[ The Types Forum (announcements only), http://lists.seas.upenn.edu/mailman/listinfo/types-announce ]
A POSTDOC position for 2 years, or a PhD position for 3 years, in computational biology at the Université de Lille 1, is available starting in September 2009. This project will held at the LIFL computer lab (http://www.lifl.fr) and the Interdisciplinary Research Institute (http://iri.ibl.fr) in Lille and within the bioComputing group (http://www.lifl.fr/BioComputing). Candidates should have a background in bioinformatics. Additional knowledge in computer science and/or biology such as modeling and simulation, gene regulation, semantics of programming languages (CAML, ...), compilation, lambda-calculus, pi-calculus, typing, logic, model checking will be appreciated. The proposed research project is summarized below. -- Cedric Lhoussaine LIFL, UMR 8022 CNRS, USTL Batiment M3, 59655 Villeneuve d'Ascq Cedex FRANCE Phone (IRI): +33 (0)3 62 53 17 09 Phone (Lifl): +33 (0)3 28 77 85 70 Fax: +33 (0)3 62 53 17 90 Email: cedric.lhoussa...@lifl.fr Web: www.lifl.fr/~lhoussai TITLE A Uniform Approach for Stochastic Modeling with Spatial Aspects in Systems Biology SUMMARY Experimental techniques in biological research are increasingly sophisticated, and allow to accumulate increasingly sharp knowledge. However, the understanding and the representation of cells and living matter remain fragmented, and do not report all interactions and interdependencies between biological mechanisms. Systems Biology tries to overcome this problem. Its long-term objective is to provide the theoretical and practical tools for describing, studying and predicting the behavior of biological systems. To this end, it is necessary to integrate into formal models the increasing and various experimental data available until now. This formalization subsequently allows the simulation and the qualitative analysis of models, and thus the observation and the in-silico study of the properties of biological systems. The predicted behaviors may themselves be validated experimentally, if necessary. Much effort within Systems Biology concentrates on gene regulatory networks. Essential genomic functions are strongly influenced by low number of biological actors, causing stochastic phenomena. The knowledge in this domain remains widely insufficient despite of novel experimental techniques, that allow to collect data from single cells. For example, recent work suggested that the non-uniform spatial distributions of elements in eukaryotic nucleus plays an important role in gene regulation. Today, it is impossible to unravel the genomic functions of space solely by experimental methods. It is thus necessary to develop modeling tools and simulation to study such systems in silico. An established computational approach for the modeling of gene networks consists in considering the cell as an executable entity, that is a system of concurrent agents with programmable interactions. Thus, a model is a concurrent program and simulating the model means to execute the program. This approach was initiated by A. Regev and E. Shapiro within the π-calculus. It was followed by many others, using process algebras extended with stochastic control. Such approaches benefit from solid theoretical foundations and formal, unambiguous, semantics that permit the study and the proof of properties of systems. However, the pi-calculus approach remains difficult to use for biologists who are not familiar with object-centered programming, that is in which the interaction capabilities are described for every agent independently of other. That is why we are currently focusing on a more intuitive alternative which preserves the expressiveness of concurrent and stochastic control. This alternative, commonly called ruled-base programming, is based on the rewriting of multi-sets of parametrized terms. It remains intuitively close to chemical reactions which are familiar biologists. In this project, we propose to investigate the design and implementation a novel stochastic modeling language. Its goal is to allow to address any type of gene network with a concurrent control, that depends on the position of biological actors. The way positions are described should remain independent of any type of spatial data. We hope to end with a unified framework, accessible to biologists, which extends on existing rule-based approaches while proposing compartments (with variable volume). The existing checking and prediction methods will be extended to this new language. We shall apply our language to specific case studies of cellular biology with spatial aspects in eukaryotic gene regulation: position of chromosomes in the nucleus, appearance and preservation of compartments, cross-talk between chromosomes, etc.