pyLM  v1.1
A Python Problem Solving Environment for the simulation of stochastic biological systems
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pyLM is a Problem Solving Environment (PSE) for biological simulations [1]. Written in Python, it wraps and extends the highly optimized multi-GPU Lattice Microbes stochastic simulation software [2,3,4]. The PSE is comprised of a base set of functionality to set up, monitor and modify simulations, as well as a set of standard post-processing routines that interface to other Python packages, including NumPy, SciPy, H5py, iGraph to name a few. See [1] for additional information as well as the user guide on the main website. If you use pyLM in your simulations, please cite references [1] and [3] below.


  1. J.R. Peterson, M.J. Hallock, J.A. Cole and Z. Luthey-Schulten. A Problem Solving Environment for Stochastic Biological Simulations. PyHPC 2013 at Supercomputing 2013, 2013.
  2. 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.
  3. E. Roberts, J. E. Stone, and Z. Luthey-Schulten. Lattice microbes: high-performance stochastic simulation method for the reaction-diffusion master equation. J. Comp. Chem., 32(3), 245-55, 2013.
  4. M.J. Hallock, J.E. Stone, E. Roberts, C. Fry and 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.