Jayajit Das

Battelle Center for Mathematical Medicine
The Research Institute at Nationwide Children's Hospital
The Ohio State University

Title: Dramatic reduction of dimensionality in large biochemical
networks due to strong pair correlations

Abstract: High-throughput experiments probing how cell signaling
proteins and genes vary over time to respond to changes in the local
environment can contain a very large (thousands or more) number
variables describing the kinetic proteins or genes involved in the
process. Is it possible that these variables are tightly correlated with
each other and the kinetics can be described by few super-variables that
can be constructed out of the original variables? We show using
in-silico simulations the answer is yes; surprisingly this result holds
for a wide variety of biological networks containing large variations in
network topology, non-linear interactions, and parameter values. I will
also illustrate how to extract biologically relevant insights such as
identifying significant time scales and groups of correlated chemical
species from our analysis.