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| Back to CCST Home |
This course was offered in January and February of 2006.
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Topics Click on a topic to view a printable PDF copy of the instructor's slides. Note: The lecture numbers correspond to the web-based lectures. Lecture 1: Introduction to Markov processes Lecture 2: Course graining of spatially distributed stochastic processes Lecture 3: Hybrid (deterministic-stochastic) multiscale simulation; closures; statistical measures Lecture 4: Multiscale aspects of biological systems; multiscale spatially well-mixed stochastic modeling Lecture 5: Introduction to cancer Lecture 6: Introduction to rule-based modeling of signaling pathways Lecture 7: Introduction to the epidermal growth factor receptor signaling Lecture 8: Tutorial of rule-based modeling of biochemical systems using the BioNetGen package Lecture 9: (Continuation of Lecture 7) Lecture 10: Membrane biology |
Funding We acknowledge partial support of this research by the U.S. Department of Energy (DOE). However, any opinions, findings, conclusions, or recommendations expressed herein are those of the authors and do not necessarily reflect the views of the DOE. |
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| Objectives Offer introductory material on multiscale modeling and simulation related to, but not limited to, biological systems. The course is intended mainly for graduate students and faculty who are interested in the topic. It is free and open to all interested. It is modular, so one could attend only some of the topics. |
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| Brief Description Multiscale simulation is an emerging scientific field that spans many disciplines including physics, chemistry, mathematics, statistics, biology, engineering, and materials science. The idea of multiscale modeling is straightforward: one computes information at a smaller (finer) scale and passes it to a model at a larger (coarser) scale by leaving out degrees of freedom as one moves from finer to coarser scales. Within this context, the most common goal of multiscale modeling is to predict the macroscopic behavior of an engineering process from first principles (upscaling or bottom-up approach). This short course is intended mainly for graduate students and faculty who are interested in the topic. It offers introductory material on multiscale modeling and simulation related, but not limited, to biological systems. In this short course, the foundations of discrete, particle models, are first discussed with emphasis on Monte Carlo stochastic simulation methods in well-mixed and spatially distributed systems. Hybrid (stochastic-deterministic) simulation and coarse-graining are identified as two general and complementary approaches of multiscale modeling and their principles are discussed. An introduction to cancer and signal transduction is given. Finally, rule-based modeling of biological systems is introduced. |
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