Sai Bavisetty, Ph.D.

About:

I am passionate about building mathematical models for biological systems, and I am currently developing computational tools to enhance diagnostic methods for improved patient outcomes. My work involves simulating and analyzing gene regulatory networks using topological techniques and advanced machine-learning algorithms.

Student Mentorship:

Advanced High School to Advanced Undergraduate students can learn about mentoring with Dr. Bavisetty here.

Current Projects:

Project: Quantifying Upstream-Downstream Interactions in a Boolean Network

Description: Boolean networks are simplified models used to represent gene regulatory networks, which control how genes turn on and off in a cell. These models are powerful tools for explaining complex biological behaviors with minimal parameters. However, as these networks grow larger, they can become computationally challenging to manage.

Recent research has shown that we can break down these large networks into smaller, more manageable modules by identifying strongly connected components within the network’s interaction diagram. This approach helps simplify the analysis but doesn’t account for how changes in one part of the network (upstream) affect another part (downstream).

Our project aims to relate the “strength” of the connections between the upstream and downstream components. In this context, strength refers to the number of connections and their canalizing power. Our ultimate goal is to understand how different aspects of these connections, such as canalizing power, affect the overall dynamics of the network.

Working with Dr. Matthew Wheeler and High School student mentees.

Project: Topological Analysis of Boolean Network Dynamics

Description: Boolean networks are simplified models used to represent gene regulatory networks, which control how genes turn on and off in a cell. These models are powerful tools for explaining complex biological behaviors with minimal parameters. However, as these networks grow larger, they can become computationally challenging to manage.

One important tool for analyzing large Boolean networks is the wiring diagram. This is a graph that shows how different genes and proteins interact within the network. In special cases, it has been shown that the topology of the wiring diagram contains useful clues about the dynamics of the Boolean network. For instance, loops in the wiring diagram have been shown to correspond to loops in the dynamics of the Boolean network.

Our project aims to explore various interesting topologies and understand the relationship between the topology of the interaction graph and the dynamics of the Boolean network. By understanding these topological effects, we can gain a better understanding of large Boolean networks that regulate processes inside the cell.

Working with Dr. Matthew Wheeler and High School student mentees.