Reverse Engineering Of Regulatory Networks

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.

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.

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.

"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].

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.

"This book focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization"--Provided by publisher.

1 Kevin Moses It is now 25 years since the study of the development of the compound eye in Drosophila really began with a classic paper (Ready et al. 1976). In 1864, August Weismann published a monograph on the development of Diptera and included some beautiful drawings of the developing imaginal discs (Weismann 1864). One of these is the first description of the third instar eye disc in which Weismann drew a vertical line separating a posterior domain that included a regular pattern of clustered cells from an anterior domain without such a pattern. Weismann suggested that these clusters were the precursors of the adult ommatidia and that the line marks the anterior edge of the eye. In his first suggestion he was absolutely correct - in his second he was wrong. The vertical line shown was not the anterior edge of the eye, but the anterior edge of a moving wave of patterning and cell type specification that 112 years later (1976) Ready, Hansen and Benzer would name the "morphogenetic furrow". While it is too late to hear from August Weismann, it is a particular pleasure to be able to include a chapter in this Volume from the first author of that 1976 paper: Don Ready! These past 25 years have seen an astonishing explosion in the study of the fly eye (see Fig.

Systems Metabolic Engineering is changing the way microbial cell factories are designed and optimized for industrial production. Integrating systems biology and biotechnology with new concepts from synthetic biology enables the global analysis and engineering of microorganisms and bioprocesses at super efficiency and versatility otherwise not accessible. Without doubt, systems metabolic engineering is a major driver towards bio-based production of chemicals, materials and fuels from renewables and thus one of the core technologies of global green growth. In this book, Christoph Wittmann and Sang-Yup Lee have assembled the world leaders on systems metabolic engineering and cover the full story – from genomes and networks via discovery and design to industrial implementation practises. This book is a comprehensive resource for students and researchers from academia and industry interested in systems metabolic engineering. It provides us with the fundaments to targeted engineering of microbial cells for sustainable bio-production and stimulates those who are interested to enter this exiting research field.