Models for Medicine
Cellular behavior emerges from a complex network of chemical interactions, the details of which remain largely unknown. Developing effective therapies requires a quantitative understanding of these fundamental processes and how they can be safely manipulated to thwart disease. Our lab focuses on constructing mathematical models to assist in diagnosis and treatment of diseases such as cancer and Huntington's Disease.
Quantitative Systems Pharmacology
The single-target view of drug discovery is yielding to a systems-based view in which drugs are used in combinations to alter the behavior of specific and multiple pathways. This approach, called Quantitative Systems Pharmacology (QSP) requires the seamless integration of computational modeling and experiments. Among the computational challenges of QSP are identifying pathways relevant to disease and outlining the mechanistic details of those pathways. Our lab is developing methods for inferring disease pathways from screening data, and is constructing mechanistic models of pathways that are central to the progression of cancer and other diseases. We are developing methods to predict the outcomes of phenotypic screens, including how cells will respond to combinations of two or more drugs.
Interpreting cellular heterogeneity
The cells within a tissue are not all identical, but exhibit a variety of behaviors and characteristics that depend on their local environments. Similarly, seemingly identical cells within a clonal population may express a range of phenotypes in response to external stimulation. This cellular heterogeneity poses a major obstacle to developing effective personalized therapies. In cancer, for example, slight differences determine whether or not a cell will go on to produce a tumor. Our lab investigates the origins of non-genetic cellular heterogeneity and its potential for improving diagnosis of cancer. As part of the University of Pittsburgh Drug Discovery Institute, we are forging new methods for lead development based on high content analysis (HCA). We are refining methods for automatically quantifying heterogeneity and extracting useful biological information from analysis of cellular phenotype distributions. Working in collaboration with a team at GE, we are developing new computational methods to exploit distributions of cellular phenotypes and their spatial organization to assist pathology.