Discovering Genetic Network Interactions Through Iterative Hypothesis Reduction

Document Type

Presentation Abstract

Presentation Date

3-7-2022

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 across complex combinations of edges. The global network dynamics then can be used to revise the import actors and interactions in the gene regulatory network. 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.

Additional Details

March 7, 2022 at 3:00 p.m. Math 103 & Zoom

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