The UTC Chair of Mathematical Modeling and Systems Biology for Predictive Toxicology, currently held by Frédéric Y. Bois, aims to develop:
- A cursus in bioinformatics, biomathematics and biostatistics at UTC (see Teaching)
- Research in systems biology, bioinformatics and related areas (Research)
- A dialogue between biologists, mathematicians and decision makers (Publications and Links)
Updates on our latest activities:
Integration of Omics Data and Systems Biology
Modeling: Effect of Cyclosporine A on the Nrf2 Pathway in Human Renal
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. arXiv:1312.4744
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.
We are recruiting a motivated post-doctoral researcher to develop hybrid (differential/boolean) stochastic models of epithelial barriers.
Our group focuses on predictive toxicology for therapeutic drugs or general chemicals. Experimentalists develop miniaturized in vitro exposure systems (bioartificial organs on microfluidic biochips) and generate omics data to characterize the state of those systems. A challenge is to develop the corresponding mechanistic toxicokinetic and toxicodynamic models and to calibrate them formally with such data, in a Bayesian framework.
See the attached pdf (English version)
L’Université de Technologie de Compiègne recrute un(e) chercheur contractuel(le) dans le cadre d’une mission post doctorale associée à la Chaire de Modélisation Mathématique et Biologie Systémique, à l'UMR CNRS 7338 du département de Génie Biologique.
Nos travaux portent sur la prédiction de la toxicité des molécules chimiques. Pour y parvenir, nous développons des systèmes d’exposition in vitro (organes bioartificiels au sein de biopuces microfluidiques, systèmes de barrières physiologiques), une modélisation de la toxicocinétique et des effets cellulaires in vivo et in vitro, évalués sur la base de méthodes omiques.
Le travail post-doctoral se focalisera sur le développement de modèles stochastiques hybrides (différentiels/booléens) décrivant la mise en place des barrières épithéliales. Ces modèles seront calibrés à l'aide des données obtenues dans le projet.
Voir le pdf joint (version française)
Brad Reisfeld and his Systems and Computational Biology Research Group
at Colorado State University have launchedDoseSim
, 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
(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).
A finely tuned balance between estrogens and androgens controls reproductive functions, and the last step of steroidogenesis plays a key role in maintaining that balance. Environmental toxicants are a serious health concern, and numerous studies have been devoted to studying the effects of endocrine disrupting chemicals (EDCs). The effects of EDCs on steroidogenic enzymes may influence steroid secretion and thus lead to reproductive toxicity. To predict hormonal balance disruption on the basis of data on aromatase activity and mRNA level modulation obtained in vitro
on granulosa cells, we developed a mathematical model for the last gonadal steps of the sex steroid synthesis pathway. The model can simulate the ovarian synthesis and secretion of estrone, estradiol, androstenedione, and testosterone, and their response to endocrine disruption. The model is able to predict ovarian sex steroid concentrations under normal estrous cycle in female rat, and ovarian estradiol concentrations in adult female rats exposed to atrazine, bisphenol A, metabolites of methoxychlor or vinclozolin, and letrozole.
The report of this work by Nadia Quignot
and Frederic Y. Bois is in press in PLOS ONE (http://dx.plos.org/10.1371/journal.pone.0053891
Two books chapters have seen the light:
Dorne J.L., Amzal P., Bois F., Crepet A., Tressou J., Verger P., 2012, Population effects and variability, in Computational Toxicology, Reisfeld B., Mayeno A.N. Eds., Methods in Molecular Biology Series, 9. Volume 929, Part 4, 521-581, Humana Press, New-York, doi: 10.1007/978-1-62703-050-2_20.
Bois F., Jamei M., 2012, Population-based pharmacokinetic modeling and simulation, in Encyclopedia of Drug Metabolism and Interactions, Lyubimov A.V., Ed., John Wiley & sons, Hoboken, vol. XI, p. 1-27.
"Addressing Human Variability in Next-Generation Human Health Risk Assessments of Environmental Chemicals" by Lauren Zeise, Frederic Y. Bois, Weihsueh A. Chiu, Dale Hattis, Ivan Rusyn and Kathryn Z. Guyton is scheduled to appear in Environmental Health Perspectives. The paper offers a critical review of the use of new data streams in health risk assessment.
See attached pdf