|Ph.D.,||Rensselaer Polytechnic Institute,||(2017)|
|B.S.,||Texas A&M University,||(2013)|
University of Texas, Austin
Modeling of biological pathways aims to summarize key experimental findings and can be used for hypotheses formulation by predicting potential future experimental results, effectively supporting experimental efforts. Whereas biological experiments can provide evidence for a specific mechanism, models of disease mechanisms tend to be complex, with many interconnections, and can provide insight into interactions between the many components of system. Biological pathways, which include signaling and metabolic pathways, are of particular interest. However, these models typically require hundreds or thousands of data points to construct and are still fraught with uncertainty. Thus, my work focuses on techniques that can alleviate these challenges by using data-driven models when limited data are available and, as an alternate approach, automatically extract previous models and data from texts when a lot of data are available in unstructured form, such as papers in the open literature.