scholarly journals Bayesian metabolic flux analysis reveals intracellular flux couplings

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Reaction Network ◽  
Reaction Rates ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Metabolic Systems ◽  
Genome Scale

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.

scholarly journals Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Martin Beyß ◽  
Victor D. Parra-Peña ◽  
Howard Ramirez-Malule ◽  
Katharina Nöh
Keyword(s):  
Experimental Design ◽  
Prior Knowledge ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Streptomyces Clavuligerus ◽  
Reaction Rates ◽  
Computational Method ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Diverse Range

13C metabolic flux analysis (MFA) has become an indispensable tool to measure metabolic reaction rates (fluxes) in living organisms, having an increasingly diverse range of applications. Here, the choice of the13C labeled tracer composition makes the difference between an information-rich experiment and an experiment with only limited insights. To improve the chances for an informative labeling experiment, optimal experimental design approaches have been devised for13C-MFA, all relying on some a priori knowledge about the actual fluxes. If such prior knowledge is unavailable, e.g., for research organisms and producer strains, existing methods are left with a chicken-and-egg problem. In this work, we present a general computational method, termed robustified experimental design (R-ED), to guide the decision making about suitable tracer choices when prior knowledge about the fluxes is lacking. Instead of focusing on one mixture, optimal for specific flux values, we pursue a sampling based approach and introduce a new design criterion, which characterizes the extent to which mixtures are informative in view of all possible flux values. The R-ED workflow enables the exploration of suitable tracer mixtures and provides full flexibility to trade off information and cost metrics. The potential of the R-ED workflow is showcased by applying the approach to the industrially relevant antibiotic producer Streptomyces clavuligerus, where we suggest informative, yet economic labeling strategies.

Metabolic flux analysis of hybridoma continuous culture steady state multiplicity

1999 ◽  
Vol 63 (6) ◽  
pp. 675-683 ◽  
Author(s):  
Brian D. Follstad ◽  
R. Robert Balcarcel ◽  
Gregory Stephanopoulos ◽  
Daniel I. C. Wang
Keyword(s):  
Steady State ◽  
Continuous Culture ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
State Multiplicity

scholarly journals State-of-the-Art Genetic Modalities to Engineer Cyanobacteria for Sustainable Biosynthesis of Biofuel and Fine-Chemicals to Meet Bio–Economy Challenges

Life ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 54 ◽  
Author(s):  
Aqib Zafar Khan ◽  
Muhammad Bilal ◽  
Shahid Mehmood ◽  
Ashutosh Sharma ◽  
Hafiz M. N. Iqbal
Keyword(s):  
High Throughput ◽  
Metabolic Flux Analysis ◽  
Large Scale ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
Fine Chemicals ◽  
Carbon Regulation ◽  
Regulation Mechanisms ◽  
Large Scale Cultivation ◽  
Genome Scale

In recent years, metabolic engineering of microorganisms has attained much research interest to produce biofuels and industrially pertinent chemicals. Owing to the relatively fast growth rate, genetic malleability, and carbon neutral production process, cyanobacteria has been recognized as a specialized microorganism with a significant biotechnological perspective. Metabolically engineering cyanobacterial strains have shown great potential for the photosynthetic production of an array of valuable native or non-native chemicals and metabolites with profound agricultural and pharmaceutical significance using CO2 as a building block. In recent years, substantial improvements in developing and introducing novel and efficient genetic tools such as genome-scale modeling, high throughput omics analyses, synthetic/system biology tools, metabolic flux analysis and clustered regularly interspaced short palindromic repeats (CRISPR)-associated nuclease (CRISPR/cas) systems have been made for engineering cyanobacterial strains. Use of these tools and technologies has led to a greater understanding of the host metabolism, as well as endogenous and heterologous carbon regulation mechanisms which consequently results in the expansion of maximum productive ability and biochemical diversity. This review summarizes recent advances in engineering cyanobacteria to produce biofuel and industrially relevant fine chemicals of high interest. Moreover, the development and applications of cutting-edge toolboxes such as the CRISPR-cas9 system, synthetic biology, high-throughput “omics”, and metabolic flux analysis to engineer cyanobacteria for large-scale cultivation are also discussed.

scholarly journals 13C metabolic flux analysis at a genome-scale

2015 ◽  
Vol 32 ◽  
pp. 12-22 ◽  
Author(s):  
Saratram Gopalakrishnan ◽  
Costas D. Maranas
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
13C Metabolic Flux Analysis ◽  
A Genome ◽  
Genome Scale

Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium

2012 ◽  
Vol 161 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Pieter-Jan D’Huys ◽  
Ivan Lule ◽  
Dominique Vercammen ◽  
Jozef Anné ◽  
Jan F. Van Impe ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Streptomyces Lividans ◽  
Complex Medium ◽  
Flux Analysis ◽  
Genome Scale

scholarly journals 13C-Metabolic Flux Analysis in Developing Flaxseed Embryos to Understand Storage Lipid Biosynthesis

2019 ◽  
Author(s):  
Sebastien Acket ◽  
Anthony Degournay ◽  
Yannick Rossez ◽  
Stephane Mottelet ◽  
Pierre Villon ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Metabolic Network ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Fatty Acid Biosynthesis ◽  
Lipid Biosynthesis ◽  
Flux Analysis ◽  
13C Metabolic Flux Analysis ◽  
Storage Lipid ◽  
Metabolic Systems

Flaxseed (Linum usitatissinum L.) oil is an important source of α-linolenic (C18:3 ω-3), this polyunsaturated fatty acid is well known for its nutritional role in human and animal diet. Understanding storage lipid biosynthesis in developing flaxseed embryos can lead to an increase in seed yield. While a tremendous amount of work has been done on different plant species to highlight their metabolism during embryos development, flaxseed metabolic flux analysis is still lacking. In this context, we have developed an in vitro cultured developing embryos of flaxseed and determined net fluxes by performing three complementary parallel labeling experiments with 13C-labeled glucose and glutamine. Metabolic fluxes were estimated by computer- aided modeling of the central metabolic network including 11 cofactors of 118 reactions of the central metabolism, 12 pseudo fluxes. A focus on lipid storage biosynthesis and the associated pathways was done in comparison with rapeseed, arabidopsis, maize and sunflower embryos. In our conditions, glucose was the main source of carbone of flaxseed embryos, leading to the conversion of phosphoenolpyruvate to pyruvate. The oxidative pentose phosphate pathway (OPPP) was identified as the producer of NADPH for fatty acid biosynthesis. Overall, the use of 13C-metabolic flux analysis provided new insight into flaxseed embryos metabolic processes involved in storage lipids biosynthesis. The elucidation of the metabolic network of this important crop plant reinforces the relevance of the application of this technique to the analysis of complex plant metabolic systems.

scholarly journals Metabolic mechanism of Saccharomyces cerevisiae under different physiological conditions based on non-stationary 13C metabolic flux analysis

2020 ◽  
Author(s):  
Huan Li ◽  
Min Chen ◽  
Peng Liu ◽  
Shuai Wang ◽  
JY Xia
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Tricarboxylic Acid Cycle ◽  
Energy Charge ◽  
Flux Analysis ◽  
13C Metabolic Flux Analysis ◽  
Metabolic Flux Distribution ◽  
Central Carbon

Abstract Crabtree effect is well known for Saccharomyces cerevisiae, and is defined as glucose-induced repression of respiratory flux. Even though a number of hypotheses have been formulated, its triggering mechanisms are still unknown. At present, the information about intracellular metabolic flux can be obtained by the 13C isotope labeling experiments. 13C metabolic flux analysis(13C-MFA) is a traditional method for calculating metabolic flux based on isotopic steady state. Another new method (INST-13C-MFA: Isotopically nonstationary metabolic flux analysis) based on isotope non-steady state is being used by researchers. In this review, we have chemostatized S. cerevisiae at three different dilution rates (D=0.12, 0.22, 0.32 h-1) and obtained the metabolic flux distribution of the intracellular central carbon metabolic of S. cerevisiae using INST-13C-MFA. Combined with the metabolome and metabolic fluxome data, we found obvious metabolic flux shift under the three different physiological states. In this process, pyruvate decarboxylase, ethanol dehydrogenase and acetyl-CoA synthase(AcCoA) catalyzed reactions were key points. Negative correlation between relative flux of embden meyerh of pathway(EMP) and tricarboxylic acid cycle(TCA) and biomass yield, while positive correlation for pentose phosphate pathway(PPP) were observed. Yield of acetate and glycerol did not change significantly, while that of ethanol increased sharply. In the central carbon metabolism (CCM), most of the carbon flux (70%) was directed to the EMP. At the same time, the energy charge increased with dilution rate, and the cell's energy supply mode gradually shifted from oxidative respiration to substrate level phosphorylation mode.

Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis

2019 ◽  
Vol 36 (1) ◽  
pp. 232-240
Author(s):  
Axel Theorell ◽  
Katharina Nöh
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Bayesian Model Averaging ◽  
Linear Models ◽  
Model Averaging ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Large Set ◽  
Vast Number

Abstract Motivation The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging. Results Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference. Supplementary information Supplementary data are available at Bioinformatics online.

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