Learning and prediction with dynamical system models of gene regulation
- Biological structure and function depend on complex regulatory interactions between many genes. A wealth of gene expression data is available from high-throughput genome-wide measurement and single-cell measurement technologies, but systematic gene regulation modeling strategies and effective inference methods are still needed. This thesis focuses on biophysics-based dynamical system models of gene regulation that capture the mechanisms of transcriptional regulation at various degrees of detail. Deterministic modeling is fairly well-established, but algorithms for inferring the structure of novel gene regulatory systems are still lacking. We propose a method for learning the parameters of a standard nonlinear deterministic model from experimental data, in which we transform the nonlinear fitting problem into a convex optimization problem by restricting attention to steady-states and using the lasso for parameter selection. Stochastic modeling is much less mature. The Master equation model captures the mechanisms of gene regulation in full molecular detail, but it is intractable for all but the simplest systems, so simulation and approximations are essential. To help clarify the often-confusing situation, we present a simulation study to demonstrate the qualitative behavior of multistable systems and compare the performance of the van Kampen expansion, Gillespie algorithm, and Langevin simulation.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Meister, Arwen Vanice
|Stanford University, Institute for Computational and Mathematical Engineering.
|Wong, Wing Hung
|Wong, Wing Hung
|Statement of responsibility
|Arwen Vanice Meister.
|Submitted to the Institute for Computational and Mathematical Engineering.
|Thesis (Ph.D.)--Stanford University, 2013.
- © 2013 by Arwen Vanice Meister
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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