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 leverage the high performance nature of the Lattice Microbes package, while providing an easy to use for simple tasks, while highly customizable for complex biological scenarios. Lattice Microbes is licensed under the University of Illinois Open Source License
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:
J.R. Peterson, M.J. Hallock, J.A. Cole, and Z. Luthey-Schulten.
A Problem Solving Environment for Stochastic Biological Simulations
Python for High Performance and Scientific Computing (PyHPC 2013) at Supercomputing 2013, 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
- Old versions 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
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. 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].