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A quantitative kinetic model is one which predicts dynamic behavior given a hypotheses of the underlying mechanisms. Mechanistic models of biological systems have two major issues that limit their usefulness. On one hand, noting every possible state of the system inflates the model specification, leading to an arbitrary tradeoff between explicitness and tractability. On the other hand, limited experimental knowledge results in under-constrained model parameters and uncertain model structures. I am interested in developing abstract and compact mechanistic specifications that enable large scale models, specifically in the application of rule-based modeling to biological problems. I develop for BioNetGen, a rule-based format for specifying reaction rules between molecular sites. I am also interested in the statistical characterization of uncertain parameter and model spaces, or naively parameter estimation, using tools such as parallel tempering and Bayesian model selection. These concepts can be applied to a wide range of biological domains (transcriptional regulation, protein conformational dynamics, biochemical pathways, population dynamics), experimental methods (spectroscopy, microscopy, immunoblot), and data resolutions (single-molecule, single-cell, ensemble, proteomics).

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