Campuses:

Using Qualitative Models of Network Dynamics to Study Cancer and Terrorism

Thursday, March 1, 2012 - 11:00am - 11:15am
Keller 3-180
Derek Ruths (McGill University)
Despite domain differences, cancer researchers and insurgency analysts share real-world constraints in common: (1) the systems they seek to understand elude accurate and complete measurement and (2) the mechanisms that influence cancer and insurgent behavior are not well-understood. In spite of these challenges, both communities must devise strategies for containing, managing, and disrupting their respective systems.
Models of biochemical networks are constructed from and trained on experimental data. Because the quality of the experimental data used can significantly influence the quality the model obtained, practical issues such as equipment measurement limitations, incompletely characterized off-target effects of reagents, as well as the inherent variability of living systems can hurt efforts to build accurate models. In response to this fact, we have developed methods for building mathematical and computational models of cellular network dynamics by using the trends, rather than the exact measurements contained within experimental results as training data. Our methods can use sparse and noisy data to build models with 85% accuracy, beating all other known methods.
Models of the effect of counter-insurgency operations on violent non-state actors are often modeled as causal networks in which the variables take on qualitative values (e.g., high, strong, and not present). We have had success in applying our methods developed for biochemical networks to building predictive models of the effect of counter-insurgency strategies.
In this presentation, we will discuss the qualitative modeling methods we developed in both the biological and social contexts. We will show how they have been successfully applied to predicting the effect of perturbations to cancer cell signaling networks as well as to assessing how different counter-insurgency strategies may affect the long-term stability of Afghanistan.