 | Analyzing stochastic models, Thomas G. Kurtz (University of Wisconsin) |
| Stochastic aspects of actin filament dynamics, Hans G. Othmer (University of Minnesota) |
| Intracellular models and single-cell measures of virus growth, John Yin (University of Wisconsin) |
| Part I: Local and global stability of biochemical reaction network dynamics, Gheorghe Craciun (University of Wisconsin) |
| An introduction to discrete-event simulation, Peter W. Glynn (Stanford University) |
| An introduction to discrete-event simulation, Peter Haas (IBM Research Division) |
| Part II: Homotopy methods for counting reaction network equilibria, Ruth J. Williams (University of California, San Diego) |
| Multiscale models for synthetic biology, Yiannis N. Kaznessis (University of Minnesota) |
| Subdiffusion and reaction networks in biophysics, Samuel Kou (Harvard University) |
| Opening plenary talk: Stochastic analysis is the fundamental tool for understanding biological function, Michael C. Reed (Duke University) |
| Stochastic oscillations in small genetic networks, Lev S. Tsimring (University of California, San Diego) |
| Bayesian inference for stochastic intracellular reaction network models, Darren James Wilkinson (University of Newcastle upon Tyne) |
| Inferring an underlying reaction network from the data, Grzegorz A. Rempala (University of Louisville) |
| Closing plenary talk: Metabolic engineering and metabolic modeling: where do we go from here?, James C. Liao (University of California) |
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