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This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology./a
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.
Gene regulatory networks dynamically control the expression levels of all the genes, and are the keys in explaining various phenotypes and biological processes. The advance of high-throughput measurement technology, such as microarray and next-generation sequencing, enabled us to globally scrutinize various cell properties related to gene regulation and build statistical models to make quantitative predictions. The evolutionary process has left all kinds of traces in the current biological systems. The study of the evolution of gene regulatory networks in comparable cell types across species is an efficient method to unravel such evolutionary traces and help us to better understand the regulatory mechanism. The two main themes of my research are: analysing various "omics" data in the evolutionary context to identify conservation and changes in gene regulatory networks; and building computational models to incorporate different "omics" data for the annotation of genomes and prediction of evolution in gene regulation. The second chapter of my thesis described a computational algorithm for de novo prediction of transcription factor binding site motifs in multiple species. The algorithm, named "GibbsModule", uses three information sources to improve the prediction power, which are 1)co-expressed genes sharing the same set of motifs; 2)binding sites co-localizing to form modules; and 3)the conservation for the use of motifs across species. We developed a Gibbs sampling procedure to incorporate the three information sources. GibbsModule out-performed the existing algorithms on several synthetic and real datasets. When applied to study the binding regions of KLF in embryonic stem cells, GibbsModule discovered a new functional motif. We also used ChIP followed by qPCR to demonstrate that the binding affinity of GibbsModule predicted binding sites are stronger than non-predicted motifs. Both genome sequence and gene expression carry information about gene regulation. Therefore, we can learn more about gene regulatory networks by jointly analysing sequence and expression data. In the third chapter of my thesis, we first introduced a comparative study of the pre-implantation process of embryos in three mammalian species: human, mouse, and cow. We measured time course expression profiles of the embryos during the early development, and analysed them together with genome sequence data and ChIP-seq data. We observed a large portion of changed homologous gene expression, suggesting a prevalent rewiring of gene regulation. We associated the changes of gene expression with different types of cis-changes on the genome sequences. Especially, we found about 10% of species specific transposons are carrying multiple functional binding sites, which are likely to explain the evolution of gene expression. The second part of this chapter presented a phylogenetic model that incorporated the change of motif use and gene expression to infer the rewiring of gene regulatory networks. Epi-genetic modifications, including histone modifications and DNA methylation, are known to be associated with gene regulation. In chapter four, we studied the evolution of epi-genomes in pluripotent stem cells of human, mice, and pigs. We observed the conservation of epi-genomes in different categories of genomic regions. We found the evidence of positive and negative selections on the evolution of epi-genomes. Using linear regression models, the evolution of epi-genomes can largely explain the evolution of gene expression. In the second part of this chapter, we introduced a statistical model to describe the evolution of genomes considering both the DNA sequences and epi-genetic modifications. Based on the evolutionary model, we improved the current alignment algorithm with the information of epi-genetic modification distributions.
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.
How new modeling techniques can be used to explore functionally relevant molecular and cellular relationships.
Gene expression is controlled by regulatory DNA sequences, often called cis-regulatory modules or CRMs in higher organisms. Even though complete genomes are available in many species, a catalog of CRMs is far from complete. Meanwhile, how basic building blocks of CRMs, called transcription factor binding sites (TFBSs), coordinate to drive gene expression is unclear. My thesis is focused on predicting the location of CRMs in genomes and understanding their function and evolution through computational methods. The first part of my thesis developed a comparative genomic method of CRM prediction. This method is based on a probabilistic model of CRM evolution, capturing the constraint as well as turnover of TFBSs during evolution. Through a statistical approach that marginalizes hidden variables, the method is able to deal with the uncertainty of sequence alignment and prediction of individual TFBSs, two primary technical hurdles of existing methods. In a related work, I collaborated with a graduate colleague to study the empirical evolutionary pattern of TFBSs, taking advantage of the recently available 12 Drosophila genomes. We found, among other things, that the evolution of binding sites is constrained by the affinities to their cognate TFs. The second part of my thesis developed predictive models of gene regulation based on physical principles. One such method is able to analyze large scale TF-DNA binding data to identify cooperative interactions of TFs, to explore the effects of sequence organization on the TF interactions and to study the conservation of TF-binding affinities of sequences. The model we developed for predicting expression patterns of CRMs advances existing work by incorporating a number of mechanistic aspects of transcriptional regulation, including cooperative binding of TFs, the synergism among multiple activators and the short-range repression, where repressors block the function of adjacent activator sites. This allows us to gain understandings of the regulatory process in Drosophila segmentation, for instance, both the cooperative interactions among activator molecules and their synergistic interaction with the transcriptional machinery are important in determining the expression patterns.
This issue on Computational Models for Cell Processes is based on a workshop that took place in Turku, Finland, May 2008. The papers span a mix of approaches to systems biology, ranging from quantitative techniques to computing paradigms inspired by biology.
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.