1 Mar 2013
Ilya Shmulevich
Institute for Systems Biology
By considering living systems as information processing systems, we can formulate questions concerning "emergent" systems-level behaviors that include cellular decision making, maintenance of homeostasis and robustness, sensitivity to diverse yet specific types of information in the presence of environmental variability, and coordination of complex macroscopic behavior. I will discuss approaches rooted in algorithmic information theory for relating structure of complex systems to their dynamics. Elements of dynamical systems theory, such as phase transitions, interpreted through the lens of information dynamics can be used to study how living systems optimally bind past discriminations to future actions. We will also consider the information storage capacity embedded in the state space of complex dynamical systems and the conditions under which the system's memory is maximized. These approaches can be used to examine specific biological systems through new biological observables derived from experimental measurement data. I will also describe a framework based on time-frequency representations for analyzing the trade-offs between stability and responsiveness of nonlinear dynamical systems and discuss its application to several models of molecular networks.