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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