Advances in tools to probe biological phenomena such as combinatorial chemistry, high-throughput screening, genomics and proteomics have, in part, resulted in a rapid rise in the rate at which information is collected. The corresponding increase in the volume of information supplies a rich source for understanding how biological systems operate, but appropriate methods for placing each new piece of information into a larger context must be developed. Certainly mathematics have been applied to the investigation of biological systems in the past, and further opportunities arise from the need to organize and understand vast amounts of information, and to, furthermore, systematically, quantitatively capture behavior for predictive engineering.
This presentation will focus on how mathematics is used as a data analysis and predictive engineering tool to understand biological processes (i.e. life!), including a general introduction to the emerging discipline of "systems biology." Doctoral research conducted at Massachusetts Institute of Technology will be used for illustration along with examples from current research conducted in 3M Pharmaceuticals.
Senior Research Engineer
Software, Electric & Mechanical Systems Technology Center/Pharmaceuticals
3M Research and Development
Ph.D. Chemical Engineering, 2001
Massachusetts Institute of Technology, Cambridge, MA
Thesis advisor: Douglas A. Lauffenburger
B.S. Chemical Engineering, 1996
University of Illinois, Urbana-Champaign, IL
DeWitt, Ann E., T. Iida, H. Lam, V. Hill, H.S. Wiley, D.A. Lauffenburger. Affinity Regulates Spatial Range of EGF Receptor Autocrine Ligand Binding. Developmental Biology, 2002, v250; pp. 305-316.
DeWitt, Ann E., H. S. Wiley, D. A. Lauffenburger. Quantitative Analysis of the EGF Receptor Autocrine System Reveals Cryptic Regulation of Cell Response by Ligand Capture. Journal of Cell Science, June 2001, v114; pp. 2301-13.