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. Along with it, the pyLM problem solving environment allows for customizable simulations.

The current version of Lattice Microbes , v2.3, 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.

To install Lattice Microbes v2.3 to build and simulate the HeLa cell model given in:

Zhaleh Ghaemi, Joseph R. Peterson, Martin Gruebele, and Zaida Luthey-Schulten, An in-silico human cell model reveals the influence of organization on RNA splicing, PLOS Computational Biology, 2020, doi:10.1371/journal.pcbi.1007717

Please follow the HeLa Cell Model Installation Guide and the LM Linux Installation Guide. and use LM_2.3_dependency_sources.tar as mentioned in the Installation Guide

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

PopulationFBA includes a Matlab script and data file which, when run, samples the copy number distributions of over 350 metabolic proteins in E. coli, and uses them to set constraints on reaction fluxes throughout a comprehensive model of metabolism. A detailed description of this methodology can be found in:

P. Labhsetwar, J.A. Cole, E. Roberts, N.D. Price, Z. Luthey-Schulten. Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proc. Nat. Acad. Sci., 110(34):14006-11, 2013, doi: 10.1073/pnas.1222569110

Includes a compressed directory containing source code for simulating the growth of a colony using the 3DdFBA methodology described in:

Cole JA, Kohler L, Hedhli J, and Luthey-Schulten Z. (2015) Spatially-Resolved Metabolic Cooperativity Within Dense Bacterial Colonies. BMC Syst. Biol. 2015, 9(15), doi:10.1186/s12918-015-0155-1

A method of characterizing allosteric signalling through biomolecular complexes. FEBS Letters, 584(2):376-386, 2010

Alexis Black Pyrkosz, John Eargle, Anurag Sethi, and Zaida Luthey-Schulten
Exit strategies for charged tRNA from GluRS J. Mol. Biol., 397:1350-1371, 2010

Anurag Sethi, John Eargle, Alexis Black, and Zaida Luthey-Schulten.
Dynamical Networks in tRNA:protein complexes Proc. Natl. Acad. Sci. U S A, 106(16):6620-6625, 2009

Is a unified bioinformatics analysis environment that allows one to organize, display, and analyze both sequence and structure data for proteins and nucleic acids. Special emphasis is placed on analyzing the data within the framework of evolutionary biology.

In any publication of scientific results based completely or in part on the use of MultiSeq, please reference:

Elijah Roberts, John Eargle, Dan Wright, and Zaida Luthey-Schulten.
MultiSeq: Unifying sequence and structure data for evolutionary analysis.
BMC Bioinformatics, 2006, 7:382.

About Us

MultiSeq was primarily developed by Zan Luthey-Schulten, Elijah Roberts, John Eargle, and Dan Wright. We would like to thank Patrick O'Donoghue, Anurag Sethi, John Stone, Michael Bach, and Carl Woese for their help and assistance.

See the MultiSeq press release: http://www.news.uiuc.edu/news/
06/0918software.html

Is a program that generates a nonredundant set of proteins that represents the topology of the molecular phylogenetic tree of the homologous group.

SeqQR is available on the following platforms:

• Windows (XP, 2000) - Download
• UNIX (Solaris, Linux, Mac OS X) - Download

Representative papers related to SeqQR:

A. Sethi, P. O'Donoghue, and Z. Luthey-Schulten. Evolutionary profiles from the QR factorization of multiple sequence alignments. PNAS, 102:4045, 2005.

P. O'Donoghue and Z. Luthey-Schulten, Evolutionary profiles derived from the QR factorization of multiple structural alignments gives an economy of information. J. Mol. Biol., 346:875, 2005.

P. O'Donoghue, and Z. Luthey-Schulten. On the evolution of structure in aminoacyl-tRNA synthetases. Microbiol. Mol. Biol. Rev., 67:550, 2003.

Is an application for the tessellation of 3D volume in and around biological molecules. It creates Tcl script files which are run within VMD to visualize volumes in context with their associated molecular structures.

For help with installation and use of Tessellator, click here.

For supplementary material associated with the paper "Visualizing the Dual Space of Biological Molecules", click here.

Representative paper related to Tessellator:

J Eargle, and Z. Luthey-Schulten, Visualizing the Dual Space of Biological Molecules. Comp. Biol. Chem., 30:219, 2006.

Is an application for the placement atomic ions around 3D biomolecular structures. It uses a pdb file with associated atomic charges to create a grid of electrostatic potential around the structures. An ion is placed at the lowest energy gridpoint, and the grid is then recalculated for the placement of the next ion. Multiple ion types can be placed in a specified order.