The robustness of biological systems is thought to arise from the interaction among its constituents, proteins, DNA, RNA and small molecules, which form an intricate web of molecules. Because of the recent progress in biological research and computation, the long-standing efforts to get a global perspective on the cellular phenomena seem to be witnessing a turning point. However, the task of elucidating design principles has been hampered by formidable amount of information even in simple model organisms, such as E. coli and yeast. The project seeks a fruitful insight from the massive analyses of typical cellular networks of protein-protein interaction and metabolism, and initiates novel statistical analyses originally developed by the same research group in the more general context of complex networks. A preliminary observation is that the correlations in node connectivity of various cellular networks are very unique compared to those seen in other non-biological systems, which reacts the inherent organizing principles of functional network formation. Fractal scaling and topological self-similarity in biological networks will provide yet unprecedented perspective on our view of biological complexity. When those networks are observed with varying scales, they consistently show the self-replicating pattern of fractal with finite fractal dimensions, which has a direct implication on the structural stability and growth mechanism of the network. In particular, the potential relevance of the scale transformation to the evolutionary process now starts to be appreciated, where the evolutionary pathways mimic the growth of the network. However, it still remains unclear how the emerging topological properties of biological networks was achieved in the long history of evolution and how it is related with the error-tolerance level of the network, on which the research will be concentrated.
The intellectual merit of the project stems from the generic quantitative methodology based on the statistical analysis of self-similar structures widely occurring in Nature. Moreover the novel scheme of fractality in biological networks will find direct applications in the newly rising interdisciplinary fields at the interfaces of biology, sociology, and computer sciences. At the same time, once enough evidence supporting the new scheme has been accumulated, it will provide a unique tool for assessing the fidelity of existing network data.
Broader Impacts: The main impact of the project includes the curriculum development and involvement of underrepresented minority students in science. The project will develop of an interdisciplinary course on complex networks in biology and other disciplines which will be tightly integrated with the research plan.
