Constraint-based models predict metabolic and associated cellular functions
TL;DR: This work states that an increasing number of studies have recently combined models with high-throughput data sets for prospective experimentation, leading to validation of increasingly important and relevant biological predictions.
Abstract: The prediction of cellular function from a genotype is a fundamental goal in biology. For metabolism, constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework that enables a mechanistic description of metabolic physiology. The use of constraint-based approaches has evolved over ~30 years, and an increasing number of studies have recently combined models with high-throughput data sets for prospective experimentation. These studies have led to validation of increasingly important and relevant biological predictions. As reviewed here, these recent successes have tangible implications in the fields of microbial evolution, interaction networks, genetic engineering and drug discovery.
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TL;DR: This review discusses the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates, proteins, plant polyphenols, bile acids, and vitamins, and the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism.
Abstract: The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays.
1,294 citations
Cites background from "Constraint-based models predict met..."
..., biomass production, metabolic fluxes through the network that satisfy this objective are predicted [146]....
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University of Luxembourg1, University of Chile2, University of California, San Diego3, European Bioinformatics Institute4, Pennsylvania State University5, Georgia Institute of Technology6, University of Navarra7, University of Latvia8, PSL Research University9, Stanford University10, University of Michigan11, Utah State University12, Imperial College London13, University of Alicante14, California Institute of Technology15, Technical University of Denmark16, Leiden University17
TL;DR: This protocol provides an overview of all new features of the COBRA Toolbox and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios.
Abstract: Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
719 citations
TL;DR: BiGG Models is presented, a completely redesigned Biochemical, Genetic and Genomic knowledge base that contains more than 75 high-quality, manually-curated genome-scale metabolic models that will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.
Abstract: Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.
690 citations
Cites background or methods from "Constraint-based models predict met..."
...GEMs can be used to predict cellular phenotypes (8), contextualize omics data (9–11), design cell factories (12,13) and understand evolutionary trajectories (14)....
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...The GEMs in BiGG Models can be analyzed using the many available Constraint-Based Reconstruction and Analysis (COBRA) methods (8,9,32) or any software that reads SBML....
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...The next generation of models can eventually be included in BiGG; these models incorporate expression networks, increased spatial resolution, regulation and protein structures into GEMs (8,12,31)....
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TL;DR: A series of questions are explored to highlight some insights that comparative genomics has produced and how it could revolutionize medicine in terms of speed and accuracy of finding pathogens and knowing how to treat them.
Abstract: Since the first two complete bacterial genome sequences were published in 1995, the science of bacteria has dramatically changed. Using third-generation DNA sequencing, it is possible to completely sequence a bacterial genome in a few hours and identify some types of methylation sites along the genome as well. Sequencing of bacterial genome sequences is now a standard procedure, and the information from tens of thousands of bacterial genomes has had a major impact on our views of the bacterial world. In this review, we explore a series of questions to highlight some insights that comparative genomics has produced. To date, there are genome sequences available from 50 different bacterial phyla and 11 different archaeal phyla. However, the distribution is quite skewed towards a few phyla that contain model organisms. But the breadth is continuing to improve, with projects dedicated to filling in less characterized taxonomic groups. The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system provides bacteria with immunity against viruses, which outnumber bacteria by tenfold. How fast can we go? Second-generation sequencing has produced a large number of draft genomes (close to 90 % of bacterial genomes in GenBank are currently not complete); third-generation sequencing can potentially produce a finished genome in a few hours, and at the same time provide methlylation sites along the entire chromosome. The diversity of bacterial communities is extensive as is evident from the genome sequences available from 50 different bacterial phyla and 11 different archaeal phyla. Genome sequencing can help in classifying an organism, and in the case where multiple genomes of the same species are available, it is possible to calculate the pan- and core genomes; comparison of more than 2000 Escherichia coli genomes finds an E. coli core genome of about 3100 gene families and a total of about 89,000 different gene families. Why do we care about bacterial genome sequencing? There are many practical applications, such as genome-scale metabolic modeling, biosurveillance, bioforensics, and infectious disease epidemiology. In the near future, high-throughput sequencing of patient metagenomic samples could revolutionize medicine in terms of speed and accuracy of finding pathogens and knowing how to treat them.
577 citations
Cites background from "Constraint-based models predict met..."
...This has enabled several developments on large-scale network analysis (McCloskey et al. 2013) that can have several applications (Bordbar et al. 2014)....
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...Applying this framework and its derivatives, several studies in microbial evolution, metabolic engineering, biomedical applications, etc. have been highly successful (Bordbar et al. 2014; Monk and Palsson 2014)....
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TL;DR: This Primer will get you started in constraint-based reconstruction and analysis at the genome scale for metabolic engineering, antibiotic design, and organismal and enzyme evolution.
576 citations
Cites background or methods from "Constraint-based models predict met..."
...Recapitulation Given its simplicity and utility, FBA has become one of the most widely employed computational techniques for the systemslevel analysis of living organisms (Bordbar et al., 2014; Lewis et al., 2012)....
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...The first GEM was created for Haemophilus influenza and appeared shortly after this first genome was sequenced (Edwards and Palsson, 1999), and GEMs have now grown to the level where they enable predictive biology (Bordbar et al., 2014; McCloskey et al., 2013; Oberhardt et al., 2009)....
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...The first GEM was created for Haemophilus influenza and appeared shortly after this first genome was sequenced (Edwards and Palsson, 1999), and GEMs have now grown to the level where they enable predictive biology (Bordbar et al., 2014; McCloskey et al., 2013; Oberhardt et al., 2009)....
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References
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TL;DR: These mutants—the ‘Keio collection’—provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome‐wide testing of mutational effects in a common strain background, E. coli K‐12 BW25113.
Abstract: We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants-the 'Keio collection'-provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).
7,428 citations
TL;DR: An approach for genome analysis based on sequencing and assembly of unselected pieces of DNA from the whole chromosome has been applied to obtain the complete nucleotide sequence of the genome from the bacterium Haemophilus influenzae Rd.
Abstract: An approach for genome analysis based on sequencing and assembly of unselected pieces of DNA from the whole chromosome has been applied to obtain the complete nucleotide sequence (1,830,137 base pairs) of the genome from the bacterium Haemophilus influenzae Rd. This approach eliminates the need for initial mapping efforts and is therefore applicable to the vast array of microbial species for which genome maps are unavailable. The H. influenzae Rd genome sequence (Genome Sequence DataBase accession number L42023) represents the only complete genome sequence from a free-living organism.
5,944 citations
TL;DR: Examination of large-scale yeast two-hybrid screens reveals interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes.
Abstract: Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.
4,877 citations
TL;DR: KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets and recent enhancements to the K EGG content, especially the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.
Abstract: Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ or http://www.kegg.jp/) is a database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem. Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing experimental knowledge in computable forms; namely, in the forms of KEGG pathway maps, BRITE functional hierarchies and KEGG modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking genomes to the molecular networks through the KEGG Orthology system. Here we report KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets. We also report a variant of the KEGG mapping procedure to extend the knowledge base, where different types of data and knowledge, such as disease genes and drug targets, are integrated as part of the KEGG molecular networks. Finally, we describe recent enhancements to the KEGG content, especially the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.
4,259 citations
TL;DR: A new framework for discovering interactions between genes based on multiple expression measurements is proposed and a method for recovering gene interactions from microarray data is described using tools for learning Bayesian networks.
Abstract: DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes and because they provide a clear methodology for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes. We then describe a method for recovering gene interactions from microarray data using tools for learning Bayesian networks. Finally, we demonstrate this method on the S. cerevisiae cell-cycle measurements of Spellman et al. (1998).
3,507 citations