Research

Complex Biomolecular Networks

We are interested in the structure, dynamics, and information-processing capabilities of complex biomolecular networks. These networks can be interrogated using high-throughput measurement technologies (genomics, transcriptomics, proteomics, metabolomics, CHIP-chip, etc.) and computational network models can be built to explain and even predict the outcome of such experiments. These network models can be of many types, including genome-scale biochemical networks with known stoichiometries, statistical inference models, kinetic models, discrete dynamic networks, probabilistic networks and so forth. Predictive cell-scale models will enable synthetic biology and cellular engineering by allowing for the rationale design of cells as an engineering problem. These engineering designs can have applications to many important challenges including for bioenergy and medicine.

Systems Biology of Cancer

The systems approach to medicine derives from a simple idea -- the difference between normal and diseased cells lies in one or more disease-perturbed network. These disease-perturbed networks have altered patterns of expression in genes and proteins, and these altered patterns necessarily result in altered molecular fingerprints that can be measured. Cancer is an important and well-suited disease to study with a systems approach. We are interested in integrating high-throughput data to construct computational models of complex biomolecular processes in cancer, including brain cancer (glioblastoma), ovarian cancer, and gastro-intestinal cancers. For each of these cancers, our lab has ongoing collaborations with clinicians and cancer biologists. The aims of this research are to 1) identify molecular signatures for cancer diagnosis and treatment selection, and 2) generate predictive cancer network models that will link observed molecular signatures to underlying causal perturbations and identify therapeutic targets.

Model-guided Cellular Engineering

We are interested in using genome-scale computational models to guide modification of organisms to accomplish engineering goals. In particular, we will focus on reconstructing genome-scale metabolic and regulatory networks to guide engineering of micro-organisms to convert feedstocks to biofuels, as part of an emerging large-scale effort on campus in bioenergy research. To accomplish this, it is necessary to model how the cell will respond as an integrated system under given environments with novel perturbations so that our engineering objective (i.e. biofuel production) is aligned with the "objective" of the organism in its given environment (i.e. cell growth). Reliable predictive models hold the promise of significantly increasing our ability to rationally design and modify biological systems.