Dr. Bree Cummins: "Discovering Genetic Network Interactions Through Iterative Hypothesis Reduction"
- Thursday, September 23, 2021 from 3:00pm to 4:00pm
- Wilson Hall, 1-143 - view map
Abstract: Time series transcriptomics and proteomics data typically record expression levels of thousands of gene products. Discovering the important elements of these data for a specific experimental question is daunting given the combinatorial nature of the problem. Myself and my collaborators take the approach that a sequential set of software tools can reduce hypothesis space tremendously. I will discuss the performance of a set of tools that aims to discover “core oscillators” or clock-like genetic networks that control highly stereotyped cellular phenomena such as the cell cycle and the circadian rhythm. We first reduce the space of potential gene products from thousands to tens, then the space of possible interactions from hundreds to tens, and then we refine this collection of interactions by considering global network dynamics and reducing network space from a factorial down to tens or hundreds again. The first two steps are exhaustive but the last depends on local sampling around an initial guess. We show that this set of software tools is in principle capable of finding core oscillator interactions from high-dimensional data, although sometimes the results are surprising and hard to quantify.
- Department of Mathematical Sciences