Systematic Approaches for Modelling and Visualising Responses to Perturbation of Transcriptional Regulatory Networks
Systematic Approaches for Modelling and Visualising Responses to Perturbation of Transcriptional Regulatory Networks

Author: Nam Shik Han

Publisher:

Published: 2013

Total Pages:

ISBN-13:

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One of the greatest challenges in modern biology is to understand quantitatively the mechanisms underlying messenger Ribonucleic acid (mRNA) transcription within the cell. To this end, integrated functional genomics attempts to use the vast wealth of data produced by modern large scale genomic projects to understand how the genome is deployed to create a diversity of tissues and species. The expression levels of tens or hundreds of thousands genes are profiled at multiple time points or different experimental conditions in the genomic projects. The profiling results are deposited in large scale quantitative data files that are not possible to analyse without systematic computational methods. In particular, it is much more difficult to experimentally measure the concentration level of transcription factor proteins and their affinity for the promoter region of genes, while it is relatively easy to measure the result of transcription using experimental techniques such as microarrays. In the absence of such biological experiments, it becomes necessary to use in silico techniques to determine the transcription factor regulatory activities given existing gene expression profile data. It therefore presents significant challenges and opportunities to the computer science community. This PhD Project made use of one such in silico technique to determine the differences (if any) in transcription factor regulatory activities of different experimental conditions and time points. The research aim of the Project was to understand the transcriptional regulatory mechanism that controls the sophisticated process of gene expression in cells. In particular, differences in the downstream signalling from which transcription factors can play a role in predisposition to diseases such as Parasitic disease, Cancer, and Neuroendocrine disease. To address this question I have had access to large integrated genomics datasets generated in studies on parasitic disease, lung cancer, and endocrine (hormone) disease. The current state-of-the-art takes existing knowledge and asks "How do these data relate to what we already know?" By applying machine learning approaches the project explored the role that such data can play in uncovering new biological knowledge.

Computational Modeling Of Gene Regulatory Networks - A Primer
Computational Modeling Of Gene Regulatory Networks - A Primer

Author: Hamid Bolouri

Publisher: World Scientific Publishing Company

Published: 2008-08-13

Total Pages: 341

ISBN-13: 1848168187

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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

Development and Applications of Integrated Metabolic and Transcriptional Regulatory Network Models
Development and Applications of Integrated Metabolic and Transcriptional Regulatory Network Models

Author:

Publisher:

Published: 2012

Total Pages: 376

ISBN-13:

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With increasing number of sequenced genomes and high-throughput omics datasets, computational models of metabolic and regulatory networks have been rapidly developed and utilized to study and engineer biological systems. Metabolic and regulatory networks are reconstructed by integrating various experimental data and existing knowledge, and represented in a mathematical format using different computational methods. Constraint-based models, using physicochemical constraints to define feasible behaviors, have significantly contributed to improving our understanding of metabolism and regulation as well as rational engineering of biological systems for many purposes. This work describes computational approaches to integrate, refine, and reconstruct metabolic and transcriptional regulatory network models, and approaches to redesign and predict the behaviors of such networks for metabolic engineering purposes. A systematic method to integrate metabolic and transcriptional regulatory networks was first described, which was used to develop an automated approach for refining such network models using high-throughput growth phenotype data. The approach (GeneForce) was used to refine integrated models of Escherichia coli and Salmonella typhimurium, which significantly improved the accuracy of growth phenotype predictions. Using an improved formulation of transcriptional regulatory networks which accounts for transcription factor conformations and transcription unit architecture, a network reconstruction algorithm was developed to identify regulatory interactions that are most consistent with high-throughput gene expression and growth phenotype data. A computational strain design approach using integrated metabolic and transcriptional network models (OptORF) was also developed to identify metabolic engineering strategies consisting of metabolic and regulatory perturbations. To facilitate the computational strain design process, mixed-integer programming solution techniques were developed, which significantly improved the performances of bi-level strain design approaches. This enabled the development of two new strain design approaches; the first approach (SimOptStrain) simultaneously considers gene deletions and non-native reaction additions, and the second approach (BiMOMA) considers the behavior of perturbed strains before adaptive evolution. Finally, an accurate predictive tool for investigating metabolic and regulatory responses to genetic and environmental perturbations (RELATCH) was developed using the concept of relative optimality which considers relative flux changes from a reference state. The computational approaches described in this work provide useful tools for studying and engineering metabolic and regulatory networks.

Systems Biology of Transcription Regulation
Systems Biology of Transcription Regulation

Author: Ekaterina Shelest

Publisher: Frontiers Media SA

Published: 2016-09-09

Total Pages: 191

ISBN-13: 2889199673

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Transcription regulation is a complex process that can be considered and investigated from different perspectives. Traditionally and due to technical reasons (including the evolution of our understanding of the underlying processes) the main focus of the research was made on the regulation of expression through transcription factors (TFs), the proteins directly binding to DNA. On the other hand, intensive research is going on in the field of chromatin structure, remodeling and its involvement in the regulation. Whatever direction we select, we can speak about several levels of regulation. For instance, concentrating on TFs, we should consider multiple regulatory layers, starting with signaling pathways and ending up with the TF binding sites in the promoters and other regulatory regions. However, it is obvious that the TF regulation, also including the upstream processes, represents a modest portion of all processes leading to gene expression. For more comprehensive description of the gene regulation, we need a systematic and holistic view, which brings us to the importance of systems biology approaches. Advances in methodology, especially in high-throughput methods, result in an ever-growing mass of data, which in many cases is still waiting for appropriate consideration. Moreover, the accumulation of data is going faster than the development of algorithms for their systematic evaluation. Data and methods integration is indispensable for the acquiring a systematic as well as a systemic view. In addition to the huge amount of molecular or genetic components of a biological system, the even larger number of their interactions constitutes the enormous complexity of processes occurring in a living cell (organ, organism). In systems biology, these interactions are represented by networks. Transcriptional or, more generally, gene regulatory networks are being generated from experimental ChIPseq data, by reverse engineering from transcriptomics data, or from computational predictions of transcription factor (TF) – target gene relations. While transcriptional networks are now available for many biological systems, mathematical models to simulate their dynamic behavior have been successfully developed for metabolic and, to some extent, for signaling networks, but relatively rarely for gene regulatory networks. Systems biology approaches provide new perspectives that raise new questions. Some of them address methodological problems, others arise from the newly obtained understanding of the data. These open questions and problems are also a subject of this Research Topic.

Modeling Transcriptional Regulation
Modeling Transcriptional Regulation

Author: Shahid M. Mukhtar

Publisher:

Published: 2021

Total Pages: 307

ISBN-13: 9781071615348

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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.

An Integrated Approach to Reconstructing Genome-scale Transcriptional Regulatory Networks
An Integrated Approach to Reconstructing Genome-scale Transcriptional Regulatory Networks

Author:

Publisher:

Published: 2015

Total Pages:

ISBN-13:

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Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied [gamma]-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the [alpha]-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating comparative genomics of closely related organisms with gene expression data to assemble large-scale TRN models with high-quality predictions.

An Integrated Experimental/computational Approach to Infer Gene Regulatory Networks
An Integrated Experimental/computational Approach to Infer Gene Regulatory Networks

Author: David Ronald Lorenz

Publisher:

Published: 2009

Total Pages: 408

ISBN-13:

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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.

Gene Network Inference
Gene Network Inference

Author: Alberto Fuente

Publisher: Springer Science & Business Media

Published: 2014-01-03

Total Pages: 135

ISBN-13: 3642451616

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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.

Structured Modeling of Mammalian Transcription Networks
Structured Modeling of Mammalian Transcription Networks

Author:

Publisher:

Published: 2005

Total Pages:

ISBN-13: 9780542228834

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High-throughput gene expression measurement technologies and complete mammalian genome sequences are resources that may be instrumental in achieving the goal of systems level understanding of neuronal behavior. Initial steps towards utilizing these resources to meet this goal were taken in the present work. Systems engineering techniques were employed to address the problem of identifying from experimental data gene regulatory networks in mammalian systems. As revealed by simulation studies, the scale and complexity of biological systems make it virtually impossible to make progress on this problem through conventional application of classical system identification methods. An alternative approach, the structured modeling of transcription networks, was developed that renders the problem tractable by integrating dynamic modeling with biological constraints and the utilization of multiple data sets. The aforementioned simulation studies, the development of the structured modeling approach, and the application of the structured approach to a specific neuronal system comprise the greater part of this dissertation. Additionally, analyses of two specific biological system computational models were performed that generate hypotheses about the cellular functional relevance of the network structures of these systems. Simulation studies of gene regulatory network computational models were employed to gain insight into the challenges of gene regulatory network identification. The results of identifiability analyses, model identification studies, and network identification studies of gene network models revealed the inherent difficulty in approaching gene network identification as a straightforward system identification problem, particularly when high throughput measurements of gene expression (microarrays) are relied upon as the sole data source. It will be very difficult in general to identify gene network structure solely through modeling microarray data. This is primarily because of the high likelihood that many incorrect networks will fit the data at least as well as the correct network, rendering the determination of the correct network in the experimental setting very difficult. Furthermore, even in the ideal case where the network structure is known in advance, successful determination of model parameters may require high quality data that is difficult to obtain experimentally, involving complex perturbations and being contaminated by very little noise. A structured approach to gene network identification was developed to overcome the challenges illuminated by the simulation studies. Making two biologically motivated assumptions permitted derivation of candidate regulatory network structures by searching co-regulated gene group promoters for oven-represented transcriptional regulatory elements. These assumptions were (1) that gene regulation occurs predominantly through transcription initiation regulation, and (2) that genes with similar patterns of expression are jointly regulated ("co-expression implies co-regulation"). Utilizing the candidate network structures to define regulatory interactions, system identification techniques were used to fit dynamic models linking regulating transcription factors to target genes. Successful model fits were strongly indicative of regulatory interactions, being supported by multiple data types and being consistent with biological constraints. This structured modeling approach was successfully applied to data from the yeast cell cycle as a case study. Consideration of gene to gene variation in dynamic parameters revealed experimental conditions for which co-regulated mammalian genes will not necessarily be co-expressed, invalidating assumption (2), above. A simple technique to partially account for this variation was described. (Abstract shortened by UMI.).