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  Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony

Hashemi, S., Razaghi-Moghadam, Z., & Nikoloski, Z. (2023). Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony. PLoS Computational Biology, 19(9): e1011489. doi:10.1371/journal.pcbi.1011489.

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Hashemi, Seirana1, Author
Razaghi-Moghadam, Z.2, Author           
Nikoloski, Z.2, Author           
Affiliations:
1external, ou_persistent22              
2Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              

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 Abstract: Author summary Data on intracellular fluxes in biological systems provide a snapshot of the rates of underlying reactions and activity of metabolic pathways. However, capturing the activity of reactions and pathways is very resource-intensive, precluding widespread usage of fluxes in understanding of cellular physiology. Therefore, approaches for accurate and precise prediction of intracellular fluxes can propel the usage of intracellular fluxes in diverse biotechnological application that require the identification of reaction targets. Here, we propose a constraint-based approach, termed complex-balanced flux balance analysis, based on the principle of maximizing multi-reaction dependencies. By using data sets of intracellular fluxes in strains of two model organisms, Escherichia coli and Saccharomyces cerevisiae, we show that the predictions from our approach are more accurate and precise in comparison to a widely used approach relying on the principle of parsimonious usage of cellular resources. Therefore, our results suggest that other cellular principles, related to properties of steady state fluxes, such as multi-reaction dependencies, may shape cellular physiology.

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Language(s): eng - English
 Dates: 2023-09-182023-09
 Publication Status: Issued
 Pages: -
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 Identifiers: DOI: 10.1371/journal.pcbi.1011489
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 19 (9) Sequence Number: e1011489 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1