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. I will also discuss 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.
Ilya Shmulevich received his Ph.D. in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, in 1997. From 1997-1998, he was a postdoctoral researcher at the Nijmegen Institute for Cognition and Information at the University of Nijmegen and National Research Institute for Mathematics and Computer Science at the University of Amsterdam in The Netherlands, where he studied computational models of music perception and recognition. In 1998-2000, he worked as a senior researcher at the Tampere International Center for Signal Processing at the Signal Processing Laboratory in Tampere University of Technology, Tampere, Finland. From 2001-2005, he was an Assistant Professor at the Cancer Genomics Laboratory in the Department of Pathology at The University of Texas M. D. Anderson Cancer Center and an Adjunct Professor in the Department of Statistics in Rice University. Presently, he is a Professor at The Institute for Systems Biology, where he has directed a Genome Data Analysis Center as part of The Cancer Genome Atlas (TCGA) project. He is an Affiliate Professor in the Departments of Bioengineering and Electrical Engineering at the University of Washington, Department of Signal Processing in Tampere University of Technology, Finland, and Department of Electronic and Electrical Engineering in Strathclyde University, Glasgow, UK. His current main research area is multiscale modeling for cancer therapy.