I work as an Associate Professor and Canada Research Chair in Learning and Simulation for Theoretical Biology at the School of Computer Science of the University of Windsor. I am also cross-appointed by the Biology Department and the Great Lakes Institute for Environmental Research at the University of Windsor. I was senior scientist, from 2000 to 2004, in the Swiss Institute of Bioinformatics, Geneva Switzerland after being post-doctorate from 1998 to 2000 in the same institute and lecturer, in 1998, at the University of Rennes, France. I received my B.Sc. and my M.Sc. in computer science at the University of Rennes. I completed my Ph.D. in computer science applied to bioinformatics at INRIA of Rennes in 1997, and obtained my Habilitation a Diriger la Recherche in 2004 in the University of Rennes. From 2000 to 2002 I was also consultant for GeneProt Inc. concerning the automation of protein identification and characterization process.

I have been funded by NSERC, SSHRC, GeneProt Inc. (Switzerland), CNRS (France), INRIA (France). I also received CFI and ORF infrastructure grants. To date, I have graduated 3 PhD and 12 MSc trainees and I currently supervise  6 PhD, 2 MSc and 4 undergraduate students. I have also supervised 2 Postdoctoral fellows and 7 graduate research assistants.

My domains of research are: artificial life, theoretical biology, ecosystem simulation, predator-prey model, bioinformatics, combinatorial optimization, machine learning.

• Khater M., Murariu D., Gras R., Contemporary Evolution and Genetic Change of Prey as a Response to Predator Removal, Ecological Informatics, in press.
  

• Mashayekhi M., MacPherson B., Gras R., A machine learning approach to investigate the reasons behind species extinction, Ecological Informatics, in press.

• Our dedicated resources allocation on SHARCNET, 225 processors and 81TB, has been renewed for 2014. It corresponds to a $83600 grant.
  

• Mashayekhi M., Hosseini Sedehi M., Gras R., Can We Predict Speciation and Species Extinction Using an Individual-Based Ecosystem Simulation?, ICAI'13 - International Conference on Artificial Intelligence, 2013, 301-307.

• Golestani A., Gras R., A New Species Abundance Distribution Model Based on Model Combination, International Journal of Biostatistics, 2013, in press.
  

• Golestani A., Gras R., Identifying Origin of Self-Similarity in EcoSim, an Individual-Based Ecosystem Simulation, Using Wavelet-based Multifractal Analysis,  Proceedings of the World Congress on Engineering and Computer Science 2012 (WCECS 2012), San Francisco, 1275-1282.

• Golestani A., Gras R., Using Machine Learning Techniques for Identifying Important Characteristics to Predict Changes in Species Richness in EcoSim, an Individual-Based Ecosystem Simulation, Proceedings of the World Congress on Engineering and Computer Science 2012 (WCECS 2012), San Francisco, 465-470.

I study the evolutionary process and the emergence of species in an artificial life simulated ecosystem. I have conceived an individual-based evolving predator-prey ecosystem simulation called EcoSim. The agents evaluate their environment (e.g., distance to predator/prey, distance to potential breeding partner, distance to food, energy level), their internal states (e.g., fear, hunger, curiosity) and choose among several possible actions such as evasion, eating or breeding. The behavioral model of each individual is unique and is the outcome of the evolution process. One major and unique contribution of this simulation is that it combines a behavioral, an evolutionary and a speciation mechanism. This is the only simulation modeling the fact that individual behaviors affect evolution and speciation. This approach allows interesting studies on theoretical ecology and artificial life in collaboration with biologists. For example, this approach is used to study the species abundance distribution, patterns and rates of speciation, the evolution of sexual and asexual populations, the interaction and diffusion of an invasive species or a disease in an existing ecosystem, etc.

Several videos of the simulation are available here. A very long run of the simulation is analyzed here weekly.

Most of the biological processes involve a dynamic system of interacting components. In general, the network of interactions between these components is partially or completely unknown. As the number of components involves is very large and the complexity of the network is very high, no exact analysis methods can provide a result in a reasonable time. I work on heuristics approaches based on the building of probabilistic models of the data and simulation of dynamic interacting systems to provide good approximations of the underlying studied processes’ model. This is particularly important to be able to understand the new data coming from system biology (gene expression data and proteomics) and from clinical measurement.