Stochastic Modeling of Post-transcriptional Regulation of Gene Expression in the Limit of Strong Interactions PDF Download
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Author: Robert Barr
Publisher:
ISBN:
Category : Gene expression
Languages : en
Pages : 74
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Author: Robert Barr
Publisher:
ISBN:
Category : Gene expression
Languages : en
Pages : 74
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Author: Jeffrey Wilusz
Publisher: Springer Science & Business Media
ISBN: 1588297837
Category : Business & Economics
Languages : en
Pages : 330
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Book Description
Step-by-step instructions that ensure successful results.
Author: Stephanie Marie Potoka
Publisher:
ISBN:
Category :
Languages : en
Pages : 102
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Author: SHAHID MUKHTAR
Publisher: Humana
ISBN: 9781071615331
Category : Science
Languages : en
Pages : 307
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Book Description
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.
Author: Orna Resnekov
Publisher: Springer
ISBN: 9783642646096
Category : Science
Languages : en
Pages : 0
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Book Description
Many important cellular processes rely on posttranscriptional control of gene expression. This book describes the mechanisms of gene expression at this level that occur in the cytoplasm of prokaryotes and eukaryotes. Several introductory chapters discuss the general principles of translation and mRNA stability. The interactions of mature mRNA with the translational machinery, the components of mRNA degradation and antisense RNA are surveyed. Subsequent chapters discuss protein folding, transport, modification and degradation. The book is an invaluable source of information for both newcomers and those wishing an overview of the field.
Author: Chen Zhao
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Arwen Vanice Meister
Publisher:
ISBN:
Category :
Languages : en
Pages :
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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.
Author: Lisa Amrhein
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Mariano A. Garcia-Blanco
Publisher:
ISBN:
Category :
Languages : en
Pages : 399
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Book Description
