PhD Defense: "A Modular Approach to Analyzing Biological Networks"

Hari Sivakumar

May 31st (Tuesday), 3:00pm
Harold Frank Hall (HFH), Rm 4164 (ECE Conference Rm)

Biological networks are inherently very complex, consisting of several entities reacting in a nonlinear fashion. While there has been a lot of study into the behavior of these discrete biological entities, rarely can biological function be attributed to a single molecular species alone. Therefore, there is a need for the recognition of functional components in biological network organization. These components are discrete entities whose biological function is separable from that of other components.

In this work, we address the decomposition of biochemical networks into functional modules that preserve their dynamic properties upon interconnection with other modules, which permits the inference of network behavior from the properties of its constituent modules. The modular decomposition method developed here also has the property that any changes in the parameters of a chemical reaction only affect the dynamics of a single module. To illustrate our results, we define and analyze a few key biological modules that arise in gene regulation, enzymatic networks, and signaling pathways, and show how modular decomposition is useful to predict network properties.

We then use this modular decomposition method to analyze the p53 network, which plays a key role in tumor suppression in many organisms. We study the evolution of the p53 core regulation network and conduct a formal analysis of the different network configurations that emerge in the evolutionary path to complexity from putative primordial organisms to modern-day vertebrates. We develop an algorithm to solve the system of equations that describe the network behavior by interconnecting the network modules systematically, as these equations are typically difficult to solve using standard numerical solvers. In the process, we qualitatively compare the distinct bifurcation behaviors that each network can exhibit. We demonstrate how our novel model for the core regulation network matches experimentally observed phenomena in human cells, and contrast this with the plausible behaviors that ancestral organisms can admit. Specifically, we show that the complexity of the p53 network in humans and evolved vertebrates permits a wide range of behaviors that can bring about distinct cell fate decisions in response to DNA damage, but that this is not the case for primordial organisms.

About Hari Sivakumar:

Hari Sivakumar received his B.S. in Electrical Engineering in 2009, and his M.S. in Electrical Engineering: Systems in 2010 from the University of Michigan Ann Arbor. Hari also received an M.S. in Electrical Engineering from the University of California Santa Barbara in 2014, where he has been working on projects in the area of the applications of Dynamical systems theory and Network science to analyzing biological networks. Hari is also a member of the Center for Control, Dynamical-systems and Computation (CCDC), and an Associate Fellow of the Network Science IGERT program.

Hosted by: Professor João Hespanha