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<< Tuesday, April 16, 2013 >>


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Inferring Network Architecture in Biological Systems: Understanding Temporal Processes that Control Immune Cell Function

Seminar: Control | April 16 | 2-3 p.m. | 380 Soda Hall


Nir Yosef, Broad Institute at Harvard Medical School

Electrical Engineering and Computer Sciences (EECS)


Complex, interacting systems are omnipresent in the world - from our social networks to the molecular circuits that guide the behavior of our cells. Charting out the connectivity of such systems and understanding their function has proven an extremely important, yet often daunting task. In this talk I will describe my work on modeling networks of molecules that control complex temporal processes in the immune system and explain how we used the resulting models to gain insight into biological organizational principles and to identify key regulators of autoimmunity.

Our modeling strategy relies on the integration of genome-scale datasets of various types, including temporal activity of individual components, probabilities for physical interactions between components, and causal relationships (i.e., perturbing component “A” affects the activity of “B”). The model inference is done iteratively such that putative key regulators are automatically chosen for perturbation experiments, which then serve for validating, refining and annotating the model. Formulating the ensuing data as network structures allows us to use graph-theoretic optimization algorithms as a modeling tool. For instance, we have developed an algorithm that combines a global optimization objective (directed Steiner trees) with a local one (shortest paths), proven its approximation bounds, and adapted its implementation for handling large-scale and real-world biological data.

Using this strategy, we investigated the differentiation of naïve T cells into autoimmune-inducing Th17 T helper cells, which, despite enormous clinical importance, remain poorly understood. We computationally derived and then experimentally validated a temporal model of the regulatory network that controls the differentiation process. The network consists of two self-reinforcing, but mutually antagonistic, modules that control the Th17 phenotype and collectively achieve appropriate balance between Th17 cells and other competing T cell lineages. Specifically, we identified several molecular circuits that are crucial to blocking the pathogenesis of Th17-dependent autoimmunity by shifting the balance toward the differentiation of immunosuppressive T cells. Overall, our study identified 41 regulatory molecules that affect Th17 differentiation, characterized their interactions, and highlighted novel drug targets for a host of autoimmune diseases.


bennettagnew@eecs.berkeley.edu, 510-642-7699