
A computational model of feedback-mediated hematopoietic stem cell differentiation in vitro
Mahadik, B. P., Hannon, B. M., & Harley, B. A. C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212502 · Cited by 17 · see citing works
Ribosome biogenesis in replicating cells: integration of experiment and theory
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Self-citationA spatially resolved stochastic model reveals the role of supercoiling in transcription regulation
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Self-citationModeling binary and graded cone cell fate patterning in the mouse retina
Eldred, K. C., Avelis, C. M., Johnston, R. J., & Roberts, E. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007691 · Cited by 15 · see citing works
Self-citationJohnson, M. E. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b08339 · Cited by 15 · see citing works
Pal, S., & SHARMA, R. R. (2021) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2021.107534 · Cited by 14 · see citing works
Melo, M. C. R., & Bernardi, R. C. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.01.042 · Cited by 15 · see citing works
Cellular and molecular structure as a unifying framework for whole-cell modeling
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Self-citationHepburn, I., Chen, W., & Schutter, E. D. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4960034 · Cited by 15 · see citing works
Spatial modeling algorithms for reactions and transport in biological cells
Francis, E. A., Laughlin, J. G., Dokken, J. S., Finsberg, H. N. T., Lee, C. T., Rognes, M. E., & Rangamani, P. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00745-x · Cited by 14 · see citing works
Gameiro, D., Pérez‐Pérez, M., Rodríguez, G. P., Monteiro, G., Azevedo, N. F., & Lourenço, A. M. G. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv096 · Cited by 13 · see citing works
Kang, H.-W., & Radek Erban, A. (2019) · Bulletin of Mathematical Biology · doi:10.1007/s11538-019-00613-0 · Cited by 13 · see citing works
Mesoscopic modeling of random walk and reactions in crowded media
Engblom, S., Lötstedt, P., & Meinecke, L. (2018) · Physical review. E · doi:10.1103/physreve.98.033304 · Cited by 12 · see citing works
Reaction-diffusion kinetics on lattice at the microscopic scale
Chew, W.-X., Kaizu, K., Watabe, M., Muniandy, S. V., Takahashi, K., & Arjunan, S. N. V. (2018) · Physical review. E · doi:10.1103/physreve.98.032418 · Cited by 11 · see citing works
Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations
Vasan, R., Rowan, M. P., Lee, C. T., Johnson, G. R., Rangamani, P., & Holst, M. (2020) · Frontiers in Physics · doi:10.3389/fphy.2019.00247 · Cited by 11 · see citing works
A community approach to whole-cell modeling
Singla, J., & White, K. L. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.03.009 · Cited by 11 · see citing works
pSpatiocyte: a high-performance simulator for intracellular reaction-diffusion systems
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Kliuchnikov, E., Zhmurov, A. A., Marx, K. A., Mogilner, A., & Barsegov, V. A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010165 · Cited by 10 · see citing works
Gibbs free energy change of a discrete chemical reaction event
Floyd, C., Papoian, G. A., & Jarzynski, C. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5140980 · Cited by 10 · see citing works
Hallock, M. J., & Luthey-Schulten, Z. A. (2016) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2016.118 · Cited by 9 · see citing works
Self-citationWhole Cell Modeling: From Single Cells to Colonies
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Self-citationSHARMA, R. R., & Roberts, E. (2016) · Physical Biology · doi:10.1088/1478-3975/13/3/036003 · Cited by 9 · see citing works
Self-citationHybrid CME–ODE method for efficient simulation of the galactose switch in yeast
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Atitey, K., Loskot, P., & Rees, P. C. T. (2018) · Journal of Computational Biology · doi:10.1089/cmb.2018.0055 · Cited by 7 · see citing works
From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation
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A computational spatial whole-Cell model for hepatitis B viral infection and drug interactions
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Multi-resolution dimer models in heat baths with short-range and long-range interactions
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Stochastic self-tuning hybrid algorithm for reaction-diffusion systems
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Stochastic Analysis Demonstrates the Dual Role of Hfq in Chaperoning E. coli Sugar Shock Response
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Parallelization of Particle‐Based Reaction–Diffusion Simulations Using MPI
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Assembly of Macromolecular Complexes in the Whole-Cell Model of a Minimal Cell
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Space-induced bifurcation in repression-based transcriptional circuits
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Integrative modeling of the cell
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pSpatiocyte: A Parallel Stochastic Method for Particle Reaction-Diffusion SystemsPreprint
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- Authors
- Roberts, E., Stone, J. E., & Luthey‐Schulten, Z.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2012-09-25
- Volume
- 34
- Issue
- 3
- Pages
- 245-255
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- PMC
- PMC3762454
- OpenAlex
- W2151643303
- Cited by
- 117
- References
- 51
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 3 · 2023: 9 · 2022: 11 · 2021: 6 · 2020: 12 · 2019: 10 · 2018: 12 · 2017: 10 · 2016: 11 · 2015: 8 · 2014: 7 · 2013: 7
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of Computational Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (P41GM104601)
- National Center for Research Resources (RR005969, P41RR005969)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Keywords
- reaction-diffusion master equation, spatial stochastic simulation, reaction-diffusion systems, GPU acceleration, cytoplasmic crowding, cell model, high-performance computing, open source software
- MeSH terms
- Algorithms, Computer Simulation, Diffusion, DNA, Escherichia coli, Gene Expression Regulation, Models, Biological, Protein Binding, Software, Stochastic Processes, Transcription Factors
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