Isotopically nonstationary metabolic flux analysis (INST-MFA): putting theory into practice

Cheah, Yi Ern; Young, Jamey D.

doi:10.1016/j.copbio.2018.02.013

Cited by 59 publications

(42 citation statements)

References 50 publications

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“…In turn, these developments triggered the setup of more comprehensive network models (Gopalakrishnan and Maranas, 2015; McCloskey et al, 2016b; Nilsson and Jain, 2016). Also INST 13 C MFA application scenarios have become more commonplace (Niedenführ et al, 2015; Cheah and Young, 2018; Delp et al, 2018; Gopalakrishnan et al, 2018). In view of these developments, existing formats have several limitations making a revision necessary.…”

Section: Decisions On the Design Of Fluxmlmentioning

confidence: 99%

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

et al. 2019

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13 C 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. 13 C 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 13 C 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 13 C 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 13 C 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 13 C MFA.

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Section: Decisions On the Design Of Fluxmlmentioning

confidence: 99%

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

et al. 2019

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“…network topology, fluxes). We refer to previous work [12][13][14]22,24,33] for extensive discussion on these topics.…”

Section: Discussionmentioning

confidence: 99%

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

et al. 2020

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C-metabolic flux analysis (13 C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13 C-MFA approaches model label propagation starting from the extracellular 13 C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13 C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.

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“…For instance, by taking time-dependent labeling of metabolites into account, metabolite pool sizes and reversible exchange fluxes can be estimated. This allows for comparison with (or inclusion of) metabolomics data [8,57].…”

Section: Metabolic Flux Analysismentioning

confidence: 99%

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

et al. 2020

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Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

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Isotopically nonstationary metabolic flux analysis (INST-MFA): putting theory into practice

Cited by 59 publications

References 50 publications

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

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

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

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