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      The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

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          Abstract

          13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA.

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          Most cited references96

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          InChI, the IUPAC International Chemical Identifier

          This paper documents the design, layout and algorithms of the IUPAC International Chemical Identifier, InChI.
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            Minimum information requested in the annotation of biochemical models (MIRIAM).

            Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.
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              Metabolic networks in motion: 13C-based flux analysis

              Uwe Sauer (2006)
              Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                24 May 2019
                2019
                : 10
                : 1022
                Affiliations
                [1] 1Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH , Jülich, Germany
                [2] 2Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University , Aachen, Germany
                Author notes

                Edited by: Lars Keld Nielsen, University of Queensland, Australia

                Reviewed by: Sonia Cortassa, National Institutes of Health (NIH), United States; Hiroshi Shimizu, Osaka University, Japan; Maciek R. Antoniewicz, University of Delaware, United States; Fumio Matsuda, Osaka University, Japan

                *Correspondence: Katharina Nöh k.noeh@ 123456fz-juelich.de

                This article was submitted to Microbial Physiology and Metabolism, a section of the journal Frontiers in Microbiology

                †These authors have contributed equally to this work

                ‡These authors have contributed equally to this work

                Article
                10.3389/fmicb.2019.01022
                6543931
                31178829
                ef7e5974-18aa-4f36-bbce-0eeba4561825
                Copyright © 2019 Beyß, Azzouzi, Weitzel, Wiechert and Nöh.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 16 October 2018
                : 24 April 2019
                Page count
                Figures: 8, Tables: 0, Equations: 3, References: 105, Pages: 24, Words: 16512
                Funding
                Funded by: Deutsche Forschungsgemeinschaft 10.13039/501100001659
                Categories
                Microbiology
                Technology Report

                Microbiology & Virology
                13c metabolic flux analysis,fluxml,machine-readable format,model specification language,computational modeling,reproducible science,data models,model exchange

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