Section: Overall Objectives
Overview
Bacteria provide fascinating examples of the survival strategies developed by single-cell organisms to respond to environmental stresses. The stress responses of bacteria are controlled by large and complex networks of molecular interactions that involve genes, mRNAs, proteins, small effector molecules, and metabolites. The study of bacterial stress response networks requires experimental tools for characterizing the interactions making up the networks and measuring the dynamics of cellular processes on the molecular level. In addition, when dealing with systems of this size and complexity, we need mathematical modeling and computer simulation to integrate available biological data, and understand and predict the dynamics of the system under various environmental and physiological conditions. The analysis of living systems through the combined application of experimental and computational methods has gathered momentum in recent years under the name of systems biology.
The first aim of the IBIS team is the unravelling of bacterial survival strategies through a systems-biology approach, making use of both models and experiments. In particular, we focus on the enterobacterium Escherichia coli , for which enormous amounts of genomic, genetic, biochemical and physiological data have been accumulated over the past decades. A better understanding of the survival strategies of E. coli in situations of nutritional stress is a necessary prerequisite for interfering with these strategies by specific perturbations or by even rewiring the underlying regulatory networks. This is the second and most ambitious aim of the project. It does not only spawn fundamental research on the control of living matter, but which may ultimately acquire medical relevance since E. coli serves as a model for many pathogenic bacteria.
The aims of IBIS raise four main challenges that generate new problems on the interface of (experimental) biology, applied mathematics, and computer science. In particular, the success of the project critically depends on (1) the modeling of large and complex bacterial regulatory networks, (2) the computer analysis and simulation of the network dynamics by means of these models, (3) high-precision and real-time measurements of gene expression to validate the models, and (4) the control and re-engineering of bacterial regulatory networks. While the first three items have been active research topics over the past few years, the control of regulatory networks is a novel challenge for IBIS that will be developed in the coming years.
The challenges of the research programme of the IBIS team require a wide range of competences on the interface of (experimental) biology, applied mathematics, and computer science. Since no single person can be expected to possess all of these competences, the international trend in systems biology is to join researchers from different disciplines into a single group. In line with this development, the IBIS team is a merger of an experimental biology group on the one hand, and a bioinformatics and biological modeling group on the other hand. In particular, the IBIS team is composed of members of the group of Hans Geiselmann at the Laboratoire Adaptation et Pathogénicité des Microorganismes of the Université Joseph Fourier (UJF, CNRS UMR 5163), and the network modeling and simulation group formerly part of the HELIX project-team at INRIA Grenoble - Rhône-Alpes, a group coordinated by Hidde de Jong. Both groups include researchers and technicians from other institutes, such as CNRS and the Université Pierre Mendès France (UPMF). The two groups have established a fruitful collaboration, which has resulted in more than 40 peer-reviewed publications in journals, conferences, and books since 2000.(See http://ibis.inrialpes.fr for a complete list.)
Hidde de Jong is the head of the IBIS team and Hans Geiselmann its co-director. The experimental biology component of IBIS also remains part of the Laboratoire Adaptation et Pathogénicité des Microorganismes, and Hans Geiselmann continues to represent this group in the interactions with the laboratory and university administration.
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