The objective of this project is to understand evolutionary principles by which biological networks have acquired their modular architecture, robustness and preservation of diverse functions to sustain life. While much is known about the general rules of natural selection, an analysis of the large-scale properties of networks is necessary to reveal other relevant patterns that emerge through evolution. The difficulty in describing systems in biology, from protein-protein interaction networks to metabolic pathways, on a theoretical level has hampered the development of our understanding of biological networks. The goal of this project is to tackle the theme of emergent phenomena in biological complex networks with novel concepts from non-equilibrium systems and complexity, and to systematically transform these theoretical concepts into practically applicable models of functional modules. In order to achieve this goal, the PI will study functional modules (clusters of proteins cooperating towards achieving a given function) by clustering and renormalization methods. The intellectual merit of this project rests on the unique quantitative methodology, based on the self-similarity measure, developed to elucidate the evolutionary principles embodied at the level of networks of constituent molecules. This novel mathematical scheme will find wide applicability in the newly rising interdisciplinary fields at the interfaces of biology, sociology, and computer sciences.
The broader impacts of this project include curriculum development, involvement of underrepresented minority students, and international and interdisciplinary collaborations. Promoting the involvement of minority undergraduate and graduate students in research environments will be a priority of this program. The project will also bring together international leading groups in Complexity and Statistical Physics, Bioinformatics, and Biology.
