Download or read book Metabolic Modeling based Tools for Integrative Microbiome Data Analysis written by Cecilia Anne Buuck Noecker and published by . This book was released on 2019 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex communities of microbes reside in and on humans, where they closely interact with their hosts by performing a massively diverse array of metabolic reactions. Genomic and metabolomic technologies can now describe both the taxonomic profile of these communities and their metabolic products in unprecedented detail. By measuring both microbial composition and metabolite phenotypes from the same samples, and using the resulting datasets to make and evaluate predictions on the links between microbes and metabolites, it may be possible to infer and characterize metabolic mechanisms occurring in complex natural communities. However, relatively few computational analysis tools exist to integrate and make sense of such “microbiome-metabolome” datasets. In this dissertation, I describe the development and application of methods that use these datasets and reference databases to identify and evaluate relationships between microbes and metabolites. After introducing the current state of knowledge and available tools in the study of how microbial metabolites impact human health and disease, I present an initial framework for integrating microbiome and metabolomics datasets using metabolic modeling. I demonstrate its ability to predict and explain metabolic shifts in bacterial vaginosis, and further illustrate its application in two case studies, deciphering diet-microbiome interactions in mice and characterizing metabolic mechanisms in the microbiota of children with autism spectrum disorder. In order to compare this approach with alternatives and gain a better understanding of the limiting factors in microbiome-metabolome data analysis, I next describe a comprehensive framework for defining gold-standard mechanistic links between microbes and metabolites and using simulations to evaluate and compare our ability to recover them across different datasets and analysis methods. Finally, informed by the previous applications and evaluations, I introduce MIMOSA2, an updated software tool for inferring mechanistic links from microbiome-metabolome datasets. Together, this work reinforces and advances the utility of metabolic modeling for the analysis and interpretation of large-scale microbiome-metabolome studies.

Download or read book Integrated Modeling of Phototrophic Metabolism Leveraging Multi Omics Datasets written by Debolina Sarkar and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid progress in high-throughput experimental technologies has enabled generation of large-scale systems biology datasets. These span all biological hierarchies from genomics describing the genetic make-up, transcriptomics and proteomics at the gene and enzyme expression level, metabolomics that helps quantify the amount and nature of resultant biomolecules, to finally phenomics that describes the overall traits of an individual. This veritable data deluge necessitates algorithmic and computational advances that can leverage multi-omics integration, in order to facilitate the analysis of complex systems and extract meaningful insights. Flux balance analysis (FBA) using genome-scale metabolic (GSM) models provide an advantageous platform for doing so as these models are (relatively) parameter-free, can be constructed using the annotated genome alone and simulated in linear time offering scale-up benefits. GSMs model a network view of metabolism, wherein metabolites are cast as nodes in a graph linked via edges representing all possible biochemical conversions occurring within an organism. In Chapter 1, we present an overview of constraints-based analysis of metabolic networks, including the reconstruction of GSM models, their use within an optimization-based scheme such as FBA, and the various applications of such models. Next, we describe the extension of metabolic modeling frameworks, originally designed for microbial systems, to the study of plants. This is accompanied by its own set of challenges, such as accurately capturing the division of roles between the various tissue and organ systems and dealing with systematic biases that are typically associated with poorly annotated non-model systems. Finally, we explore how the incorporation of new data types, modeling schemes, and computational tools have impacted FBA by helping increase its predictive power and scope. FBA has proven to be quite adept at describing aggregated metabolite flows, i.e., providing a snapshot of metabolism as averaged over the entire growth cycle. However, it is also time invariant, and thus does not accommodate temporally varying cell processes such as sequestering different biomass components at various time points in a growth cycle However, we know from experiments that many organisms including cyanobacteria have a lifestyle that is heavily tailored around light availability and thus show metabolic oscillations. In Chapter 2, we present a framework called CycleSyn that augments FBA by accounting for such temporal trends. CycleSyn discretizes a growth cycle into individual time periods (called Time Point Models or TPMs), each described by its own GSM model. The flow of metabolites across TPMs is allowed while inventorying metabolite levels and only allowing for the utilization of currently or previously produced compounds. Additional time-dependent constraints can also be imposed to capture the cyclic nature of cellular processes. CycleSyn was used to develop a diurnal FBA model of Synechocystis sp. PCC 6803 metabolism. Predicted flux and metabolite pools were in line with published studies, paving the way for constructing time-resolved GSM models. Additionally, the metabolic reorganization that would be required to enable Synechocystis PCC 6803 to fix nitrogen by temporally separating it from photosynthesis was also explored. Similar to modeling multiple metabolic models at once in CycleSyn, in Chapter 3 we extend this to modeling multiple organisms together as in a community, so as to discern the underlying interactions. This community comprised a genetically streamlined unicellular cyanobacterium called Candidatus Atelocyanobacterium thalassa (or UCYN-A) living in a symbiosis with a phototrophic microalga. We used metabolic modeling to glean insights into UCYN-A's unique physiology and metabolic processes governing the symbiotic association. To this end, we developed an optimization-based framework that infers all possible trophic scenarios consistent with the observed data. Possible mechanisms employed by UCYN-A to accommodate diazotrophy with daytime carbon fixation by the host (i.e., two mutually incompatible processes) were also elucidated. We found that the metabolic functions of the two constituents, and UCYN-A's streamlined genome is optimized to support maximal nitrogen fixation flux, alluding that this symbiosis is as close to being a functional 'nitroplast' as any observed till date. We envision that the developed framework using UCYN-A and its algal host will be used as a roadmap and motivate the study of similarly unique microbial systems in the future. Understanding how genomic mutations impact the overall phenotype of an organism has been a focus of efforts aimed at improving growth yield, determining genetic markers governing a trait, and understanding adaptive processes. This has been performed conventionally using genome-wide association studies, which seek to identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. In Chapter 4, we propose a complementary tool called SNPeffect that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect was used to explain differential growth rate and metabolite accumulation in Arabidopsis and poplar as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicated that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally-characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino-acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition. Thus far, we have developed computational frameworks that examine how the metabolism of an organism dictates its total phenotype and interactions with other organisms in a community. In Chapter 5, we take the next step by examining ways in which an organism can impact its host, specifically how the infant gut microbiome is shaped. Fecal samples from newborn infants showed that gut bacteria is detectable by 16 h after birth. However, analysis of the microbiome, proteome, and metabolome data did not suggest a single genomic signature for neonatal gut colonization. Using flux balance modeling, we found E. coli to be the most common early colonizer. The appearance of bacteria was associated with decreased levels of free amino acids and an increase in products of bacterial fermentation, primarily acetate and succinate. Among all the microbial species found, these observations were only consistent with E. coli growing under anaerobic conditions using amino acid fermentation to support maximal ATP yield. These results provide a deep characterization of the first microbes in the human gut and show how the biochemical environment is altered by their appearance. Finally, in Chapter 6, we conclude with our efforts to develop computational frameworks enabling the integration of heterogeneous datasets within constraints-based optimization. We discuss current challenges associated with such modeling frameworks and their uses, and finally present future perspectives for augmenting these models with the incorporation of diverse data types, multi-scale modeling, cross-cutting applications.

Download or read book An Integrated Analysis of Microbiomes and Metabolomics written by Yinglin Xia and published by American Chemical Society. This book was released on 2022-03-25 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because the microbial community is dynamic, an individual’s microbiota at a given time is varied, and many factors, including age, host genetics, diet, and the local environment, significantly change the microbiota. Thus, microbiome researchers have naturally expanded their research to look for insights into the interaction of the microbiome with other “omics”. Metabolites (small molecules) are the intermediate or end products of metabolism. Metabolites have various functions. The microbial-derived metabolites play an important role in the function of the microbiome. Thus, the advancement in microbiome studies is becoming particularly critical for the integration of microbial DNA sequencing data with other omics data, especially microbiome-metabolomics integration.

Download or read book Metagenomic Systems Biology written by Shailza Singh and published by Springer Nature. This book was released on 2020-12-07 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book serves as an amalgamation of knowledge and principles used in the area of systems and synthetic biology, and targets inter-disciplinary research groups. The readers from diversified areas would be benefited by the valuable resources and information available in one book. Microbiome projects with efficient data handling can fuel progress in the area of microbial synthetic biology by providing a ready to use plug and play chassis. Advances in gene editing technology such as the use of tailor made synthetic transcription factors will further enhance the availability of synthetic devices to be applied in the fields of environment, agriculture and health. The different chapters of the book reviews a broad range of topics, including food microbiome in ecology, use of microbiome in personalized medicine, machine learning in biomedicine. The book also describes ways to harness and exploit the incredible amounts of genomic data. The book is not only limited to medicine but also caters to the needs of environmentalists, biochemical engineers etc. It will be of interest to advanced students and researchers in life sciences, computational biology, microbiology and other inter-disciplinary areas.

Download or read book MetaboTools written by and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.

Download or read book Systems Biology written by Bernhard Palsson and published by Cambridge University Press. This book was released on 2015-01-26 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive single-authored textbook on genome-scale models and the bottom-up approach to systems biology.

Download or read book Integrative Bioinformatics written by Ming Chen and published by Springer Nature. This book was released on 2022-04-15 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of the history of integrative bioinformatics and the actual situation and the relevant tools. Subjects cover the essential topics, basic introductions, and latest developments; biological data integration and manipulation; modeling and simulation of networks; as well as a number of applications of integrative bioinformatics. It aims to provide basic introduction of biological information systems and guidance for the computational analysis of systems biology. This book covers a range of issues and methods that unveil a multitude of omics data integration and relevance that integrative bioinformatics has today. It contains a unique compilation of invited and selected articles from the Journal of Integrative Bioinformatics (JIB) and annual meetings of the International Symposium on Integrative Bioinformatics.

Download or read book Principles in Microbiome Engineering written by Matthew W. Chang and published by John Wiley & Sons. This book was released on 2022-05-03 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles in Microbiome Engineering Provides an overview of the techniques and applications insight into the complex composition and interactions of microbiomes Microbiomes, the communities of microorganisms that inhabit specific ecosystems or organisms, can be engineered to modify the structure of microbiota and reestablish ecological balance. In recent years, a better understanding of microbial composition and host-microbe interactions has led to the development of new applications for improving human health and increasing agricultural productivity and quality. Principles in Microbiome Engineering introduces readers to the tools and applications involved in manipulating the composition of a microbial community to improve the function of an eco-system. Covering a range of key topics, this up-to-date volume discusses current research in areas such as microbiome-based therapeutics for human diseases, crop plant breeding, animal husbandry, soil engineering, food and beverage applications, and more. Divided into three sections, the text first describes the critical roles of systems biology, synthetic biology, computer modelling, and machine learning in microbiome engineering. Next, the volume explores various state-of-the-art applications, including cancer immunotherapy and prevention of diseases associated with the human microbiome, followed by a concluding section offering perspectives on the future of microbiome engineering and potential applications. Introduces a variety of applications of microbiome engineering in the fields of medicine, agriculture, and food and beverage products Presents current research into the complex interactions and relationships between microbiomes and biotic and abiotic elements of their environments Examines the use of technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics to advance understanding of microbiomes Discusses the engineering of microbiomes to address human health conditions such as neuro psychiatric disorders and autoimmune and inflammatory diseases Edited and authored by leading researchers in the rapidly evolving field, Principles in Microbiome Engineering is an essential resource for biotechnologists, biochemists, microbiologists, pharmacologists, and practitioners working in the biotechnology and pharmaceutical industries.

Download or read book Statistical Analysis of Microbiome Data written by Somnath Datta and published by Springer Nature. This book was released on 2021-10-27 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.

Download or read book Integrative Omics written by Manish Kumar Gupta and published by Elsevier. This book was released on 2024-05-10 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrative Omics: Concepts, Methodology and Applications provides a holistic and integrated view of defining and applying network approaches, integrative tools, and methods to solve problems for the rationalization of genotype to phenotype relationships. The reference includes a range of chapters in a systemic ‘step by step’ manner, which begins with the basic concepts from Omic to Multi Integrative Omics approaches, followed by their full range of approaches, applications, emerging trends, and future trends. All key areas of Omics are covered including biological databases, sequence alignment, pharmacogenomics, nutrigenomics and microbial omics, integrated omics for Food Science and Identification of genes associated with disease, clinical data integration and data warehousing, translational omics as well as omics technology policy and society research. Integrative Omics: Concepts, Methodology and Applications highlights the recent concepts, methodologies, advancements in technologies and is also well-suited for researchers from both academic and industry background, undergraduate and graduate students who are mainly working in the area of computational systems biology, integrative omics and translational science. The book bridges the gap between biological sciences, physical sciences, computer science, statistics, data science, information technology and mathematics by presenting content specifically dedicated to mathematical models of biological systems. Provides a holistic, integrated view of a defining and applying network approach, integrative tools, and methods to solve problems for rationalization of genotype to phenotype relationships Offers an interdisciplinary approach to Databases, data analytics techniques, biological tools, network construction, analysis, modeling, prediction and simulation of biological systems leading to ‘translational research’, i.e., drug discovery, drug target prediction, and precision medicine Covers worldwide methods, concepts, databases, and tools used in the construction of integrated pathways

Download or read book Metabolic Modeling written by Radhakrishnan Mahadevan and published by Wiley-Interscience. This book was released on 2018-09-24 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book documents the different modeling methods and their successful applications in a wide variety of fields including functional genomics, metabolic engineering, integrative data analysis, and biomedical research. Most importantly, this book focuses on the strengths and limitations of the current modeling approaches and enumerates some of the challenges in extending the metabolic models to a broader range of applications. This book is unique in describing the practical applications of such metabolic modeling approaches with clearly elucidated case studies.

Download or read book Metabolic Pathway Engineering written by Jean F. Challacombe and published by CRC Press. This book was released on 2021-07-14 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metabolic systems engineering combines the tools and approaches of systems biology, synthetic biology, and evolutionary engineering. This book reviews studies on metabolism, from the earliest work of Lavoisier and Buchner to current cutting-edge research in metabolic systems engineering. This technology has been used in bioengineering applications to create high-performing microbes and plants that produce important chemicals, pharmaceuticals, crops, and other natural products. Current applications include optimizing metabolic pathways to enhance degradation of biomass for biofuel production and accelerated processing of environmental waste products and contaminants. The book includes examples to illustrate the applications of this technology in the optimization of metabolic pathways to create robust industrial strains as well as in the engineering of biological processes involving health and diseases of humans, animals, and plants. Written by a seasoned computational biologist with many years of experience in genomics, bioinformatics, and systems biology, this book will appeal to anyone interested in metabolic systems analysis and metabolic pathway engineering.

Download or read book Novel Approaches in Microbiome Analyses and Data Visualization written by Jessica Galloway-Peña and published by Frontiers Media SA. This book was released on 2019-02-06 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing technologies are widely used to study microbial ecology across species and habitats in order to understand the impacts of microbial communities on host health, metabolism, and the environment. Due to the dynamic nature of microbial communities, longitudinal microbiome analyses play an essential role in these types of investigations. Key questions in microbiome studies aim at identifying specific microbial taxa, enterotypes, genes, or metabolites associated with specific outcomes, as well as potential factors that influence microbial communities. However, the characteristics of microbiome data, such as sparsity and skewedness, combined with the nature of data collection, reflected often as uneven sampling or missing data, make commonly employed statistical approaches to handle repeated measures in longitudinal studies inadequate. Therefore, many researchers have begun to investigate methods that could improve incorporating these features when studying clinical, host, metabolic, or environmental associations with longitudinal microbiome data. In addition to the inferential aspect, it is also becoming apparent that visualization of high dimensional data in a way which is both intelligible and comprehensive is another difficult challenge that microbiome researchers face. Visualization is crucial in both the analysis and understanding of metagenomic data. Researchers must create clear graphic representations that give biological insight without being overly complicated. Thus, this Research Topic seeks to both review and provide novels approaches that are being developed to integrate microbiome data and complex metadata into meaningful mathematical, statistical and computational models. We believe this topic is fundamental to understanding the importance of microbial communities and provides a useful reference for other investigators approaching the field.

Download or read book Statistical Analysis of Microbiome Data with R written by Yinglin Xia and published by Springer. This book was released on 2018-10-06 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.

Download or read book Advances in methods and tools for multi omics data analysis written by Ornella Cominetti and published by Frontiers Media SA. This book was released on 2023-05-12 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi Omics Analysis of the Human Microbiome written by Indra Mani and published by Springer Nature. This book was released on with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Comprehensive Foodomics written by and published by Elsevier. This book was released on 2020-11-12 with total page 2444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Foodomics, Three Volume Set offers a definitive collection of over 150 articles that provide researchers with innovative answers to crucial questions relating to food quality, safety and its vital and complex links to our health. Topics covered include transcriptomics, proteomics, metabolomics, genomics, green foodomics, epigenetics and noncoding RNA, food safety, food bioactivity and health, food quality and traceability, data treatment and systems biology. Logically structured into 10 focused sections, each article is authored by world leading scientists who cover the whole breadth of Omics and related technologies, including the latest advances and applications. By bringing all this information together in an easily navigable reference, food scientists and nutritionists in both academia and industry will find it the perfect, modern day compendium for frequent reference. List of sections and Section Editors: Genomics - Olivia McAuliffe, Dept of Food Biosciences, Moorepark, Fermoy, Co. Cork, Ireland Epigenetics & Noncoding RNA - Juan Cui, Department of Computer Science & Engineering, University of Nebraska-Lincoln, Lincoln, NE Transcriptomics - Robert Henry, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Australia Proteomics - Jens Brockmeyer, Institute of Biochemistry and Technical Biochemistry, University Stuttgart, Germany Metabolomics - Philippe Schmitt-Kopplin, Research Unit Analytical BioGeoChemistry, Neuherberg, Germany Omics data treatment, System Biology and Foodomics - Carlos Leon Canseco, Visiting Professor, Biomedical Engineering, Universidad Carlos III de Madrid Green Foodomics - Elena Ibanez, Foodomics Lab, CIAL, CSIC, Madrid, Spain Food safety and Foodomics - Djuro Josic, Professor Medicine (Research) Warren Alpert Medical School, Brown University, Providence, RI, USA & Sandra Kraljevic Pavelic, University of Rijeka, Department of Biotechnology, Rijeka, Croatia Food Quality, Traceability and Foodomics - Daniel Cozzolino, Centre for Nutrition and Food Sciences, The University of Queensland, Queensland, Australia Food Bioactivity, Health and Foodomics - Miguel Herrero, Department of Bioactivity and Food Analysis, Foodomics Lab, CIAL, CSIC, Madrid, Spain Brings all relevant foodomics information together in one place, offering readers a ‘one-stop,’ comprehensive resource for access to a wealth of information Includes articles written by academics and practitioners from various fields and regions Provides an ideal resource for students, researchers and professionals who need to find relevant information quickly and easily Includes content from high quality authors from across the globe