Speaker
Sara A. Solla
Description
Coupling large numbers of relatively simple elements often
results in networks with complex computational abilities.
Examples abound in biological systems - from genetic to
neural networks, from metabolic networks to immune
systems, from networks of proteins to networks of
economic and social agents. Recent and continuing
increases in the experimental ability to simultaneously track
the dynamics of many constituent elements within these
networks challenge the theorists to provide conceptual
frameworks and develop theoretical tools for the analysis of
such vast data. The subject poses great challenges, as the
systems of interest are noisy and the available information
is incomplete. The techniques and approaches of statistical
physics have proven remarkably useful, but need to be
further developed in their application to non equilibrium
dynamical systems. In this talk, I will review the currently
available theoretical tools, describe a few applications to the
analysis and characterization of biological networks, and
discuss the limitations of these techniques and some of the
directions along which novel approaches and
implementations are needed.
