MCSim

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MCSim
Original author(s) Don Maszle, Frederic Y. Bois
Developer(s) GNU Project
Initial release1991;34 years ago (1991)
Stable release
6.2.0 / 3 June 2020;4 years ago (2020-06-03)
Repository
Written in C
Operating system Cross-platform
Type Numerical analysis
License GNU General Public License
Website www.gnu.org/software/mcsim

GNU MCSim is a suite of simulation software. It allows users to design statistical or simulation models, perform Monte Carlo simulations, and Bayesian inference through (tempered) Markov chain Monte Carlo (MCMC) simulations. The latest version allows parallel computing of Monte Carlo or MCMC simulations.

Contents

Description

GNU MCSim is a simulation and statistical inference tool for algebraic or differential equation systems, optimized for performing Monte Carlo analysis. The software comprises a model generator and a simulation engine:

Internally, the software uses the GNU Scientific Library for some of its numerical calculations.

History

The project began in 1991 in Berkeley when Don Maszle and Frederic Y. Bois translated in C and reorganized a program that Bois had developed at Harvard for his PhD thesis. The main motive for the work was to be able to quickly develop and easily maintain physiologically based pharmacokinetic modelling (PBPK) models. However, the syntax was defined with enough generality that many algebraic and first-order ordinary differential equations can be solved. The ability to perform efficient Monte Carlo simulations was added early on, for the research needs of the group. The code was made freely available from a server at UC Berkeley. Discussions with Stuart Beal at University of California, San Francisco (UCSF) School of Pharmacy, led the team to investigate the use of Markov chain Monte Carlo techniques for PBPK models' calibration. The corresponding code was developed by Maszle, during a project in collaboration with Andrew Gelman, then professor at University of California, Berkeley (UC Berkeley) Statistics Department. Additional code written by Ken Revzan allowed the definition and Bayesian calibration of hierarchical (multilevel) statistical models. At the time of these developments (around 1996), those abilities were unique for a freely distributed, easily accessible, very powerful and versatile software. Since then the software has been consistently maintained and extended.

Version history

Licensing

GNU MCSim is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or any later version, at user option.

Platform availability

The C source code is provided and can be compiled on any machine with a C compiler. The GNU Scientific Library must be available also on the target platform to use a few extra distributions in statistical models. To use the SBML translation abilities, the LibSBLM library should be installed. Starting with version 6.0.0, the Sundials Cvodes integrator is also used. To take advantage of parallel computation (from version 6.2.0 on) a Message Passing Interface (MPI) library must be installed.

See also

References

Bois F., Maszle D., 1997, MCSim: A simulation program, Journal of Statistical Software, 2(9):http://www.stat.ucla.edu/journals/jss/v02/i09.

Jonsson F., Johanson G., 2003, The Bayesian population approach to physiological toxicokinetic-toxicodynamic models – An example using the MCSim software, Toxicology Letters 138:143-150.

Bois F., 2009, GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models, Bioinformatics, 25:1453-1454, doi: 10.1093/bioinformatics/btp162.

Allen B.C., Hack E.C., Clewell H.J., 2007, Use of Markov chain Monte Carlo analysis with a physiologically-based pharmacokinetic model of methylmercury to estimate exposures in u.s. women of childbearing age, Risk Analysis, 27:947-959.

Covington T.R., Gentry P.R., et al., 2007, The use of Markov chain Monte Carlo uncertainty analysis to support a Public Health Goal for perchloroethylene, Regulatory Toxicology and Pharmacology, 47:1-18.

David R.M., Clewell H.J., et al., 2006, Revised assessment of cancer risk to dichloromethane II. Application of probabilistic methods to cancer risk determinations. Regulatory Toxicology and Pharmacology 45: 55–65.

Franks S.J., Spendiff M.K., et al., 2006, Physiologically based pharmacokinetic modelling of human exposure to 2-butoxyethanol, Toxicology Letters 162:164-173.

Hack E.C., 2006, Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models, Toxicology, 221:241-248.

Hack E.C., Chiu W.A, et al., 2006, Bayesian population analysis of a harmonized physiologically based pharmacokinetic model of trichloroethylene and its metabolites, Regulatory Toxicology and Pharmacology, 46:63-83.

Lyons M.A., Yang R.S.H, Mayeno A.N., Reisfeld B. 2008, Computational toxicology of chloroform: reverse dosimetry using Bayesian inference, Markov chain Monte Carlo simulation, and human biomonitoring data, Environmental Health Perspectives, 116:1040-1046.

Marino, D. J., H. Clewell, et al., 2006, Revised assessment of cancer risk to dichloromethane: part I Bayesian PBPK and dose-response modeling in mice, Regulatory Toxicology and Pharmacology 45:44-54.

Mezzetti M., Ibrahim J.G., et al., 2003, A Bayesian compartmental model for the evaluation of 1,3-butadiene metabolism, Journal of the Royal Statistical Society, Series C, 52:291-305.

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