Stochastic Modeling Of Post Transcriptional Regulation Of Gene Expression In The Limit Of Strong Interactions

The last ten years have witnessed a remarkable increase in our awareness of the importance of events subsequent to transcriptional initiation in terms of the regulation and control of gene expression. In particular, the development of recombinant DNA techniques that began in the 1970s provided powerful new tools with which to study the molecular basis of control and regulation at all levels. The resulting investigations revealed a diversity of post-transcriptional mechanisms in both prokaryotes and eukaryotes. Scientists working on translation, mRNA stability, transcriptional (anti)termination or other aspects of gene expression will often have met at specialist meetings for their own research area. However, only rarely do workers in different areas of post-transcriptional control/ regulation have the opportunity to meet under one roof. We therefore thought it was time to bring together leading representatives of most of the relevant areas in a small workshop intended to encourage interaction across the usual borders of research, both in terms of the processes studied, and with respect to the evolutionary division prokaryotes/eukaryotes. Given the breadth of topics covered and the restrictions in size imposed by the NATO workshop format, it was an extraordinarily difficult task to choose the participants. However, we regarded this first attempt as an experiment on a small scale, intended to explore the possibilities of a meeting of this kind. Judging by the response of the participants during and after the workshop, the effort had been worthwhile.

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This book provides methods and techniques used in construction of global transcriptional regulatory networks in diverse systems, various layers of gene regulation and mathematical as well as computational modeling of transcriptional gene regulation. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Modeling Transcriptional Regulation: Methods and Protocols aims to provide an in depth understanding of new techniques in transcriptional gene regulation for specialized audience.

To effectively understand and treat complex diseases such as cancer, mathematical and statistical modeling is essential if one wants to represent and characterize the interactions among the different regulatory components that govern the underlying decision making process. Like in any other complex decision making networks, the regulatory power is not evenly distributed among its individual members, but rather concentrated in a few high power "commanders". In biology, such commanders are usually called masters or canalizing genes. Characterizing and detecting such genes are thus highly valuable for the treatment of cancer. Chapter II is devoted to this task, where we present a Bayesian framework to model pathway interactions and then study the behavior of master genes and canalizing genes. We also propose a hypothesis testing procedure to detect a "cut" in pathways, which is useful for discerning drugs' therapeutic effect. In Chapter III, we shift our focus to the understanding of the mechanisms of action (MOA) of cancer drugs. For a new drug, the correct understanding of its MOA is a key step for its application to cancer treatments. Using the Green Fluorescent Protein technology, researchers have been able to track various reporter genes from the same cell population for an extended period of time. Such dynamic gene expression data forms the basis for drug similarity comparisons. In Chapter III, we design an algorithm that can identify mechanistic similarities in drug responses, which leads to the characterization of their respective MOAs. Finally, in the course of drug MOA study, we observe that cells in a hypothetical homogeneous population do not respond to drug treatments in a uniform and synchronous way. Instead, each cell makes a large shift in its gene expression level independently and asynchronously from the others. Hence, to systematically study such behavior, we propose a mathematical model that describes the gene expression dynamics for a population of cells after drug treatments. The application of this model to dose response data provides us new insights of the dosing effects. Furthermore, the model is capable of generating useful hypotheses for future experimental design.

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.