A system biology approach to robustness analysis of circadian rhythm

Summary form only given. Understanding regulation is a critical hurdle in unraveling complex biological systems. As gene-level architectures become known, the open challenge is to assign predictable behavior to a known structure, the so-called "genotype-to-phenotype" problem. In response to this challenge, the discipline of systems biology has emerged with an integrative perspective towards determining complex systems behavior. A property of particular interest is the robustness of the biophysical network: the ability to maintain some target level of behavior or performance in the presence of uncertainty and/or perturbations. In biological systems, these disturbances can be environmental (heat, pH, etc.) or intrinsic to the organism (changes in kinetic parameters). While preliminary results are available for simple (low-dimensional, deterministic) biological systems, general tools for analyzing these tradeoffs are the subject of active research. The gene network which underlies circadian rhythms is an ideal system for robustness studies, owing to its remarkable performance in a highly uncertain environment. Of interest for control theoretic analyses, the dominant elements of the postulated architecture for Drosophila consist of nested negative autoregulatory feedback loops controlling the expression of timeless (tim) and period (per) interlocked with a positive feedback loop established via the dClock gene. Complex formation, regulated translocation and degradation of several of these gene products, which is additionally controlled (and delayed) by protein phosphorylation, add further levels of complexity to the system. In this talk, a number of quantitative tools from systems theory are presented as enabling methodologies for unraveling robust biological regulatory systems, with an emphasis on sensitivity analysis. Our work on modeling and analysis of the Drosophila circadian rhythm gene network are detailed, and generalizations are be drawn for the mammalian analog and for more general gene regulatory networks.