Lattice Microbes and pyLM
Lattice Microbes is a software package for efficiently sampling trajectories from the chemical and reaction-diffusion master equations (CME/RDME) on high performance computing (HPC) infrastructure using both exact and approximate methods. pyLM is a problem solving environment written in Python that leverages the high-performance nature of the Lattice Microbes package while providing ease of use for common stochastic simulations and high customizability for complex biological applications. Lattice Microbes is licensed under the University of Illinois Open Source License
Lattice Microbes v2.2 brings with it the capability to split computations over many GPUs attached to a computer, allowing up to 1 billion grid points on a machine fitted with four current top-of-the-line GPUs. pyLM v1.0 is the initial release.
The current version of Lattice Microbes is v2.3a, released 2016-09-08.
Our recent publication provides an overview of the software. In any publication of scientific results based completely or in part on the use of Lattice Microbes and pyLM, please reference:
M. J. Hallock, J. E. Stone, E. Roberts, C. Fry, Z. Luthey-Schulten.
Simulation of reaction diffusion processes over biologically-relevant size and time scales using multi-GPU workstations
Parallel Comput. 40:86-99, 2014, doi: 10.1016/j.parco.2014.03.009
J.R. Peterson, M.J. Hallock, J.A. Cole, and Z. Luthey-Schulten.
A Problem Solving Environment for Stochastic Biological Simulations
PyHPC '13: Proceedings of the 3rd Workshop on Python for High-Performance and Scientific Computing, 2013
Elijah Roberts, John E Stone, and Zaida Luthey-Schulten.
Lattice Microbes: high-performance stochastic simulation method for the reaction-diffusion master equation
J. Comput. Chem., 34(3):245-255, 2013
- Code versions 2.1 and before can be found here.
Please email support at Lattice Microbes Support for bug reports, installation problems, and help/feature requests. Subscribe to the mailing list for news and announcements at: Lattice Microbes Mailing List.
Other publications describing and/or using the software
T.M. Earnest, J.A. Cole, J.R. Peterson, M.J. Hallock, T.E. Kuhlman, Z. Luthey-Schulten.
Ribosome biogenesis in replicating cells: integration of experiment and theory.
Biopolymers, 2016, Oct, 105(10):735-751, doi:10.1002/bip.22892
M.J. Hallock, Z. Luthey-Schulten.
Improving reaction kernel performance in Lattice Microbes: particle-wise propensities and run-time generated code.
Parallel and Distributed Processing Symposium Workshop (IPDPSW), 2016 IEEE International.
J.E. Stone, M.J. Hallock, J.C. Phillips, J.R. Peterson, Z. Luthey-Schulten, K. Schulten.
Evaluation of Emerging Energy-Efficient Heterogeneous Computing Platforms for Biomolecular and Cellular Simulation Workloads.
Parallel and Distributed Processing Symposium Workshop (IPDPSW), 2016 IEEE International
J.R. Peterson*, J.A. Cole*, J. Fei, T.J. Ha and Z. Luthey-Schulten.
Effects of DNA Replication on mRNA Noise.
Proc. Nat. Acad. Sci., 2015, Nov 11, 112(52): 15886-15891 doi:10.1073/pnas.1516246112
T. M. Earnest, J. Lai, K. Chen, M. J. Hallock, J. R. Williamson, and Z. Luthey-Schulten.
Towards a whole-cell model of ribosome biogenesis: Kinetic modeling of SSU assembly.
Biophysical Journal, 2015, Sep 15, 109(6): 1117-35 doi:10.1016/j.bpj.2015.07.030
J. Cole, Z. Luthey-Schulten.
Whole Cell Modeling: From Single Cells to Colonies.
Isr. J. Chem., 2014 doi: 10.1002/ijch.201300147
J. Cole, M. J. Hallock, P. Labhsetwar, J. R. Peterson, J. E. Stone, Z. Luthey-Schulten.
Stochastic Simulations of Cellular Processes: From Single Cells to Colonies.
in Computational Systems Biology 2nd Edition: From Molecular Mechanisms to Disease, Eds. Kriete and Eils, Elsevier, 2014
E. Roberts, A. Magis, J.O. Ortiz, W. Baumeister, and Z. Luthey-Schulten.
Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study
PLoS. Comput. Biol., 7(3):e1002010, 2011
E. Roberts, J.E. Stone, L. Sepulveda, W.W. Hwu, and Z. Luthey-Schulten.
Long time-scale simulations of in vivo diffusion using GPU hardware
In Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing, 2009
This work was partially supported by the Department of Energy Office of Science (BER) [DE-FG02-10ER6510], the National Institutes of Health through the Center for Macromolecular Modeling and Bioinformatics [NIH-RR005969], and the National Science Foundation [MCB08-44670].