Download or read book Applying Integrative Computational Models to Study the Evolution of Gene Regulation written by Dan Xie and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computational Modeling Of Gene Regulatory Networks A Primer written by Hamid Bolouri and published by World Scientific Publishing Company. This book was released on 2008-08-13 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

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 2016-01-20 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 An Integrated Experimental computational Approach to Infer Gene Regulatory Networks written by David Ronald Lorenz and published by . This book was released on 2009 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Elucidating the structure and function of biological interaction networks is a major challenge of the post-genomic era; the development of methods to infer these networks has thus been an active area of research. In this work, I describe an integrated experimental/computational strategy for reverse-engineering gene regulatory networks called NIR (Network Inference by multiple Regression), derived from a branch of engineering known as system identification. This method uses mRNA expression changes in response to network gene perturbations to formulate a first-order model of functional interactions between genes in the chosen network, providing a quantitative, directed and unsupervised description of transcriptional regulatory interactions. This approach was first applied to nine genes from the SOS pathway in the model prokaryote Escherichia coli, where it correctly identified RecA and LexA as key transcriptional regulators responding to DNA damage. Further, the quantitative network model was used to distinguish the transcriptional targets of pharmacological compounds, an important consideration in drug development and discovery. In the model eukaryote Saccharomyces cerevisiae, I applied the NIR method to ten genes from the glucose-responsive Snf 1 pathway. The network model inferred from this analysis correctly identified the major transcriptional regulators, and revealed a greater degree of complexity for this pathway than previously known. The majority of putative novel interactions were subsequently verified using gene deletions and chromatin immunoprecipitation experiments. This new, validated network architecture was then used to identify and experimentally confirm combinatorial transcriptional regulation of yeast aging, a mechanism not likely to be identified in the absence of knowledge of the network structure. Overall, these results demonstrate the utility of our inference approach to characterize smaller gene regulatory networks at a higher level of detail, and to successfully use the network model to gain new insights into complex biological processes.

Download or read book Evolution of Translational Omics written by Institute of Medicine and published by National Academies Press. This book was released on 2012-09-13 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.

Download or read book Information sharing Models for Computational Genetics written by Matthew Douglas Edwards and published by . This book was released on 2016 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern genetics has been transformed by a dramatic explosion of data. As sample sizes and the number of measured data types grow, the need for computational methods tailored to deal with these noisy and complex datasets increases. In this thesis, we develop and apply integrated computational and biological approaches for two genetic problems. First, we build a statistical model for genetic mapping using pooled sequencing, a powerful and efficient technique for rapidly unraveling the genetic basis of complex traits. Our approach explicitly models the pooling process and genetic parameters underlying the noisy observed data, and we use it to calculate accurate intervals that contain the targeted regions of interest. We show that our model outperforms simpler alternatives that do not use all available marker data in a principled way. We apply this model to study several phenotypes in yeast, including the genetic basis of the surprising phenomenon of strain-specific essential genes. We demonstrate the complex genetic basis of many of these strain-specific viability phenotypes and uncover the influence of an inherited virus in modifying their effects. Second, we design a statistical model that uses additional functional information describing large sets of genetic variants in order to predict which variants are likely to cause phenotypic changes. Our technique is able to learn complicated relationships between candidate features and can accommodate the additional noise introduced by training on groups of candidate variants, instead of single labeled variants. We apply this model to a large genetic mapping study in yeast by collecting multiple genome-wide functional measurements. By using our model, we demonstrate the importance of several molecular phenotypes in predicting genetic impact. The common themes in this thesis are the development of computational models that accurately reflect the underlying biological processes and the integration of carefully controlled biological experiments to test and utilize our new models.

Download or read book Computational Genetic Regulatory Networks Evolvable Self organizing Systems written by Johannes F. Knabe and published by Springer. This book was released on 2012-08-14 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting from a single cell interacting with its environment, eventually including a changing local neighbourhood of other cells. These methods may help us understand the genesis, organization, adaptive plasticity, and evolvability of differentiated biological systems, and may also provide a paradigm for transferring these principles of biology's success to computational and engineering challenges at a scale not previously conceivable.

Download or read book Evolution as Computation written by Laura F. Landweber and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of the genetic basis for evolution has flourished in this century, as well as our understanding of the evolvability and programmability of biological systems. Genetic algorithms meanwhile grew out of the realization that a computer program could use the biologically-inspired processes of mutation, recombination, and selection to solve hard optimization problems. Genetic and evolutionary programming provide further approaches to a wide variety of computational problems. A synthesis of these experiences reveals fundamental insights into both the computational nature of biological evolution and processes of importance to computer science. Topics include biological models of nucleic acid information processing and genome evolution; molecules, cells, and metabolic circuits that compute logical relationships; the origin and evolution of the genetic code; and the interface with genetic algorithms and genetic and evolutionary programming.

Download or read book The Regulatory Genome written by Eric H. Davidson and published by Elsevier. This book was released on 2010-07-19 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene regulatory networks are the most complex, extensive control systems found in nature. The interaction between biology and evolution has been the subject of great interest in recent years. The author, Eric Davidson, has been instrumental in elucidating this relationship. He is a world renowned scientist and a major contributor to the field of developmental biology. The Regulatory Genome beautifully explains the control of animal development in terms of structure/function relations of inherited regulatory DNA sequence, and the emergent properties of the gene regulatory networks composed of these sequences. New insights into the mechanisms of body plan evolution are derived from considerations of the consequences of change in developmental gene regulatory networks. Examples of crucial evidence underscore each major concept. The clear writing style explains regulatory causality without requiring a sophisticated background in descriptive developmental biology. This unique text supersedes anything currently available in the market. - The only book in the market that is solely devoted to the genomic regulatory code for animal development - Written at a conceptual level, including many novel synthetic concepts that ultimately simplify understanding - Presents a comprehensive treatment of molecular control elements that determine the function of genes - Provides a comparative treatment of development, based on principles rather than description of developmental processes - Considers the evolutionary processes in terms of the structural properties of gene regulatory networks - Includes 42 full-color descriptive figures and diagrams

Download or read book Computational Systems Biology written by Andres Kriete and published by Academic Press. This book was released on 2013-11-26 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensively revised second edition of Computational Systems Biology discusses the experimental and theoretical foundations of the function of biological systems at the molecular, cellular or organismal level over temporal and spatial scales, as systems biology advances to provide clinical solutions to complex medical problems. In particular the work focuses on the engineering of biological systems and network modeling. - Logical information flow aids understanding of basic building blocks of life through disease phenotypes - Evolved principles gives insight into underlying organizational principles of biological organizations, and systems processes, governing functions such as adaptation or response patterns - Coverage of technical tools and systems helps researchers to understand and resolve specific systems biology problems using advanced computation - Multi-scale modeling on disparate scales aids researchers understanding of dependencies and constraints of spatio-temporal relationships fundamental to biological organization and function.

Download or read book DNA Microarrays and Gene Expression written by Pierre Baldi and published by . This book was released on 2002-09-19 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concise, 2002 inter-disciplinary introduction to DNA microarray technology, which is revolutionizing biology and medicine.

Download or read book Evolutionary Transitions to Multicellular Life written by Iñaki Ruiz-Trillo and published by Springer. This book was released on 2015-03-27 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book integrates our understanding of the factors and processes underlying the evolution of multicellularity by providing several complementary perspectives (both theoretical and experimental) and using examples from various lineages in which multicellularity evolved. Recent years marked an increased interest in understanding how and why these transitions occurred, and data from various fields are providing new insights into the forces driving the several independent transitions to multicellular life as well as into the genetic and molecular basis for the evolution of this phenotype. The ultimate goal of this book is to facilitate the identification of general and unifying principles and mechanisms.

Download or read book Spirochete Biology The Post Genomic Era written by Ben Adler and published by Springer. This book was released on 2018-08-21 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spirochetes comprise a fascinating group of bacteria. Although diverse in terms of their habitat, ecology and infectivity for vertebrate and non-vertebrate hosts, they are often considered together because of their similar cellular morphologies. This volume brings together an international group of experts to provide essential insights into spirochete biology, with an emphasis on recent advances made possible by the availability of genome sequences. As such, it offers a valuable resource for microbiologists and other scientists with an interest in spirochete biology.

Download or read book Review of the Department of Energy s Genomics GTL Program written by National Research Council and published by National Academies Press. This book was released on 2006-04-19 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: The U.S. Department of Energy (DOE) promotes scientific and technological innovation to advance the national, economic, and energy security of the United States. Recognizing the potential of microorganisms to offer new energy alternatives and remediate environmental contamination, DOE initiated the Genomes to Life program, now called Genomics: GTL, in 2000. The program aims to develop a predictive understanding of microbial systems that can be used to engineer systems for bioenergy production and environmental remediation, and to understand carbon cycling and sequestration. This report provides an evaluation of the program and its infrastructure plan. Overall, the report finds that GTL's research has resulted in and promises to deliver many more scientific advancements that contribute to the achievement of DOE's goals. However, the DOE's current plan for building four independent facilities for protein production, molecular imaging, proteome analysis, and systems biology sequentially may not be the most cost-effective, efficient, and scientifically optimal way to provide this infrastructure. As an alternative, the report suggests constructing up to four institute-like facilities, each of which integrates the capabilities of all four of the originally planned facility types and focuses on one or two of DOE's mission goals. The alternative infrastructure plan could have an especially high ratio of scientific benefit to cost because the need for technology will be directly tied to the biology goals of the program.

Download or read book Gene Quantification written by Francois Ferre and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geneticists and molecular biologists have been interested in quantifying genes and their products for many years and for various reasons (Bishop, 1974). Early molecular methods were based on molecular hybridization, and were devised shortly after Marmur and Doty (1961) first showed that denaturation of the double helix could be reversed - that the process of molecular reassociation was exquisitely sequence dependent. Gillespie and Spiegelman (1965) developed a way of using the method to titrate the number of copies of a probe within a target sequence in which the target sequence was fixed to a membrane support prior to hybridization with the probe - typically a RNA. Thus, this was a precursor to many of the methods still in use, and indeed under development, today. Early examples of the application of these methods included the measurement of the copy numbers in gene families such as the ribosomal genes and the immunoglo bulin family. Amplification of genes in tumors and in response to drug treatment was discovered by this method. In the same period, methods were invented for estimating gene num bers based on the kinetics of the reassociation process - the so-called Cot analysis. This method, which exploits the dependence of the rate of reassociation on the concentration of the two strands, revealed the presence of repeated sequences in the DNA of higher eukaryotes (Britten and Kohne, 1968). An adaptation to RNA, Rot analysis (Melli and Bishop, 1969), was used to measure the abundance of RNAs in a mixed population.

Download or read book Computational Molecular Evolution written by Ziheng Yang and published by Oxford University Press, USA. This book was released on 2006-10-05 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes.

Download or read book Scientific Frontiers in Developmental Toxicology and Risk Assessment written by National Research Council and published by National Academies Press. This book was released on 2000-12-21 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific Frontiers in Developmental Toxicology and Risk Assessment reviews advances made during the last 10-15 years in fields such as developmental biology, molecular biology, and genetics. It describes a novel approach for how these advances might be used in combination with existing methodologies to further the understanding of mechanisms of developmental toxicity, to improve the assessment of chemicals for their ability to cause developmental toxicity, and to improve risk assessment for developmental defects. For example, based on the recent advances, even the smallest, simplest laboratory animals such as the fruit fly, roundworm, and zebrafish might be able to serve as developmental toxicological models for human biological systems. Use of such organisms might allow for rapid and inexpensive testing of large numbers of chemicals for their potential to cause developmental toxicity; presently, there are little or no developmental toxicity data available for the majority of natural and manufactured chemicals in use. This new approach to developmental toxicology and risk assessment will require simultaneous research on several fronts by experts from multiple scientific disciplines, including developmental toxicologists, developmental biologists, geneticists, epidemiologists, and biostatisticians.