Literature DB >> 19304877

GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models.

Frédéric Y Bois1.   

Abstract

SUMMARY: Statistical inference about the parameter values of complex models, such as the ones routinely developed in systems biology, is efficiently performed through Bayesian numerical techniques. In that framework, prior information and multiple levels of uncertainty can be seamlessly integrated. GNU MCSim was precisely developed to achieve those aims, in a general non-linear differential context. Starting with version 5.3.0, GNU MCSim reads in and simulates Systems Biology Markup Language models. Markov chain Monte Carlo simulations can be used to generate samples from the joint posterior distribution of the model parameters, given a dataset and prior distributions. Hierarchical statistical models can be used. Optimal design of experiments can also be investigated.
AVAILABILITY AND IMPLEMENTATION: The GNU GPL source is available at (http://savannah.gnu.org/projects/mcsim). A distribution package is at (http://www.gnu.org/software/mcsim). GNU MCSim is written in standard C and runs on any platform supporting a C compiler. Supplementary Material is available online at (http://www.gnu.org/software/mcsim).

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Year:  2009        PMID: 19304877     DOI: 10.1093/bioinformatics/btp162

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  36 in total

1.  Predicting the Disposition of the Antimalarial Drug Artesunate and Its Active Metabolite Dihydroartemisinin Using Physiologically Based Pharmacokinetic Modeling.

Authors:  Ryan Arey; Brad Reisfeld
Journal:  Antimicrob Agents Chemother       Date:  2021-02-17       Impact factor: 5.191

2.  Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans.

Authors:  Todd J Zurlinden; Garrett J Eppers; Brad Reisfeld
Journal:  Antimicrob Agents Chemother       Date:  2016-07-22       Impact factor: 5.191

3.  Probabilistic generation of random networks taking into account information on motifs occurrence.

Authors:  Frederic Y Bois; Ghislaine Gayraud
Journal:  J Comput Biol       Date:  2015-01       Impact factor: 1.479

4.  Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim.

Authors:  Periklis Tsiros; Frederic Y Bois; Aristides Dokoumetzidis; Georgia Tsiliki; Haralambos Sarimveis
Journal:  J Pharmacokinet Pharmacodyn       Date:  2019-04-04       Impact factor: 2.745

5.  Physiologically based modeling of the pharmacokinetics of acetaminophen and its major metabolites in humans using a Bayesian population approach.

Authors:  Todd J Zurlinden; Brad Reisfeld
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2015-01-31       Impact factor: 2.441

6.  PBPK modeling of impact of nonalcoholic fatty liver disease on toxicokinetics of perchloroethylene in mice.

Authors:  Chimeddulam Dalaijamts; Joseph A Cichocki; Yu-Syuan Luo; Ivan Rusyn; Weihsueh A Chiu
Journal:  Toxicol Appl Pharmacol       Date:  2020-05-21       Impact factor: 4.219

7.  Modeling and Simulation of Pretomanid Pharmacodynamics in Pulmonary Tuberculosis Patients.

Authors:  Michael A Lyons
Journal:  Antimicrob Agents Chemother       Date:  2019-09-30       Impact factor: 5.191

8.  A Physiologically Based Pharmacokinetic Model for Naphthalene With Inhalation and Skin Routes of Exposure.

Authors:  Dustin F Kapraun; Paul M Schlosser; Leena A Nylander-French; David Kim; Erin E Yost; Ingrid L Druwe
Journal:  Toxicol Sci       Date:  2020-10-01       Impact factor: 4.849

9.  Well-tempered MCMC simulations for population pharmacokinetic models.

Authors:  Frederic Y Bois; Nan-Hung Hsieh; Wang Gao; Weihsueh A Chiu; Brad Reisfeld
Journal:  J Pharmacokinet Pharmacodyn       Date:  2020-07-31       Impact factor: 2.745

10.  Characterizing the Effects of Race/Ethnicity on Acetaminophen Pharmacokinetics Using Physiologically Based Pharmacokinetic Modeling.

Authors:  Todd J Zurlinden; Brad Reisfeld
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-02       Impact factor: 2.441

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