Research in Complex
networks at Bose Institute is focussed in two directions: (i) the study
networks in different biological systems and contexts, and, (ii)
metrics and exploiting them
meaningfully in a given system, ...), which have important consequences
not just for biological networks but for all
networked systems in general.
Drawing on inputs from
various experimental labs,
we construct (using experimental data, results from text-mining, ...)
analyse networks in different biological systems (microbes, plants, ...), with a keen eye towards
identifying biologically important
One of the questions that
we specifically seek to answer is: (how) does
network topology encode biological phenotype?
phenotypes and their network architecture
We use an
approach to characterize metabolic networks of 32 microbial species
using 11 topological metrics from complex networks. Clustering allows
us to extract the indispensable, independent, and informative metrics.
Using hierarchical linear modeling, we identify relevant subgroups of
these metrics and establish that they associate with microbial
phenotypes surprisingly well. This work can serve as a stepping stone
to cataloging biologically relevant topological properties of networks
and toward better modeling of phenotypes. The methods we use can also
be applied to networks from other disciplines.
space by the graphlet growth model
(orange), Barabasi-Albert model (blue) and 113 real-world networks
Modeling and verifying a broad array of
network properties: graphlet growth
Almost all papers in network theory focus on only one
network metrics at a time. Very natural questions arise: (a) why
shouldn't all network metrics be studied
together instead of studying just one or two, (b) why should'nt higher
moments of metrics be studied, (c) how to identify, which of these
metrics are most
informative (or redundant), in any given situation? And finally, given
information, what systemic or emergent
properties of the system(s) under study can we extract.
Motivated by the observation that network growth often
takes place via
addition of graphlets rather than single nodes (modules in
biology and software, families in social networks etc) , we attempt to
answer the above questions.