Computational methods based on mathematical and statistical approaches are now permeating all areas of modern pharmacology. Driving this explosion in biological computation are new high-throughput techniques in genomics and proteomics, novel large-scale screening methods for drug discovery and advances in single cell approaches. Each of these areas generates complex data sets that require computational approaches to analyze and interpret. A major challenge facing pharmacologists is to integrate these diverse data sets to achieve a systems-level understanding of human disease. To accomplish this goal, computational pharmacologists are developing tools to:

  1. analyze large genomic and proteomic data sets;
  2. design novel molecular tools to probe and perturb cellular function;
  3. perform quantitative image analysis and 4) develop predictive models of complex cellular processes