Different kinds of ‘omics’ data for several organisms and bio-molecular interaction
networks (e.g. reconstructed networks of biochemical reactions and protein-protein
physical interactions) are becoming very common nowadays.
These bio-molecular networks are being used as a platform to integrate genome-scale
‘omics’ datasets. Identification of sub-networks in these large networks that show
maximum collective response to a perturbation is one the interesting problems to solve
by using an integrative analysis.
Sub-networks can be hypothesized to represent significant collective biological activity
due to the underlying interactions between the bio-molecules. The biological activity
can be estimated in several ways- for example coordinated change in the expression
level (e.g. mRNA). Identifying these regions reduce complexity of the network to be
analyzed in greater detail by revealing the regions that are perturbed by a conditionremoving
the interactions that are potentially false-positive and not related to the
response under study.
As the simulated annealing does not guarantee to find the global optimum and may
lead to an incomplete picture of the biological phenomenon, we report a method to
estimate the theoretical optimal score curve.
The simulated annealing algorithm (SA) used in this study is a slightly modified
version of the algorithm by Ideker et al.. Each node in the graph is associated with
a binary variable turning the node visible or invisible and therefore inducing several
sub-graphs. In the standard formulation, the initial solution is obtained by randomly
attributing 0 or 1 to the nodes of the graph. Based in concepts described above, we
propose an alternative initialization method to improve the performance of the
simulated annealing algorithm.Systems Biology as a Driver for Industrial Biotechnology (SYSINBIO