Hierarchical organization of fluxes in Escherichia coli metabolic network: Using flux coupling analysis for understanding the physiological properties of metabolic genes

Hosseini, Zhaleh; Marashi, Sayed-Amir

doi:10.1016/j.gene.2015.02.032

Cited by 7 publications

(20 citation statements)

References 38 publications

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“…All these observations are compatible with the fact that canonical metabolic network reductions only keep the reactions which discriminate different elementary modes by their distinct activity patterns. For example, the first exchange reactions in pathways are merged into the intermediate reactions which differentiate those pathways from each other which is in agreement with the findings of [46].…”

Section: Discussion and Resultssupporting

confidence: 87%

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Metabolic network reductions

Tefagh

Boyd

2018

Preprint

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Genome-scale metabolic networks are exceptionally huge and even efficient algorithms can take a while to run because of the sheer size of the problem instances. To address this problem, metabolic network reductions can substantially reduce the overwhelming size of the problem instances at hand. We begin by formulating some reasonable axioms defining what it means for a metabolic network reduction to be "canonical" which conceptually enforces reversibility without loss of any information on the feasible flux distributions. Then, we start to search for an efficient way to deduce some of the attributes of the original network from the reduced one in order to improve the performance. As the next step, we will demonstrate how to reduce a metabolic network repeatedly until no more reductions are possible. In the end, we sum up by pointing out some of the biological implications of this study apart from the computational aspects discussed earlier.Keywords: systems biology; metabolic network analysis; canonical metabolic network reduction; quantitative flux coupling analysis; QFCA. Author summaryMetabolic networks appear at first sight to be nothing more than an enormous body of reactions. The dynamics of each reaction obey the same fundamental laws and a metabolic network as a whole is the melange of its reactions. The oversight in this kind of reductionist thinking is that although the behavior of a metabolic network is determined by the states of its reactions in theory, nevertheless it cannot be inferred directly from them in practice. Apart from the infeasibility of this viewpoint, metabolic pathways are what explain the biological functions of the organism and thus also what we are frequently concerned about at the system level.Canonical metabolic network reductions decrease the number of reactions substantially despite leaving the metabolic pathways intact. In other words, the reduced metabolic networks are smaller in size while retaining the same metabolic pathways. The possibility of such operations is rooted in the fact that the total degrees of freedom of a metabolic network in the steady-state conditions are significantly lower than the number of its reactions because of some emergent redundancies. Strangely enough, these redundancies turn out to be very well-studied in the literature.

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Section: Discussion and Resultssupporting

confidence: 87%

“…Recall that the reactions reduced during the suggested process are the reactions which the rest of the remaining reactions are coupled to. A recent study [46] has shown that these are the essential reactions enriching the vital metabolic processes of the cell. In addition, they are more conserved and of older evolutionary age.…”

Section: Discussion and Resultsmentioning

confidence: 99%

Metabolic network reductions

Tefagh

Boyd

2018

Preprint

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“…Next, we asked if the flux order relation differed from flux coupling relations, which have already been used to discover hierarchies in metabolic networks [14]. To this end, we compared the flux order relation with the three types of flux coupling: full, partial and directional [13].…”

Section: A Comparison Between Flux Ordering and Flux Couplingmentioning

confidence: 99%

“…The directionality (i.e. asymmetry) of this coupling relation induces a hierarchical organization, in which reactions situated at the top of the hierarchy control the activation state of all reactions accessible below [14].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, genes associated to stoichiometrically coupled reaction pairs tend to be co-expressed in E. coli [15] and maize [16] and to co-evolve in E. coli [17]. Further, genes associated to reactions in upper layers of the hierarchy based on directional flux coupling tend to be more regulated at the transcriptional level [14]. In addition, computational and experimental evidence has indicated that reactions in the core of a metabolic network (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Flux-based hierarchical organization of Escherichia coli’s metabolic network

Robaina-Estévez

Nikoloski

2019

Preprint

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AbstractBiological networks across scales exhibit hierarchical organization that may constrain network function. Yet, understanding how these hierarchies arise due to the operational constraint of the networks and whether they impose limits to molecular phenotypes remains elusive. Here we show that metabolic networks include a hierarchy of reactions based on a natural flux ordering that holds for every steady state. We find that the hierarchy of reactions is reflected in experimental measurements of transcript, protein and flux levels of Escherichia coli under various growth conditions as well as in the catalytic rate constants of the corresponding enzymes. Our findings point at resource partitioning and a fine-tuning of enzyme levels in E. coli to respect the constraints imposed by the network structure at steady state. Since reactions in upper layers of the hierarchy impose an upper bound on the flux of the reactions downstream, the hierarchical organization of metabolism due to the flux ordering has direct applications in metabolic engineering.

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A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology

et al. 2016

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Chameleon proteins are proteins which include sequences that can adopt α-helix-β-strand (HE-chameleon) or α-helix-coil (HC-chameleon) or β-strand-coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein-protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein-protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.

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Hierarchical organization of fluxes in Escherichia coli metabolic network: Using flux coupling analysis for understanding the physiological properties of metabolic genes

Cited by 7 publications

References 38 publications

Metabolic network reductions

Metabolic network reductions

Flux-based hierarchical organization of Escherichia coli’s metabolic network

A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology

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