ITR: Statistical Mechanics of Sloppy Models: From Signal Transduction in the Cell Cycle to Forest Modeling and the Nitrogen Cycle

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This grant is made in response to a small proposal submitted to the Information Technology Research (ITR) Initiative. The PI will leverage information technology and sophisticated methods from the statistical mechanics of disordered systems to systematically explore large, complex biological models. These models are sloppy, and the key challenge is to extract reliable and falsifiable predictions from them. The PI will apply statistical mechanics not to the physical system, but to the parameters in the dynamical models of the system: this meta-modeling technique thus draws predictions from the entire ensemble of models that are consistent with the currently available data. Meta-modeling will be applied to four problems. Three of these are systems studied in the field of cellular signal transduction: the Erk system in PC12 cells; growth factor receptor trafficking and Cdc42; and the cell cycle in the colon cell line Caco-2. The final application is in ecology: the nutrient cycles in forested ecosystems. The PI bypasses traditional analytical methods in mathematical statistics, by leveraging the computational power made possible by the information technology revolution. The sampling methods are direct, flexible, and easily implemented; the methods properly average over multiple locally optimal states and properly cope with nonlinearity in the modeling process. The PI will use many technical ideas drawn from materials simulations. Numerical efficiency is enhanced through the use of a soft-mode symmetry-breaking field to penalize computer time, and through the use of algorithms developed to accelerate simulations of glassy materials. Weakly-coupled replicas of the model will be used to avoid problems due to soft modes when comparing different cell types: renormalization of parameters will be studied when comparing different theories of the same system. These methods are efficiently run in parallel on current large-scale computers; ensembel genration could be Grid enabled, delocalized over the Web.The meta-modeling concept of abstracting the modeling process into a statistical mechanics problem transforms a tedious, ill-posed search into an exciting new problem in complex, disordered systems. From an applications view, insights drawn from the materials physics community form powerful tools, allowing real predictive power where only qualitative exploration was previously possible. From a physics view, the prevalence of soft modes in parameter space provides new insights and provokes new methods. From an information technology view, intensive computational analysis can now be brought to bear on a large new class of problems of importance to science and society.The three signal transduction networks being studied are of particular interest to the development of drug therapies in cancer, and are a well-studied preview of the kinds of challenges the bioinformatics and proteomics revolution will present in the coming decade. The extension to ecological meta-modeling will lead to applications in many other complex systems where incomplete or preliminary data nonetheless need rigorous analysis.%%% This grant is made in response to a small proposal submitted to the Information Technology Research (ITR) Initiative. The PI will leverage information technology and sophisticated methods from the statistical mechanics of disordered systems to systematically explore large, complex biological models. These models are sloppy, and the key challenge is to extract reliable and falsifiable predictions from them. The PI will apply statistical mechanics not to the physical system, but to the parameters in the dynamical models of the system: this meta-modeling technique thus draws predictions from the entire ensemble of models that are consistent with the currently available data. Meta-modeling will be applied to four problems. Three of these are systems studied in the field of cellular signal transduction: the Erk system in PC12 cells; growth factor receptor trafficking and Cdc42; and the cell cycle in the colon cell line Caco-2. The final application is in ecology: the nutrient cycles in forested ecosystems.***