Keynote Lecture

Stochastic Effects in Signaling Pathways in Cells:
Interaction between Visualization and Modeling

Prof. Marek Kimmel

Rice University, USA

Abstract

Individual biological cells display stochastic variability in their responses to activating stimuli. This variability can be measured using recent molecular biology techniques, which prove that in many respects no cell in the population behaves like an "average cell". An important example of a system in which stochastic effects play a major role is the innate immune response, which plays the role of a first line of defense from potentially harmful organisms. In this response, infecting organisms induce cellular signaling pathways to protective cytokines such as interferon. Understanding how individual cells react to low-level signals induced by the presence of pathogens is important for fields such as medicine, pharmacology and biodefense. In cells taking part in the innate immune response stochastic variability confers a kind of "Stochastic robustness": Subpopulations of cells may react differently to the same stimulus, for example some of them proliferating, some of them moving to apoptosis (programmed cell death). It is important to understand the sources of stochasticity in biological cells and develop mathematical tools to estimate, model and predict stochastic effects.

The primary sources of stochasticity in eukaryotic cells seem to be: (i) Assembly of the transcription complexes of molecules attracting RNA Polymerase II. (ii) For low levels of activation signal, fluctuations in the number of cell membrane receptors binding activating molecule. Building mathematical models of these processes involves such tools as the theory of stochastic processes, ordinary and partial differential equations as well as a variety of simulation tools.

A very important aspect in building models in this context is visualization and measurements of dynamic changes of concentrations of biomolecules such as messenger RNA and proteins in individual cells. The tools, which have been recently developed for this purpose, are high-throughput microscopy, flow-cytometry and refined methods of labeling biomolecules.

The talk will review the issues outlined above, based on joint work of my colleagues and myself in Houston and in Gliwice, on the background of general progress in this field.

• Fujarewicz K, Kimmel M, Lipniacki T, Swierniak A (2007) Adjoint systems for models of cell signalling pathways and their application to parameter fitting, IEEE/ACM Transactions on Computational Biology and Bioinformatics 4: 322-435.
• Kimmel M and Axelrod DE (2001) Branching Processes in Biology. Springer, New York.
• Hat B, Paszek P, Kimmel M, Piechor K, Lipniacki T (2007) How the Number of Alleles Inflences Gene Expression. Journal of Statistical Physics, 128: 511-533.
• Lipniacki T, P Paszek, AR Brasier, B Luxon and M Kimmel (2004) Mathematical model of NFkB module. J Theor Biol 228: 195-215.
• Lipniacki T, Paszek P, Brasier AR, Luxon B, Kimmel M (2006) Stochastic regulation in early immune response. Biophys J 90: 725-742.
• Lipniacki T, Paszek P, Marciniak-Czochra A, Brasier AR, Kimmel M (2006) Transcriptional stochasticity in gene expression. J Theor Biol 238: 348-367.
• Lipniacki T, Puszynski K, Paszek P, Brasier AR, Kimmel M (2007) Single TNFa trimers mediating NFkB activation: Stochastic robustness of NFkB signaling. BMC Bioinformatics in press
• Polanski A and Kimmel M (2007) Bioinformatics. Spirnger, New York.

Brief biography of the Speaker

Marek Kimmel is a Professor in the Systems Engineering Group at the Silesian University of Technology, Professor of Statistics at Rice University, a Professor of Biostatistics and Applied Mathematics (adj.) at M.D. Anderson Cancer Center and a Professor of Biometry (adj.) at the School of Public Health of the University of Texas Health Sciences Center in Houston. His principal interests are systems biology (modeling of signaling pathways), stochastic modeling of human diseases (lung cancer progression and screening), and statistical and population genetics (microsatellite and SNP markers evolution). In 1980, Dr. Kimmel received a doctoral degree in control engineering from the Silesian University of Technology. His postdoctoral training took place in the Memorial Sloan-Kettering Cancer Center in New York, NY. In 1997 he received a habilitation in Mathematical Sciences from the Jagiellonian University. He also holds a titular professorship in Poland. Since 1990, he has been employed by Rice University. At Rice University, he was the Chairman of the Statistics Department and currently he is leading the Rice Bioinformatics Group as well as the doctoral program in Statistical Genetics and Bioinformatics. Dr. Kimmel is a Fellow of the American Statistical Association. Dr. Kimmel is a member of editorial boards of Journal of the National Cancer Institute, Journal of Theoretical Biology, and Mathematical Biosciences. He is heading the Mathematical Biology Section of Biology Direct (a BMC journal). He published around 150 peer-review papers and co-authored two monographs "Branching Processes in Biology" (Springer 2002) and "Bioinformatics" (Springer 2007) and a number of co-edited volumes. Dr. Kimmel has advised around 20 Ph.D. students in United States, Poland and France. He organized numerous international meetings, schools and conferences, including a recent NSF workshop on stochastic effects in gene circuits. His research has been supported by NIH (NCI), NSF, NATO and KBN. Dr. Kimmel has a long record of collaboration with cell and molecular biologists, geneticists, epidemiologists and physicians on one side, and pure and applied mathematicians on the other, in building and analysis of mathematical and computational models in systems biology.