A graph-based approach to analyze flux-balanced pathways in metabolic networks

Arabzadeh, Mona; Zamani, Morteza Saheb; Sedighi, Mehdi; Marashi, Sayed-Amir

doi:10.1016/j.biosystems.2017.12.001

Cited by 10 publications

(10 citation statements)

References 36 publications

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“…The first ones consider the network as a directed graph and explore it [17–19]. The main disadvantage is that they do not use stoichiometric coefficients during the exploration process.…”

Section: Methodsmentioning

confidence: 99%

Improving the EFMs quality by augmenting their representativeness in LP methods

et al. 2018

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BackgroundAlthough cellular metabolism has been widely studied, its fully comprehension is still a challenge. A main tool for this study is the analysis of meaningful pieces of knowledge called modes and, in particular, specially interesting classes of modes such as pathways and Elementary Flux Modes (EFMs). Its study often has to deal with issues such as the appearance of infeasibilities or the difficulty of finding representative enough sets of modes that are free of repetitions. Mode extraction methods usually incorporate strategies devoted to mitigate this phenomena but they still get a high ratio of repetitions in the set of solutions.ResultsThis paper presents a proposal to improve the representativeness of the full set of metabolic reactions in the set of computed modes by penalizing the eventual high frequency of occurrence of some reactions during the extraction. This strategy can be applied to any linear programming based extraction existent method.ConclusionsOur strategy enhances the quality of a set of extracted EFMs favouring the presence of every reaction in it and improving the efficiency by mitigating the occurrence of repeated solutions. The new proposed strategy can complement other EFMs extraction methods based on linear programming. The obtained solutions are more likely to be diverse using less computing effort and improving the efficiency of the extraction.

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“…The first ones consider the network as a directed graph and explore it [17–19]. The main disadvantage is that they do not use stoichiometric coefficients during the exploration process.…”

Section: Methodsmentioning

confidence: 99%

Improving the EFMs quality by augmenting their representativeness in LP methods

et al. 2018

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“…Moreover, the computational algorithms should consider the case that an enzyme connects with at least two substrates at the same time to increase the yield of production. Though the graph-based approach can be used to analyze flux-balanced pathways in the metabolic network (Arabzadeh et al, 2018), it usually needs extra post-processing steps to adjust co-metabolites of the predicted pathway that could be unbalanced. In addition, the prediction of catalytic activities of enzymes has become one of the hot research topics.…”

Section: Issues Need To Be Addressedmentioning

confidence: 99%

Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways

Shah

Liu

Yang

et al. 2021

Front. Mol. Biosci.

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Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.

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“…A number of algorithms have been proposed for the efficient enumeration of EFMs 89 . Some of these algorithms exploit topological connectivity and flux information simultaneously, e.g., by using graph-based models to identify flux balanced pathways 91 ; by enumerating the pathways with minimum flux variability 92 ; or by dividing the steady state flux space into smaller, independent sub-modules 93,94 . A somewhat related strategy utilizes the concept of flux coupling, first defined by Burgard and colleagues 95 .…”

Section: Integrating Flux Information and Network Sciencementioning

confidence: 99%

Opportunities at the interface of network science and metabolic modelling

Dusad,

Thiel,

Barahona

et al. 2020

Preprint

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Metabolism plays a central role in cell physiology and provides the cellular machinery for building biomolecules essential for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: constraint-based methods, such as flux balance analysis, and network science. Whereas constraint-based methods estimate metabolic flux on the basis of an optimization principle, networktheoretic approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in basic discovery science, precision medicine and industrial biotechnology.

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A graph-based approach to analyze flux-balanced pathways in metabolic networks

Cited by 10 publications

References 36 publications

Improving the EFMs quality by augmenting their representativeness in LP methods

Improving the EFMs quality by augmenting their representativeness in LP methods

Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways

Opportunities at the interface of network science and metabolic modelling

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