Network Analysis: It has been observed that in most real-world networks the connection topology between individual entities is not completely random nor is governed by only nearest neighbour interactions but is somewhere in-between. That is, most of the connections are short-range, except for a few random long-range connections. It is interesting to understand the role of these few long range connections in the stability of the real-world networks, especially biological networks. Graph theory provides necessary tools to analyze these systems, called small-world networks. We are also interested in understanding the effect of a small change in the coupling topology of a distributed dynamical system on the characterization and control of dynamical behaviour of the system. We have extensively studied the characterization, synchronization and control of spatiotemporal dynamics with applications to physical, chemical and biological systems.
Pattern Recognition in Biological Sequences/Structures: The basic underlying assumption in the analysis of DNA and protein sequences is that a pattern conserved across the evolution may be functionally important. Some of the pattern recognition problems of interest to us are repeats in DNA and proteins (both at sequence and structure level), identifying structural domains, genomic islands, SNPs, and genes.
A brief description of the ongoing work is given below.

Graduates having a background in physics, mathematics or computer science would be well equipped to work in the area of computational biology and systems biology.

Sub Areas