ゲノム情報科学研究教育機構  アブストラクト
Date June 14, 2011
Speaker Assoc. Prof. Jose Nacher, Future University-Hakodate
Title Discoveries from non-randomness in complex cellular networks and systems
Abstract In the last years, network ideas have become a key approach to analyzing and understanding complex biological systems. In sharp contrast to the predictions from pure graph theory, modern network theory has shown that real cellular networks exhibits a striking non-randomness reflecting the existence of specific system-based dynamical rules that guides its evolution. Here, we describe several representative studies on cellular networks and systems where the underlying non-randomness together with network analytical tools leads to provide biological insights. First, we consider the protein-domain system and present a general methodology to investigate the network structure of bipartite graphs using mathematical modeling. A closer insight into the data analysis shows that the number of total protein domains and the number of domain families in a protein are governed by different statistical laws. We then develop a methodology and propose an evolutionary dynamics model, based on rate equation formalism, and consisting on various processes that demonstrates that these distinct distributions are in fact rooted in the internal domain duplication mechanism. On the other hand, life molecules in a cell are connected and organized into modules and communities. The problem turns to be more complicated when a bipartite network structure is involved. Connections between protein complexes and key diseases have been suggested for decades. However, it was not until recently that protein complexes were identified and classified in sufficient amounts to carry out a large-scale analysis of the human protein complex - disease system. We constructed the first systematic and comprehensive set of relationships between protein complexes, target drugs and related diseases. The network structure is characterized by a high modularity, both in the bipartite graph and in its projections. To unravel the relationships between such modules and diseases, we investigated in depth the origins of this modular structure in examples of particular diseases. This analysis unveils new associations between diseases and protein complexes and highlights the potential role of polypharmacological drugs, which target multiple cellular functions to combat complex diseases. Finally, emergent principles of gene expression dynamics are presented together with some mathematical models that lead to elucidate gene expression systemic features.
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