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AfCS > Modeling Cellular Systems for Fun and ProfitJohn Doyle & Adam Arkin SummaryThis four hour course will introduce Alliance members to the basic mechanics of modeling and its uses. The course will cover: constructing a model from data and both logical and physical knowledge, the basics in model analysis and simulations, comparison of models with experiment and development of biological theory. GoalsAttendees should leave with a basic understanding of the utility of modeling in the biological context. The role of experimental data in building and validating a model and the role of a model in driving experiment should be clear. The basic mechanics of building, analyzing and simulating a model will be taught to the point that attendees can basically understand how to use some of the pre-existing modeling tools. The role of modeling in the Alliance will be made clear. Possible Background Reading1) Stryer, L. Biochemistry, 4th Edition, W.H. Freeman and Company, Chapter 8. Basic tutorial on enzyme thermodynamics and kinetics that sets the stage for a lot of the elementary math. 2) Murray, J.D. Mathematical Biology, Springer-Verlag One of the best general overviews of how people have applied theory and calculation to biological systems. But a bit abstract for my tastes at times. 3) Edelstein-Keshet, Leah. Mathematical Models in Biology Another of the standard textbooks. 4) Fell, David, Understanding the Control of Metabolism (Frontiers in Metabolism Ser. ; No. 2)) A good background in the current state of metabolic control theory. Be warned, though, this is a particular theory with particular viewpoints and assumptions. Nonetheless, good discussion. 5) Voit, Eberhardt O. Computational Analysis of Biochemical Systems : A Practical Guide for Biochemists and Molecular Biologists Similar to above, somewhat broader biological applicability but also more complex math. 5) Gerhart, John and Marc W. Kirschner Cells, Embryos, and Evolution : Toward a Cellular and Developmental Understanding of Phenotypic Variation and Evolutionary Adaptability Conceptual approach to � engineering design principles� that occur in biology using no math. Authors don�t realize they�re describing familiar engineering principles, but that in some ways only strengthens their argument that there is something deep and intrinsic about these motifs. Website (www.cds.caltech.edu/~doyle/AFCS_shortcourse)This website will have updates to this syllabus, background articles and slide sets, and a list of current modeling tools and where to get them. The slide sets for the talks will be posted after the meeting. Syllabus Topic 1: Background What is a model? What is a biological model? What can and can�t a model tell you? How do you go from biological data and interpretation to a model? When is it profitable to model? What are the big-picture lessons that come from models and theory? Theory and Modeling: Necessity, limits, and levels of description. Topic 2: Mechanics of Models Representation of biological data and interpretation? Examples: Lambda Phage, Bacterial �taxis and heat shock Logical, Phenomenological, and physical models? Logical Models of Lambda Physical Models of Lambda Abstractions: Formal and informal Physical Hierarchy: From the molecular mechanics, to the master equations, to ODEs, etc. Formal abstraction: Lumped parameter systems Time scale separation�Michaelis Menten Assumption Tracking Informal abstraction: Fit models, logic models Topic 3: Model Analysis Sensitivity, Robustness and Bifurcation Steady-State Analysis Simulation Challenges in modeling multiple levels of abstraction Comparison with data Topic 4: Model Validation and the Experiment/Theory Cycle Deducing Models from Data Measuring sensitive parameters Testing model structure hypotheses Generalizing from models Comparative network analysis Robustness Design principles Resources currently available Rough Tentative Schedule 40 Minutes: What is a model and what are modeling objectives? Adam Arkin 20 Minutes: Big picture�what can we learn from theory? John Doyle 60 Minutes: Physical Modeling�examples with l-phage Adam Arkin 40 Minutes: Heat Shock, Taxis and Feedback Systems John Doyle 40 Minutes: Connections of data to models Adam Arkin 20 Minutes: What can theory tell us about experiments John Doyle 20 Minutes: Discussion and questions Everyone. |
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