Biological processes are tightly controlled by a conserved and limited collection of signaling networks (Figure 1). Specificity and robustness (to perturbations) is imparted by an interplay of the spatiotemporal properties and the underlying network architecture. At the same time, it is not surprising that certain variations can overcome robustness and specificity, leading to abnormal outcomes, i.e. diseases. A series of fundamental questions ensue: what features of the network architecture impart robustness to biological processes? Are there process-specific ‘ideal’ parameters of signaling network? How do perturbations affect the natural parameters and the underlying network architecture leading to problems? Along the same lines, could some biological processes be relatively immune to perturbations? Answers to such questions are essential for acquiring a holistic understanding of biological processes which have direct relevance to human health. Since many such behaviors may rely on subtle effects and/or rare events, a thorough quantitative analyses is needed.
I am trying to answer some of these fundamental questions by developing a range of quantitative experimental and computational frameworks.