This research project focuses on the generic properties of complex networks, with the goal of elucidating the functional implications of different types of network architecture. The working hypothesis is that certain classes of Boolean networks illustrate principles of organization that underlie the structure of biological organisms. Such networks are known to exhibit fundamentally distinct regimes of behavior: ordered, chaotic, and critical. Do biological cells belong to one of these classes? Modern gene array techniques now permit the gathering of vast amounts of data on gene expression, providing a new experimental window on questions of this type. The proposed research aims to develop a useful theoretical framework for designing such experiments and interpreting their results.
Specific aims of the project include the determination of scaling laws for cascades of activity following perturbations in scale-free and modular Boolean networks, the characterization of sets of dynamical attractors, and transitions between them, associated with different network architectures, and the analysis of such networks when stochastic timing rules are employed for updating rather than the traditional synchronous updating rules. In all cases, attention will be focused on quantities that can be measured in biological systems, and currently available experimental data will be used to refine the classes of models to be studied.
The project is highly cross-disciplinary, requiring a collaboration between experts in dynamical systems theory and cell and developmental biology. Funds will be used to provide interdisciplinary training for an undergraduate student, a graduate student, and a postdoc in the burgeoning fields of functional genomics and bioinformatics, where analytical skills traditionally taught in physics contexts and principles of cell and molecular biology are equally important.
