Our research aims to better understand how living systems respond to chemical agents. A key aspect of our approach involves using computational frameworks that are powered by formal (i.e. machine understandable) semantics to make effectively use of vast and diverse amounts of biomedical knowledge. We are particularly interested in understanding how the response to chemical exposure is modulated by genetic and physiological variation among individuals and how this translates into altered capabilities at the molecular level.
From the life sciences perspective, we are interested in:
From the computer science perspective, we are interested in the following approaches
- the molecular basis of an altered response due to genetic variation
- how binding of small molecules affects macromolecular structure and function
- the extent to which metabolic products contribute to overall toxicity
- identifying chemicals that could be the basis for the treatment of disease
- the extent to which cell structure and organization has been optimized for cellular activity
- the discovery and on-demand use of biomedical data and services
- the formulation, discovery and evaluation of scientific hypotheses
- the simulation of biological systems at the level of individual molecules
Our research has significant implications for basic science, drug discovery, and health care. We expect our work on formal representation and reasoning will improve the quality of biomedical data, enable the optimal use of bioinformatic services, and increase the overall pace of biomedical knowledge discovery. Our investigations into the dynamics of biochemical transformations will lead to improved identification of drug leads, thereby reducing the time and cost of drug discovery. Finally, by linking research with clinical care, we expect better treatment options and outcomes for individual patients, thereby increasing the efficacy of treatment, spare those that would suffer side-effects, and shorten the drug development cycle by having the regulatory agencies approve treatments effective for a sub-population.