Joint CMU-Pitt Ph.D. Program in Computational Biology Seminar

Friday, December 1, 2017 - 11:00am to 12:30pm


ASA Conference Room 6115 Gates Hillman Centers


JEAN YANG, Assistant Professor

Understanding Cellular Signalling Pathways Through Causal Analysis of Rule-Based Models

In prediction and diagnosis, important questions include “when?” and “how?” For instance, in cellular signaling, we may want to understand the events that lead to apoptosis, both in terms of what events may trigger apoptosis, and the specific mechanism of the apoptosis. Typically, understanding these systems have involved translating intuitions about cause and mechanism into low-level models, and then matching the low-level models to experimental data.

My work focuses on a modeling approach that provides enough structure to allow for automated analysis of mechanism and cause. We have been using Kappa, a rule-based graph-rewrite language that supports the modeling of intracellular signaling as stochastic transformations over graphs of protein agents, where edges represent protein complexes. One of the main advantages of using Kappa is that we can leverage the structure of the rules, combined with a precise understanding of Kappa’s semantics, for interesting and useful analyses. In this talk, I will present our research involving causality between rules: given two rules r and s, does rule r need to trigger before rule s in order to reach the event of interest? I will introduce Kappa and present our formulation of causality, as well as our work on counterfactual reasoning for causal analysis. I will also present ongoing work on tools for causal analysis of Kappa programs.

Jean Yang is an Assistant Professor position in the Computer Science Department at Carnegie Mellon University. She received her AB from Harvard and PhD from MIT. Her research interests are in developing programming models and tools towards making provable guarantees ubiquitous. She works on language-based solutions for security and privacy and programming tools for modeling intracellular signaling.

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