Program 
Abstract
Mathematical Models of Cellular Systems
 
Ted Kalbfleisch, Ph.D.
Kenneth S. Ramos, Ph.D.
Grzegorz (“Greg”) Rempala, Ph.D. D.Sc.
University of Louisville
 

Gene expression as well as cellular reactions involving limiting protein concentrations are intrinsically stochastic. In biology, this is referred to as intrinsic noise, while extrinsic noise refers to cell-to-cell variations due, for example, to differential expression of proteins.

A series of presentations will provide an introduction to stochastic modeling in intracellular biological systems with a particular focus on chemical reactions and intrinsic noise.  Emphasis will be placed on the usefulness of building stochastic models based on the objective of the modeling and the availability of data for different modeling frameworks.

The presenters shall illustrate the pros and cons of stochastic modeling as well as describe some of the computational and methodological challenges in their usage, emphasizing the key differences between stochastic and deterministic approaches to systems biology.  Simple examples of cellular systems like transcriptional control of gene expression and calcium oscillations shall be provided to illustrate these concepts.

Biosketch
Ted Kalbfleisch, Ph.D.

Dr. Kalbfleisch received his undergraduate degree in Chemistry from the University of Louisville.  He received his Ph.D. in Chemical Physics, and a postdoctoral fellowship from Boston University.  From there, he joined the biotech industry where he worked for eight years developing and managing data and laboratory information management systems, and to provide bioinformatics support for the high and ultrahigh throughput genomic production operations within those companies.  He joined the University of Louisville in January of 2005 as the director of bioinformatics operations with the center for genetics and molecular medicine.  While there, he has worked in a very productive collaboration with Drs. Kenneth Ramos, and Gregory Rempala to study cellular systems using stochastic simulations.  His other efforts and interests are directed toward the development of software and database infrastructure for the support of the bioinformatics needs of the life sciences community.

Kenneth S. Ramos, Ph.D.

Dr. Ramos is Chairman and Professor of the Department of Biochemistry and Molecular Biology and Director of the Center for Genetics and Molecular Medicine in the School of Medicine at the University of Louisville.  He also holds appointments at the James Graham Brown Cancer Center and the Gheens Center for Aging. Dr. Ramos has a B.S in Pharmaceutical Sciences and Chemistry (magna cum laude), and completed a postdoctoral fellowship in Physiology and Pharmacology.  He is a leading expert in the study of molecular mechanisms of environmental disease.  He is a molecular biologist and toxicologist with long-standing interests in transcriptional control, genomic basis of environmental vascular and renal disease and inference of biological regulatory networks.

Grzegorz (“Greg”) Rempala, Ph.D. D.Sc.

Dr. Rempala holds undergraduate and MA degrees in mathematics (summa cum laude) from the University of Warsaw and two doctoral degrees: in mathematical statistics from Bowling Green State University and in applied mathematics from Warsaw Technical University. He is currently an associate professor of statistics in the Department of Mathematics and the Department of Biochemistry and Molecular Biology at the University of Louisville.  Dr Rempala is a senior research fellow of the Institute for Mathematics and Its Applications (IMA) at the University of Minnesota as well the Statistical and Applied Mathematical Sciences Institute (SAMSI) in North Carolina.  He is an author of multiple scientific papers on the issues of theoretical and applied mathematics (especially applied probability theory) and statistics.  In particular, he has written extensively on the applications of modern statistical methods in analyzing complex systems and the new ways of developing statistical and mathematical modeling techniques relevant to the studies of complex networks.