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      Constraint-based modeling in microbial food biotechnology

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          Abstract

          Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.

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          A protocol for generating a high-quality genome-scale metabolic reconstruction.

          Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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            High-throughput generation, optimization and analysis of genome-scale metabolic models.

            Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking approximately 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.
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              Lactic acid bacteria as functional starter cultures for the food fermentation industry

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                Author and article information

                Journal
                Biochem Soc Trans
                Biochem. Soc. Trans
                ppbiost
                BST
                Biochemical Society Transactions
                Portland Press Ltd.
                0300-5127
                1470-8752
                17 April 2018
                27 March 2018
                : 46
                : 2
                : 249-260
                Affiliations
                Discovery, R&D Microbial Platform, Chr. Hansen A/S, 2970 Hørsholm, Denmark
                Author notes
                Correspondence: Ahmad A. Zeidan ( dkahze@ 123456chr-hansen.com )
                Author information
                http://orcid.org/0000-0001-9984-5625
                Article
                BST-46-249
                10.1042/BST20170268
                5906707
                29588387
                aea76cd0-71de-4814-b1c8-9fd09c15c5b8
                © 2018 The Author(s)

                This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND).

                History
                : 19 January 2018
                : 1 March 2018
                : 2 March 2018
                Categories
                Review Articles
                Review Article
                52
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                Biochemistry
                constraint-based modeling,food fermentation,metabolic networks,microbial community modeling,microbial food cultures,strain development

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