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.

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.

Experimental design and metabolic flux analysis tools to optimize industrially relevantHaemophilus influenzaetype b growth medium

2017 ◽  
Vol 33 (6) ◽  
pp. 1508-1519
Author(s):  
Mateus Ribeiro da Silva ◽  
Carla Andreia Freixo Portela ◽  
Silvia Maria Ferreira Albani ◽  
Paola Rizzo de Paiva ◽  
Martha Massako Tanizaki ◽  
...  
Keyword(s):  
Experimental Design ◽  
Growth Medium ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
Analysis Tools

scholarly journals Multi-objective experimental design for 13 C-based metabolic flux analysis

2015 ◽  
Vol 268 ◽  
pp. 22-30 ◽  
Author(s):  
Jeroen Bouvin ◽  
Simon Cajot ◽  
Pieter-Jan D’Huys ◽  
Jerry Ampofo-Asiama ◽  
Jozef Anné ◽  
...  
Keyword(s):  
Experimental Design ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
Multi Objective

scholarly journals Charting the metabolic landscape of the facultative methylotroph Bacillus methanolicus

2019 ◽  
Author(s):  
Baudoin Delépine ◽  
Marina Gil López ◽  
Marc Carnicer ◽  
Cláudia M. Vicente ◽  
Volker F. Wendisch ◽  
...  
Keyword(s):  
Metabolic Flux ◽  
Reaction Rates ◽  
Computational Method ◽  
Flux Analysis ◽  
Industrial Chemistry ◽  
Facultative Methylotroph ◽  
Cell Factories ◽  
Bacillus Methanolicus ◽  
Metabolic States

ABSTRACTBacillus methanolicus MGA3 is a thermotolerant and relatively fast-growing methylotroph able to secrete large quantities of glutamate and lysine. These natural characteristics make B. methanolicus a good candidate to become a new industrial chassis organism, especially in a methanol-based economy. This has motivated a number of omics studies of B. methanolicus at the genome, transcript, protein and metabolic levels. Intriguingly, the only substrates known to support B. methanolicus growth as sole source of carbon and energy are methanol, mannitol, and to a lesser extent glucose and arabitol. We hypothesized that comparing methylotrophic and non-methylotrophic metabolic states at the flux level would yield new insights into MGA3 metabolism. 13C metabolic flux analysis (13C-MFA) is a powerful computational method to estimate carbon flows from substrate to biomass (i.e. the in vivo reaction rates of the central metabolic pathways) from experimental labeling data. In this study, we designed and performed a 13C-MFA of the facultative methylotroph B. methanolicus MGA3 growing on methanol, mannitol and arabitol to compare the associated metabolic states. The results obtained validate previous findings on the methylotrophy of B. methanolicus, allowed us to characterize the assimilation pathway of one of the studied carbon sources, and provide a better overall understanding of this strain.IMPORTANCEMethanol is cheap, easy to transport and can be produced both from renewable and fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as B. methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories, or to design methylotrophic capability in other strains already used by the industry.

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