Stochastic simulation algorithm for isotope-based dynamic flux analysis

Carbon isotope labeling method is a standard metabolic engineering tool for flux quantification in living cells. To cope with the high dimensionality of isotope labeling systems, diverse algorithms have been developed to reduce the number of variables or operations in metabolic flux analysis (MFA),...

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Bibliographic Details
Published in:Metabolic engineering Vol. 75; pp. 100 - 109
Main Authors: Thommen, Quentin, Hurbain, Julien, Pfeuty, Benjamin
Format: Journal Article
Language:English
Published: Belgium Elsevier Inc 01.01.2023
Subjects:
Algorithms
Carbon
Carbon Isotopes - metabolism
Computer Simulation
Flux balance analysis
Isotope Labeling - methods
Life Sciences
Metabolic flux analysis
Metabolic Flux Analysis - methods
Metabolic network model
Metabolism
Models, Biological
Pentose Phosphate Pathway
Stable-isotope tracers
Systems biology
Flux balance analysis
Stable-isotope tracers
Metabolism
Metabolic network model
Systems biology
Metabolic flux analysis
Metabolic flux analysis Flux balance analysis Metabolism Metabolic network model Stable-isotope tracers Systems biology
ISSN:1096-7176, 1096-7184, 1096-7184, 1096-7176
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Summary:Carbon isotope labeling method is a standard metabolic engineering tool for flux quantification in living cells. To cope with the high dimensionality of isotope labeling systems, diverse algorithms have been developed to reduce the number of variables or operations in metabolic flux analysis (MFA), but lacks generalizability to non-stationary metabolic conditions. In this study, we present a stochastic simulation algorithm (SSA) derived from the chemical master equation of the isotope labeling system. This algorithm allows to compute the time evolution of isotopomer concentrations in non-stationary conditions, with the valuable property that computational time does not scale with the number of isotopomers. The efficiency and limitations of the algorithm is benchmarked for the forward and inverse problems of 13C-DMFA in the pentose phosphate pathways, and is compared with EMU-based methods for NMFA and MFA including the central carbon metabolism. Overall, SSA constitutes an alternative class to deterministic approaches for metabolic flux analysis that is well adapted to comprehensive dataset including parallel labeling experiments, and whose limitations associated to the sampling size can be overcome by using Monte Carlo sampling approaches. •A new fast algorithm for isotope-based flux analysis is presented.•The temporal evolution of isotopomer concentrations under non-stationary flux conditions is now computable.•Stochastic methods efficiently mimic the propagation of carbon labeling through the metabolic network.•Combination of chemical kinetics and labeling propagation is suited for isotope-based dynamic flux analysis (13C-DMFA).
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ISSN:1096-7176
1096-7184
1096-7184
1096-7176
DOI:10.1016/j.ymben.2022.11.001