Latino Studies at New York University

Andrew Matteson

Doctoral Candidate
Computational Biology Program
Courant Institute of Mathematical Sciences
New York University

April 23, 2013

Doctoral Dissertation Defense

Computational Methods in Model Merging with Applications to Pharmaceutical Development

Quantitative biochemical models of cell signaling have been successfully applied to develop insights into the nature of signaling regulation. These models have diverse applications to understanding developmental biology, normal cellular behavior, and disease states. They have been especially powerful in guiding the development of targeted oncology therapeutics.  Construction of biochemical models has several challenges. One in particular is parameter estimation. Since many parameters of biological models are not directly measurable, they must be estimated by some technique. The estimated parameters can exert large influences on a model's predictions, but uncertainties in the estimation process tend to give different estimates for the same parameter when it is estimated using two different sets of data.  The incomparability of parameters estimated on different sets of data is a significant barrier to the creation of certain kinds of models. In particular, a parameter that appears in two different signaling pathways must be resolved in order to make a single model of both pathways. Such models encompassing multiple interacting pathways would be useful, especially in understanding resistance that develops to targeted cancer therapeutics; however, estimating parameters of such multi-pathway models has been extremely challenging.  In this work, we introduce and discuss a heuristic used in conjunction with optimization algorithms to resolve these challenges. This heuristic is called "model merging." We perform an example driven exploration of the heuristic for two toy models, one in which a resolution of parameter estimates is possible and in the case where it is not. We find advantages in both cases relative to using optimization alone.  We then turn to biological applications. We use the heuristic to build two models of biological signaling, a cell signaling model of two interacting receptor signaling pathways, MET and EGFR, active in renal cancer, and a model of erythropoeitin signaling in normal and cancerous cells. In both models, parameters for which a resolution could not be found directly correspond to biological differences between pathways or cell types. This indicates that the heuristic is a powerful tool in identifying resolved models.