Persons in charge:
The main objective of this research axis is to foster the combination of quantitative and modeling approaches to address the problem of biological complexity. Unraveling the complexity of living systems from cells to animal societies, is a challenge that requires a multi-disciplinary collaboration between fields that are still compartmentalized as regards developing new theoretical tools, data analysis and modeling techniques. It is at the interface between biology with physics and computer science that the fundamental principles governing living matter are likely to be discovered. Collaborative efforts are required between biologists, computer scientists and statisticians for the manipulation, the representation and the analysis of big data sets, and with statistical physicists and mathematicians for the development of predictive models of complex biological phenomena.
The Computational and Systems Biology Axis is intended to play a key role (1) to promote collaborations between experimentalists and theoreticians, (2) to develop the quantitative and modeling approaches within the CBI and (3) to define the recruitment policy of new research groups at the frontier between non-linear and statistical physics, computational sciences and biology thus bringing new skills and competencies to the CBI.
The most innovative approach of this axis is the identification of common questions at various scales of biological organization. This provides a unique opportunity for researchers coming from different biological areas (molecular, cellular, developmental and behavioral biology) to exchange analytical and theoretical approaches, and stimulates new research directions. Three main research themes have been identified: (1) collective motion, (2) large-scale structure and dynamics of interaction networks, and (3) morphogenesis and self-assembling. All these research themes require specific data analysis and modeling techniques.