The UTC Chair of Mathematical Modeling and Systems Biology for Predictive Toxicology, currently held by Frédéric Y. Bois, aims to develop:
Updates on our latest activities:
On our software page you will now find the code and manual for Graph_Sampler, a tool for simulating structured random networks and for performing Bayesian inference on networks' structures.
We are recruiting a motivated post-doctoral researcher to further develop the steroidogenesis model we started working on with Nadia Quignot. See the attached pdf (English version and version française) for more information.
Integration of Omics Data and Systems Biology Modeling: Effect of Cyclosporine A on the Nrf2 Pathway in Human Renal Kidneys Cells
Jérémy Hamon, Paul Jennings, Frederic Y. Bois
In a recent paper, Wilmes et al. demonstrated a qualitative integration of omics data streams to gain a mechanistic understanding of cyclosporine A toxicity. One of their major conclusions was that cyclosporine A strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal proximal tubular epithelial cells exposed in vitro. We pursue here the analysis of those data with a quantitative integration of omics data with a differential equation model of the Nrf2 pathway. That was done in two steps: (i) Modeling the in vitro pharmacokinetics of cyclosporine A (exchange between cells, culture medium and vial walls) with a minimal distribution model. (ii) Modeling the time course of omics markers in response to cyclosporine A exposure at the cell level with a coupled PK-systems biology model. Posterior statistical distributions of the parameter values were obtained by Markov chain Monte Carlo sampling. Data were well simulated, and the known in vitro toxic effect EC50 was well matched by model predictions. The integration of in vitro pharmacokinetics and systems biology modeling gives us a quantitative insight into mechanisms of cyclosporine A oxidative-stress induction, and a way to predict such a stress for a variety of exposure conditions.
BMC Systems Biology
Probabilistic generation of random networks taking into account information on motifs occurrence
Frederic Y. Bois, Ghislaine Gayraud
Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli.
Thirty years ago, Richard Stallman published the announcement that would launch the free software movement.See the Web page of the Free Software Foundation and learn about the associated events.
Brad Reisfeld and his Systems and Computational Biology Research Group at Colorado State University have launched
DoseSim, a simulation software based on our brainchild, GNU MCSim. DoseSim has a nice interface and runs on PCs...
By the way, check also F. Bois' chapter on Bayesian Statistics in Brad Reisfeld's and Arthur Mayeno's two-volume book as part of the Humana Press “Methods in Molecular Biology” series entitled “Computational Toxicology”. It is now available from Springer and major bookstores, including amazon.com.
Andrew Gelman (Columbia University) will visit us for 6 months this year. We will work with us on his latest creature: Stan, and it will not be just cosmetics!
Version 5.5.0 of MCsim is available at http://ftp.gnu.org/gnu/mcsim/
See the official web page at http://www.gnu.org/software/mcsim/
The latest version of the model generator "mod" can now
generate C code directly usable with the R package deSolve. Simply use the "-R" option for that.
Version 5.5.0 also fixes a potential security problem at the installation phase (originating from automake).
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