Information about the environment, which organisms collect by membrane receptors, is processed by a complex network of signaling reactions to generate appropriate responses in terms of gene expression, development and differentiation, motility, cell growth and division, and programmed cell death. To survive and exist in harmony with its environment, the cell has to arrive at responses that are robust, specific and consistent with its role in a cell ensemble. The information processing system is replete with nonlinear interactions, which create bistable switches, signal relaying, adaptation, limit cycle oscillations, and other exotic responses. The purpose of this workshop is to survey recent advances in our understanding of the signal-response characteristics of living cells, and to foster deeper and more fruitful collaborations between theorists and experimentalists.
The application of sophisticated methods of biochemistry and molecular genetics in a variety of experimentally convenient organisms - budding yeast (Saccharomyces), fruit flies (Drosophila), green plants (Arabidopsis), nematodes (Caenorhabditis), and mammals (mice and men) - have provided many clues about the molecular mechanisms underlying signal processing and response regulation. Experimental studies of in vitro chemical and biochemical reaction networks have shown surprisingly similar dynamic behaviors, such as excitability, oscillations, multiple steady states, and signal propagation. During recent years mathematical models, based on realistic biochemistry and biophysics, have delivered useful insights into the dynamical principles underlying information processing by switches and clocks in living organisms. In addition, theoretical models have drawn attention to unexpected properties, such as hysteresis and critical slowing-down, which can be tested in the laboratory.
Mathematical analysis of large-scale transcriptome and interactome maps use graph theory, discrete mathematics, dynamical systems and signal processing theory, and elements of statistical mechanics. Modeling the dynamics of gene-protein regulatory networks involves all the tools from nonlinear dynamical systems theory: bifurcations of vector fields, numerical simulation, parameter estimation, hybrid systems (continuous-discrete, and deterministic-stochastic), sensitivity analysis, robust design, and multi-scale modeling. In most situations, new approaches are needed to adapt tools developed for engineering applications (such as control theory) to life science problems. Of crucial importance are algorithms and software to enable modelers to build larger, more complex and realistic models of information processing in cells.