Metabolic reaction network ofPichia pastoriswith glycosylation reactions: Flux analysis for erythropoietin production

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

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Reaction network flux analysis: Optimization-based evaluation of reaction pathways for biorenewables processing

AIChE Journal ◽

10.1002/aic.12704 ◽

2011 ◽

Vol 58 (6) ◽

pp. 1788-1801 ◽

Cited By ~ 76

Author(s):

A. Voll ◽

W. Marquardt

Keyword(s):

Reaction Network ◽

Reaction Pathways ◽

Flux Analysis

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Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach

BMC Systems Biology ◽

10.1186/1752-0509-7-s2-s13 ◽

2013 ◽

Vol 7 (Suppl 2) ◽

pp. S13 ◽

Cited By ~ 4

Author(s):

Limin Li ◽

Hao Jiang ◽

Yushan Qiu ◽

Wai-Ki Ching ◽

Vassilios S Vassiliadis

Keyword(s):

Reaction Network ◽

Flux Analysis ◽

Network Approach

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A Promising Method for Calculating True Steady-State Metabolite Concentrations in Large-Scale Metabolic Reaction Network Models

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2018.2853724 ◽

2020 ◽

Vol 17 (1) ◽

pp. 27-36

Author(s):

Atsuko Miyawaki-Kuwakado ◽

Soichiro Komori ◽

Fumihide Shiraishi

Keyword(s):

Steady State ◽

Large Scale ◽

Reaction Network ◽

Network Models ◽

Promising Method ◽

Metabolic Reaction ◽

Metabolite Concentrations

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Flux Analysis of Central Metabolic Pathways in Geobacter metallireducens during Reduction of Soluble Fe(III)-Nitrilotriacetic Acid

Applied and Environmental Microbiology ◽

10.1128/aem.02986-06 ◽

2007 ◽

Vol 73 (12) ◽

pp. 3859-3864 ◽

Cited By ~ 42

Author(s):

Yinjie J. Tang ◽

Romy Chakraborty ◽

Hï¿½ctor Garcï¿½a Martï¿½n ◽

Jeannie Chu ◽

Terry C. Hazen ◽

...

Keyword(s):

Metabolic Pathways ◽

Tricarboxylic Acid Cycle ◽

Nitrilotriacetic Acid ◽

Reaction Rates ◽

Total Carbon ◽

Exponential Growth Phase ◽

Flux Analysis ◽

Metabolic Reaction ◽

Geobacter Metallireducens ◽

Terminal Electron Acceptor

ABSTRACT We analyzed the carbon fluxes in the central metabolism of Geobacter metallireducens strain GS-15 using 13C isotopomer modeling. Acetate labeled in the first or second position was the sole carbon source, and Fe-nitrilotriacetic acid was the sole terminal electron acceptor. The measured labeled acetate uptake rate was 21 mmol/g (dry weight)/h in the exponential growth phase. The resulting isotope labeling pattern of amino acids allowed an accurate determination of the in vivo global metabolic reaction rates (fluxes) through the central metabolic pathways using a computational isotopomer model. The tracer experiments showed that G. metallireducens contained complete biosynthesis pathways for essential metabolism, and this strain might also have an unusual isoleucine biosynthesis route (using acetyl coenzyme A and pyruvate as the precursors). The model indicated that over 90% of the acetate was completely oxidized to CO2 via a complete tricarboxylic acid cycle while reducing iron. Pyruvate carboxylase and phosphoenolpyruvate (PEP) carboxykinase were present under these conditions, but enzymes in the glyoxylate shunt and malic enzyme were absent. Gluconeogenesis and the pentose phosphate pathway were mainly employed for biosynthesis and accounted for less than 3% of total carbon consumption. The model also indicated surprisingly high reversibility in the reaction between oxoglutarate and succinate. This step operates close to the thermodynamic equilibrium, possibly because succinate is synthesized via a transferase reaction, and the conversion of oxoglutarate to succinate is a rate-limiting step for carbon metabolism. These findings enable a better understanding of the relationship between genome annotation and extant metabolic pathways in G. metallireducens.

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A minimal and self-consistent in silico cell model based on macromolecular interactions

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2007.2075 ◽

2007 ◽

Vol 362 (1486) ◽

pp. 1831-1839 ◽

Cited By ~ 5

Author(s):

Christoph Flamm ◽

Lukas Endler ◽

Stefan Müller ◽

Stefanie Widder ◽

Peter Schuster

Keyword(s):

Cell Model ◽

Reaction Network ◽

Metabolic Reaction ◽

Minimal Cell ◽

Biochemical Kinetics ◽

Biological Phenomena ◽

Self Consistent ◽

Basic Features ◽

Insight Into ◽

Modelling Approach

A self-consistent minimal cell model with a physically motivated schema for molecular interaction is introduced and described. The genetic and metabolic reaction network of the cell is modelled by multidimensional nonlinear ordinary differential equations, which are derived from biochemical kinetics. The strategy behind this modelling approach is to keep the model sufficiently simple in order to be able to perform studies on evolutionary optimization in populations of cells. At the same time, the model should be complex enough to handle the basic features of genetic control of metabolism and coupling to environmental factors. Thereby, the model system will provide insight into the mechanisms leading to important biological phenomena, such as homeostasis, (circadian) rhythms, robustness and adaptation to a changing environment. One example of modelling a molecular regulatory mechanism, cooperative binding of transcription factors, is discussed in detail.

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Metabolic Reaction Network-based Recursive Metabolite Identification for Untargeted Metabolomics

10.1101/305201 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xiaotao Shen ◽

Xin Xiong ◽

Ruohong Wang ◽

Yandong Yin ◽

Yuping Cai ◽

...

Keyword(s):

Data Acquisition ◽

Biological Sample ◽

Large Scale ◽

Recursive Algorithm ◽

Reaction Network ◽

Metabolite Identification ◽

Untargeted Metabolomics ◽

Metabolic Reaction ◽

Link Type ◽

Functional Metabolomics

Metabolite identification is a long-standing challenge in untargeted metabolomics and a major hurdle for functional metabolomics studies. Here, we developed a metabolic reaction network-based recursive algorithm and webserver called MetDNA for the large-scale and unambiguous identification of metabolites (available at http://metdna.zhulab.cn). We showcased the versatility of our workflow using different instrument platforms, data acquisition methods, and biological sample types and demonstrated that over 2,000 metabolites could be identified from one experiment.

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