Robin Gras

Canada Research Chair                                                                           
Associate Professor
School of Computer Science
Biology department
Great Lakes Institute for Environmental Research
University of Windsor
Windsor, Ontario Canada

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 7 PhD and 14 MSc trainees and I currently supervise  5 PhD and 1 MSc. I have also supervised 2 Postdoctoral fellows and 9 graduate research assistants.

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

I am a member of Bioinformatics @ Windsor


Can we predict the unpredictible? Our new method for long term prediction has been published in Scientific Reports. Interview "Predicting Seizures Amid the Chaos" in (12/17/2014), Interview "Predire le chaos" on Radio Canada in "Les samedis du monde" (29/11/2014), interview "New software predicts seizures 17 minutes before they happen" on Canada AM CTV (25/11/2014), interview "Predicting Epileptic Seizures" on CBC Radio (27/11/2014), interview on CBC TV (from 0:33:53, 27/11/2014), interview "Predire l'avenir" on Radio Canada Montreal in "Les Annees Lumieres" (02/11/2014), interview "Methode pour prevenir l'avenir" on Radio Canada Windsor (from 2:22:42) (27/10/2014), "Epileptic seizures can be predicted through researchers' software" in CBC News (25/11/27), "La crise d’épilepsie prédite avec 17 minutes d’avance" in Science et Avenir (12/12/2014) and "University researchers develop software to predict seizures" in Windsor Star (07/11/2014).

  • Mashayekhi M. Gras R., Rule Extraction from Random Forest: the RF+HC Methods, AI 2015, in press.
  • Our dedicated resources allocation on SHARCNET, 170 processors and 80TB, has been renewed for 2015. It corresponds to a $60000 grant.
  • Scott R., Gras R., MacIsaac H.J., Brown E.A., Cristescu M.E., Exploring the Balance between Type I and Type II Error in Clustering of Ribosomal DNA (18S) Sequences Using UPARSE, ASLO 2015, Aquatic Sciences Meeting, Granada, Spain, Feb 2015.
  • Golestani A., Gras R., Can we predict the unpredictable?, Scientific Reports, 4, 6834: DOI:10.1038/srep06834, 2014.
  • Mashayekhi M., MacPherson B., Gras R., Species-area relationship and a tentative interpretation of the function coefficients in an ecosystem simulation, Ecological Complexity, 19, 84-95, 2014.
  • Mashayekhi M., Golestani A., Majdabadi Farahani Y., Gras R., An enhanced artificial ecosystem: Investigating emergence of ecological niches, the Fourteenth International Conference on the Synthesis and Simulation of Living Systems - Artificial Life 14, 693-700.

  • Scott R., Gras R., Testing the Effects of Speciation and Mutation Rates on Distance-Based Phylogenetic Tree Construction Accuracy Using EcoSim, the Fourteenth International Conference on the Synthesis and Simulation of Living Systems - Artificial Life 14, 719-725.

Research Interests:

A run of the EcoSim simulation with an uniform distribution of grass. Grass is in green, predators are in white and a random color is associated to every prey species.

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.

These works are supported by the NSERC grant ORGPIN 341854, the CRC grant 950-2-3617 and the CFI grant 203617 and are made possible by the facilities of the Shared Hierarchical Academic Research Computing Network (SHARCNET) and Compute Canada.


University of Windsor
School of Computer Science
401 Sunset Avenue
Windsor, Ontario, N9b3P4 CANADA

Tel: +1 519 253 3000 ext. 2994

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