'Analyses for Rule-Based Models of Cellular Signalling'

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• Dr Jean Yang, Assistant Professor,Department of Computer Science, Carnegie Mellon University, Pittsburgh USA
• Thursday 25 May 2017, 15:00-16:00
• Sackler Lecture Theatre (Level 7), Cambridge Institute for Medical Research.

If you have a question about this talk, please contact Mala Jayasundera.

SPECIAL COMPUTATIONAL BIOLOGY SEMINAR - Host by: Dr Ben Hall

Abstract: In prediction and diagnosis, important questions include “when?” and “how?” For instance, in cellular signalling, 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 signalling 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. My research group has been focused on analyses 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? In this talk, I will introduce Kappa, present an overview of our formulation of causality, and discuss our work on combining causal analysis with statistical model checking techniques. I will present ongoing work on tools for 1) relative frequency analysis for different pathways in a model and 2) finer-grained inhibition analysis.

Bio. 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. During her PhD she created a programming language, Jeeves, that factors information flow checks out of the rest of the program. Her paper on Verve, and operating system verified for type safety, received Best Paper Award at PLDI 2010 . Jean also works on analysis tools for modeling intracellular signalling using rule-based graph-rewrite programs.

This talk is part of the Cambridge Oncology Seminar Series series.

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