Our research is focused on modeling, analysis, simulation and software, applied to multiscale, networked systems in biology, materials and social networks. My group has been developing advanced algorithms for discrete stochastic simulation of systems where the fate of a few key molecules can make a big difference to important outcomes. We engage with experimentalists through the analysis of data and the development of mathematical models that yield insight and offer new directions for research. Current collaborations range from biology (circadian rhythm [jet lag] and cell polarization), to medicine (coagulopathy and post-traumatic stress disorder), to ecology (chytrid disease in frogs), to social networks (sentiment analysis and opinion dynamics), to materials. We are collaborating with Prof. Chandra Krintz on the development of an integrated, cloud-based environment called Stochastic Simulation Service (StochSS) for modeling and simulation of biological processes.
Announcements & News
|interface_latex.pdf||Lawson, M. J., Petzold, L., & Hellander, A. (2015). Accuracy of the Michaelis-Menten Approximation When Analysing Effects of Molecular Noise. J. R. Soc. Interface 12:20150054.|
|1.4921638.pdf||Wu, S., Fu, J., & Petzold, L. R. (2015). Adaptive Deployment of Model Reductions for Tau-Leaping Simulation. J. Chem. Phys. 142, 204108|
|temporal_opdetection-2.pdf||Bhattacharjee, K., & Petzold, L. (2015). Detecting Opinions in a Temporally Evolving Conversation on Twitter. Proceedings of the International Conference on Social Informatics (SocInfo), Beijing, China.|
|gpubasedsims.pdf||Pro, J. W., Lim, R. K., Petzold, L. R., Utz, M., & Begley, M. R. (2015). GPU-Based Simulations of Fracture in Idealized Brick and Mortar Composites. J. Mech. Phys. Solids 80, 68-85.|
|international_journal_of_high_performance_computing_applications-2015-lim-1094342015593395.pdf||Lim, R. K., Pro, J. W., Begley, M. R., Utz, M., & Petzold, L. R. (2015). High-performance Simulation of Fracture in Idealized "Brick and Mortar" Composites Using Adaptive Monte Carlo Minimization on the GPU. Int. J. High Perform. C. 1-14|