Meeting Abstract

S6.2-1  Sunday, Jan. 5 10:30  Computational analysis of signaling data elucidates dynamic regulatory networks that govern complex cellular responses. BAGHERI, N; Northwestern University n-bagheri@northwestern.edu

A unique and evasive property of biological systems relates to their ability to move between multiple stable states, confer resilient responses, and respond consistently to dynamic inputs/stimuli despite an ever-changing, noisy environment. Such robust cellular responses are often attributed to complex underlying regulatory mechanisms that remain largely undefined.

In the past decade, emerging technologies have offered increasingly high throughput data with greater resolution to investigate cellular responses. To gain insight from dynamic gene expression, transcription factor activity, phospho-signaling or other data, improved computational strategies to analyze, integrate, and predict complex biological function must be developed. We employ a variety of inference and modeling algorithms to investigate the temporal and multifunctional evolution of various cellular responses. Specifically, we use perturbation data of cancer and stem cell models to gain insight on the signaling mechanisms that control highly integrated functions. By developing predictive models that are informed by novel experimental approaches—namely, microwestern arrays and transcription factor arrays—we are able to resolve the regulatory pathways responsible for complex biological response and cell fate decisions. We use such systems-level approaches to investigate how short-term transcription factor and phosphorylation dynamics govern cell phenotype. In this manner, we can generate informed hypotheses on the mechanism of action of potential drug candidates and gain insight for improved efficacy/specificity of treatment strategies, providing a unique opportunity to predict and modulate biological responses.