Download or read book Towards Reverse Engineering Gene Regulatory Networks written by Thomas Schlitt and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Reverse Engineering of Regulatory Networks written by Sudip Mandal and published by Springer Nature. This book was released on 2023-11-07 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume details the development of updated dry lab and wet lab based methods for the reconstruction of Gene regulatory networks (GRN). Chapters guide readers through culprit genes, in-silico drug discovery techniques, genome-wide ChIP-X data, high-Throughput Transcriptomic Data Exome Sequencing, Next-Generation Sequencing, Fuorescence Spectroscopy, data analysis in Bioinformatics, Computational Biology, and S-system based modeling of GRN. 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 key tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Reverse Engineering of Regulatory Networks aims to be a useful and practical guide to new researchers and experts looking to expand their knowledge.
Download or read book LINKING THE GENES written by ALBERTO. DE LA FUENTE and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Gene Network Inference written by Alberto Fuente and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians.
Download or read book Reverse Engineering of Gene Regulatory Networks for Discovery of Novel Interactions in Pathways Using Gene Expression Data written by Tanwir Habib and published by . This book was released on 2009 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book System Identification Methods for Reverse Engineering Gene Regulatory Networks written by Zhen Wang and published by . This book was released on 2010 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of high throughput measurement technologies, large scale gene expression data are available for analysis. Various computational methods have been introduced to analyze and predict meaningful molecular interactions from gene expression data. Such patterns can provide an understanding of the regulatory mechanisms in the cells. In the past, system identification algorithms have been extensively developed for engineering systems. These methods capture the dynamic input/output relationship of a system, provide a deterministic model of its function, and have reasonable computational requirements. In this work, two system identification methods are applied for reverse engineering of gene regulatory networks. The first method is based on an orthogonal search; it selects terms from a predefined set of gene expression profiles to best fit the expression levels of a given output gene. The second method consists of a few cascades, each of which includes a dynamic component and a static component. Multiple cascades are added in a parallel to reduce the difference of the estimated expression profiles with the actual ones. Gene regulatory networks can be constructed by defining the selected inputs as the regulators of the output. To assess the performance of the approaches, a temporal synthetic dataset is developed. Methods are then applied to this dataset as well as the Brainsim dataset, a popular simulated temporal gene expression data. Furthermore, the methods are also applied to a biological dataset in yeast Saccharomyces Cerevisiae. This dataset includes 14 cell-cycle regulated genes; their known cell cycle pathway is used as the target network structure, and the criteria sensitivity, precision, and specificity are calculated to evaluate the inferred networks through these two methods. Resulting networks are also compared with two previous studies in the literature on the same dataset.
Download or read book Gene Regulatory Networks written by Guido Sanguinetti and published by Humana. This book was released on 2018-12-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the extraction of causal information from biological data; GRN inference from multiple heterogeneous data sets; non-parametric and hybrid statistical methods; the joint inference of differential networks; and mechanistic models of gene regulation dynamics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, descriptions of recently developed methods for GRN inference, applications of these methods on real and/ or simulated biological data, and step-by-step tutorials on the usage of associated software tools. Cutting-edge and thorough, Gene Regulatory Networks: Methods and Protocols is an essential tool for evaluating the current research needed to further address the common challenges faced by specialists in this field.
Download or read book Reverse Engineering Biological Networks written by Gustavo Stolovitzky and published by Wiley-Blackwell. This book was released on 2007-12-26 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This volume is the result of a workshop entitled Dialogue on Reverse Engineering Assessment and Methods (DREAM) held on September 7-8, 2006, at Wave Hill, New York"--P [vii].
Download or read book Time Lagged Information Theoretic Approaches to the Reverse Engineering of Gene Regulatory Networks written by Vijender Chaitankar and published by . This book was released on 2010 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Toward Reverse Engineering Spatiotemporal Gene Regulatory Networks of Nematostella Vectensis written by Amir Masoud Abdol and published by . This book was released on 2018 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, the sea anemone Nematostella vectensis has become a popular model to study bilaterian evolution, development and more recently also regeneration. Understanding genetic interactions during the early development of N. vectensis is the first step toward unveiling the details of its early developmental processes, e.g., polarization, the formation of the blastula and initiation of the gastrulation process. Furthermore, the collective knowledge of gene interactions allows researchers to speculate about possible Gene Regulatory Networks (GRNs) governing each process. The knowledge of gene interactions also provides an opportunity to reverse engineer gene interactions in a GRN model which potentially leads to detailed understanding of the early developmental processes, e.g., pattern formation and the mechanics of gastrulation. There is still a limited amount of knowledge available regarding N. vectensis gene interactions and possible GRNs involved in each developmental process. This thesis introduces a method for extracting spatial gene expression profiles from in situ hybridization images of N. vectensis embryo. My collaborators and I have introduced a systematic procedure to combine and process the available data from different sources (e.g., in situ and qPCR) in order to understand gene interactions and reconstruct testable hypotheses for GRNs controlling development.
Download or read book Reverse Engineering written by A.C. Telea and published by IntechOpen. This book was released on 2012-03-07 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reverse engineering encompasses a wide spectrum of activities aimed at extracting information on the function, structure, and behavior of man-made or natural artifacts. Increases in data sources, processing power, and improved data mining and processing algorithms have opened new fields of application for reverse engineering. In this book, we present twelve applications of reverse engineering in the software engineering, shape engineering, and medical and life sciences application domains. The book can serve as a guideline to practitioners in the above fields to the state-of-the-art in reverse engineering techniques, tools, and use-cases, as well as an overview of open challenges for reverse engineering researchers.
Download or read book Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays written by Mehmet Eren Ahsen and published by Birkhäuser. This book was released on 2015-02-25 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs.
Download or read book Combining Heterogeneous Sources of Data for the Reverse engineering of Gene Regulatory Networks written by Emma Steele and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Evolutionary Computation in Gene Regulatory Network Research written by Hitoshi Iba and published by John Wiley & Sons. This book was released on 2016-01-21 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. • Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) • Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications • Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology • Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
Download or read book Reverse Engineering Gene Regulatory Networks written by Vincenzo Belcastro and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Computational Framework to Model and Learn Context specific Gene Regulatory Networks from Multi source Data written by Ina Sen and published by . This book was released on 2011 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reverse engineering gene regulatory networks (GRNs) is an important problem in the domain of Systems Biology. Learning GRNs is challenging due to the inherent complexity of the real regulatory networks and the heterogeneity of samples in available biomedical data. Real world biological data are commonly collected from broad surveys (profiling studies) and aggregate highly heterogeneous biological samples. Popular methods to learn GRNs simplistically assume a single universal regulatory network corresponding to available data. They neglect regulatory network adaptation due to change in underlying conditions and cellular phenotype or both. This dissertation presents a novel computational framework to learn common regulatory interactions and networks underlying the different sets of relatively homogeneous samples from real world biological data. The characteristic set of samples/conditions and corresponding regulatory interactions defines the cellular context (context). Context, in this dissertation, represents the deterministic transcriptional activity within the specific cellular regulatory mechanism. The major contributions of this framework include - modeling and learning context specific GRNs; associating enriched samples with contexts to interpret contextual interactions using biological knowledge; pruning extraneous edges from the context-specific GRN to improve the precision of the final GRNs; integrating multisource data to learn inter and intra domain interactions and increase confidence in obtained GRNs; and finally, learning combinatorial conditioning factors from the data to identify regulatory cofactors. The framework, Expattern, was applied to both real world and synthetic data. Interesting insights were obtained into mechanism of action of drugs on analysis of NCI60 drug activity and gene expression data. Application to refractory cancer data and Glioblastoma multiforme yield GRNs that were readily annotated with context-specific phenotypic information. Refractory cancer GRNs also displayed associations between distinct cancers, not observed through only clustering. Performance comparisons on multi-context synthetic data show the framework Expattern performs better than other comparable methods.
Download or read book Reverse Engineering of Gene Regulation Networks Using Gene Expression Data written by Jan Wildenhain and published by . This book was released on 2004 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt:
