Download or read book Computational Approaches for Inferring Gene Regulatory Networks Based on DNA Microarray Expression Data written by Anshu Man Gupta and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Research on Computational Methodologies in Gene Regulatory Networks written by Das, Sanjoy and published by IGI Global. This book was released on 2009-10-31 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization"--Provided by publisher.
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 Analysis of Microarray Data written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2008-03-17 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents: * Understanding and Preprocessing Microarray Data * Clustering of Microarray Data * Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order * Bilayer Verification Algorithm * Probabilistic Boolean Networks as Models for Gene Regulation * Estimating Transcriptional Regulatory Networks by a Bayesian Network * Analysis of Therapeutic Compound Effects * Statistical Methods for Inference of Genetic Networks and Regulatory Modules * Identification of Genetic Networks by Structural Equations * Predicting Functional Modules Using Microarray and Protein Interaction Data * Integrating Results from Literature Mining and Microarray Experiments to Infer Gene Networks The book is for both, scientists using the technique as well as those developing new analysis techniques.
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 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 Computational Methods for Integrative Inference of Genome scale Gene Regulatory Networks written by Alireza Fotuhi Siahpirani and published by . This book was released on 2019 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inference of transcriptional regulatory networks is an important filed of research in systems biology, and many computational methods have been developed to infer regulatory networks from different types of genomic data. One of the most popular classes of computational network inference methods is expression based network inference. Given the mRNA levels of genes, these methods reconstruct a network between regulatory genes (called transcription factors) and potential target genes that best explains the input data. However, it has been shown that the networks that are inferred only using expression, have low agreement with experimentally validated physical regulatory interactions. In recent years, many methods have been developed to improve the accuracy of these computational methods by incorporating additional data types. In this dissertation, we describe our contributions towards advancing the state of the art in this field. Our first contribution, is developing a prior-based network inference method, MERLIN-P. MERLIN-P uses both expression of genes, and prior knowledge of interactions between regulatory genes and their potential targets, and infers a network that is supported by both expression and prior knowledge. Using a logistic function, MERLIN-P could incorporate and combine multiple sources of prior knowledge. The inferred networks in yeast, outperform state of the art expression based network inference methods, and perform better or at a par with prior based state of the art method. Our second contribution, is developing a method to estimate transcription factor activity from a noisy prior network, NCA+LASSO. Network Component Analysis (NCA), is a computational method that given expression of target genes and a (potentially incomplete and noisy) network structure that describes the connection of regulatory genes to these target genes, estimates unobserved activity of the regulators (transcription factor activities, TFA). It has been shown that using TFA can improve the quality of inferred networks. However, our prior knowledge in new contexts could be incomplete and noisy, and we do not know to what extent presence of noise in input network affects the quality of estimated TFA. We first show how presence of noise in the input prior network can decrease the quality of estimated TFA, and then show that by adding a regularization term, we can improve the quality of the estimated TFA. We show that using estimated TFA instead of just expression of TFs in network inference, improves the agreement of inferred networks to experimentally validated physical interactions, for all state of the art methods, including MERLIN-P. Our final contribution, is developing a multi-task inference method, Dynamic Regulatory Module Network (DRMN), that simultaneously infers regulatory networks for related cell lines, while taking into account the expected similarity of the cell lines. Many biological contexts are hierarchically related, and leveraging the similarity of these contexts could help us infer more accurate regulatory programs in each context. However, the small number of measurements in each context makes the inference of regulatory networks challenging. By inferring regulatory programs at module level (groups of co-expressed genes), DRMN is able to handle the small number of measurements, while the use of multi-task learning allows for incorporation of hierarchical relationship of contexts. DRMN first infers modules of co-expressed genes in each cell line, then infers a regulatory network for each module, and iteratively updates the inferred modules to reflect both co-expression and co-regulation, and updates the inferred networks to reflect the updated modules. We assess the accuracy of the inferred networks by predicting the expression on hold out genes, and show that the resulting modules and networks, provide insight into the process of differentiation between these related cell lines. For all the developed methods, we validate our results by comparing to known experimentally validated networks, and show that our results provide useful insight into the biological processes under consideration. Specifically, in chapter 2, we evaluated our inferred networks based on both network structure and predictive power, identified TFs that all tested methods fail to recover their target sets, and explored potential reasons that can explain this failure. Additionally, we used our method to infer stress specific networks, and evaluated predictions using stress specific knock-down experiments. In chapter 3, we evaluated our inferred networks based on both network structure and predictive power, and furthermore used our inferred networks to identify potential regulators that could be important for pluripotency state in mESC. We tested the effect of these regulators using shRNA experiments, and experimentally validated some of their predicted targets. Finally, in chapter 4, we evaluated our inferred models based on their predictive power and ability to predict gene expression in hold out data.
Download or read book Emerging Research in the Analysis and Modeling of Gene Regulatory Networks written by Ivanov, Ivan V. and published by IGI Global. This book was released on 2016-06-06 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: While technological advancements have been critical in allowing researchers to obtain more and better quality data about cellular processes and signals, the design and practical application of computational models of genomic regulation continues to be a challenge. Emerging Research in the Analysis and Modeling of Gene Regulatory Networks presents a compilation of recent and emerging research topics addressing the design and use of technology in the study and simulation of genomic regulation. Exploring both theoretical and practical topics, this publication is an essential reference source for students, professionals, and researchers working in the fields of genomics, molecular biology, bioinformatics, and drug development.
Download or read book Biomolecular Networks written by Luonan Chen and published by John Wiley & Sons. This book was released on 2009-06-29 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach. Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods: GENE NETWORKS—Transcriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks PROTEIN INTERACTION NETWORKS—Prediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function METABOLIC NETWORKS AND SIGNALING NETWORKS—Analysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends In addition to theoretical results and methods, many computational software tools are referenced and available from the authors' Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics.
Download or read book Inferring Gene Regulatory Networks from Expression Data Using Ensemble Methods written by Janusz Slawek and published by . This book was released on 2014 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput technologies for measuring gene expression made inferring of the genome-wide Gene Regulatory Networks an active field of research. Reverse-engineering of systems of transcriptional regulations became an important challenge in molecular and computational biology. Because such systems model dependencies between genes, they are important in understanding of cell behavior, and can potentially turn observed expression data into the new biological knowledge and practical applications. In this dissertation we introduce a set of algorithms, which infer networks of transcriptional regulations from variety of expression profiles with superior accuracy compared to the state-of-the-art techniques. The proposed methods make use of ensembles of trees, which became popular in many scientific fields, including genetics and bioinformatics. However, originally they were motivated from the perspective of classification, regression, and feature selection theory. In this study we exploit their relative variable importance measure as an indication of the presence or absence of a regulatory interaction between genes. We further analyze their predictions on a set of the universally recognized benchmark expression data sets, and achieve favorable results in compare with the state-of-the-art algorithms.
Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.
Download or read book Computational Methods for Analysis and Modeling of Time course Gene Expression Data written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Genes encode proteins, some of which in turn regulate other genes. Such interactions make up gene regulatory relationships or (dynamic) gene regulatory networks. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data may provide a snapshot of most (if not all) of the interesting genes and may lead to a better understanding gene regulatory relationships and networks. However, inferring either gene regulatory relationships or networks puts a high demand on powerful computational methods that are capable of sufficiently mining the large quantities of time-course gene expression data, while reducing the complexity of the data to make them comprehensible. This dissertation presents several computational methods for inferring gene regulatory relationships and gene regulatory networks from time-course gene expression. These methods are the result of the authors doctoral study. Cluster analysis plays an important role for inferring gene regulatory relationships, for example, uncovering new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. Two dynamic model-based clustering methods, namely the Markov chain model (MCM)-based clustering and the autoregressive model (ARM)-based clustering, are developed for time-course gene expression data. However, gene regulatory relationships based on cluster analysis are static and thus do not describe the dynamic evolution of gene expression over an observation period. The gene regulatory network is believed to be a time-varying system. Consequently, a state-space model for dynamic gene regulatory networks from time-course gene expression data is developed. To account for the complex time-delayed relationships in gene regulatory networks, the state space model is extended to.
Download or read book Systems Genetics written by Florian Markowetz and published by Cambridge University Press. This book was released on 2015-07-02 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whereas genetic studies have traditionally focused on explaining heritance of single traits and their phenotypes, recent technological advances have made it possible to comprehensively dissect the genetic architecture of complex traits and quantify how genes interact to shape phenotypes. This exciting new area has been termed systems genetics and is born out of a synthesis of multiple fields, integrating a range of approaches and exploiting our increased ability to obtain quantitative and detailed measurements on a broad spectrum of phenotypes. Gathering the contributions of leading scientists, both computational and experimental, this book shows how experimental perturbations can help us to understand the link between genotype and phenotype. A snapshot of current research activity and state-of-the-art approaches to systems genetics are provided, including work from model organisms such as Saccharomyces cerevisiae and Drosophila melanogaster, as well as from human studies.
Download or read book Unsupervised Gene Regulatory Network Inference on Microarray Data written by Nidhi Radia and published by . This book was released on 2015 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Obtaining gene regulatory networks (GRNs) from expression data is a challenging and crucial task. Many computational methods and algorithms have been developed to infer gene networks for gene expression data, which are usually obtained from microarray experiments. A gene network is a method to depict the relation among clusters of genes. To infer gene networks, the unsupervised method is used in this study. The two types of data used are time-series data and steady-state data. The data is analyzed using various tools containing different algorithms and concepts. GRNs from time-series data tools are obtained using the Time-delayed Algorithm for the Reconstruction of Accurate Cellular Networks (TD-ARACNe), the Bayesian Network Inference with Java Objects (BANJO), and causality. For steady-state data tools such as ARACNe, Gene Network Inference with Ensemble of trees (GENIE3), Context Likelihood or Relatedness Network (CLR), and Maximum Relevance Minimum Redundancy (MRNET) are used. The performance of time-series data as well as steady-state data based tool algorithms is compared by calculating their accuracy. The accuracy is calculated by comparing gene interactions between predicted and true networks. From the experimental studies it was found that the TD-ARACNe gives the highest accuracy on time-series gene expression data while for steady-state data, the ARACNe tool gives the highest accuracy. Overall, these analyses suggest that the suitability of the tools depends on the types of gene expression data available.
Download or read book Modeling and Learning Realistic Genetic Interactions Using Dynamic Bayesian Network and Information Theory written by Nizamul Morshed and published by . This book was released on 2013 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deciphering genetic interactions is of fundamental importance in computational systems biology, with wide applications in a number of other associated areas. Realistic modeling of these interactions poses novel challenges while dealing with the problem. Further, learning these interactions using computational methods becomes increasingly complex with the adoption of advanced and more realistic modeling techniques. In this thesis, we propose methods to address this challenge using a graphical model having sound probabilistic underpinnings, commonly known as dynamic Bayesian networks. Inference of genetic interactions is usually carried out using DNA microarray data. This data provides snapshots of mRNA expression levels of a large number of genes from a single experiment. However, the number of samples from such experiments is small, and additionally, they contain missing values and noise. Bayesian networks are considered as one of the most promising ways by which these issues can be tackled. However, traditional Bayesian networks have their own limitations; for example, they neither take time information into account nor can they capture feedback. Further, accurate determination of the direction of regulation requires a significant number of tests to be performed. Dynamic Bayesian networks (DBN) are extensions of Bayesian networks that can effectively address these limitations. In this thesis, we develop novel techniques for gene regulatory network reconstruction using DBN based modeling approach. We start with a basic DBN based model, and improve it so that it can represent and model both instantaneous and time-delayed genetic interactions. Initially, we aim to detect the occurrence of instantaneous and single-step time-delayed interactions, and subsequently this approach is further extended to model the instantaneous and multi-step time-delayed interactions. This approach of modeling both instantaneous and multi-step time-delayed genetic interactions is superior to traditional DBN based GRN reconstruction techniques, where only the time delayed interactions are learnt.%, thereby advancing the state of the art for modeling genetic regulations using DBNs.In addition to modeling interactions, one needs a learning mechanism for inferring genetic interactions. To facilitate detection of nonlinear gene to gene interactions (in addition to linear interactions), which are prevalent in all genetic networks, we propose using well known properties, including fundamental results related to information theoretic measures for testing conditional independence relations in a DBN. This enables us to formulate efficient learning techniques for reconstructing GRNs. Using these theoretical underpinnings, we first implement simple hill-climbing techniques that enable detection of various types of interactions among genes. Subsequently, we use these results to devise novel score and search based evolutionary computation techniques, which can effectively explore a significantly larger search space. We carry out investigations using both synthetic networks as well as real-life networks. For real-life network study, we use four different microarray data sources, covering three organisms, namely, yeast, E. coli and cyanobacteria. We use networks of varying sizes, ranging from five-gene small networks (yeast) to large scale networks of cyanobacteria (730 genes). The evaluation of the performance is carried out using four widely used performance measures. For some networks where we do not have sufficient information for calculating these performance measures, we use literature mining for performing comparative evaluations of the proposed approaches. For the large scale network of cyanobacteria, we use gene ontology (GO) based analysis of gene functionalities, in addition to degree distribution analysis of the inferred network.Due to the inherent difficulties associated with inferring GRNs using DNA microarray data, it is often supplemented by other sources of data; for example, genomic data and protein-protein interaction data. In this thesis, we propose a framework that jointly learns the structure of a GRN and a protein-protein interaction network (PPIN). Using this process, the GRN reconstruction technique can effectively make use of the vast wealth of knowledge available from these external sources of data. This knowledge is fed to the GRN reconstruction process probabilistically, thereby enabling it to weigh each different data source according to the reliability of that source. The approach is applied on yeast networks where four different interaction data sources and a number of genomic data sources are used. Together with the novel modeling and learning techniques proposed in this thesis, the probabilistic integration of different types of knowledge sources and the co-learning of GRN with PPIN represents a significant step towards the reconstruction of GRNs using DBNs.
Download or read book Inferring Gene Regulatory Network Based on Bayesian Network written by Ali Ebrahimi and published by LAP Lambert Academic Publishing. This book was released on 2014-02 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today the comprehension of gene regulatory networks and understanding the regulatory procedures in a cell in the gene level, is an important goal in computational biology. The modeling of gene networks can be used in many different areas such as the discovery of new drugs, reducing side effects of treatment methods, better recognition of genetic disorders, choosing candidates for gene treatment, comparison and scrutiny of gene expressions that have unknown operations and to obtain ideas regarding their operations, that's why researchers have made a lot of attempts to this thing.
Download or read book Drosophila Eye Development written by Kevin Moses and published by Springer Science & Business Media. This book was released on 2002-03-12 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
