F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

Larhlimi, Abdelhalim; Dávid, László; Selbig, Joachim; Bockmayr, Alexander

doi:10.1186/1471-2105-13-57

Cited by 64 publications

(59 citation statements)

References 36 publications

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“…Full, partial, and directional coupling are calculated using the F2C2 tool (Larhlimi et al 2012). To define anti-coupling, we restrict the set of feasible flux distributions to…”

Section: Calculation Of Flux Coupling Relationsmentioning

confidence: 99%

Control of fluxes in metabolic networks

et al. 2016

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Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.[Supplemental material is available for this article.]Understanding how cellular systems are controlled on a genomescale is a central issue in biology and medicine. Metabolic networks are at the center of systems biology approaches to unraveling cellular control, because metabolism carries the life-sustaining cellular functions shaping the molecular phenotype (Sweetlove and Ratcliffe 2011). The steady-state principle and physico-chemical constraints (e.g., mass balance and thermodynamics) have been employed to reduce the number of considered network states, facilitating the prediction of genotype-phenotype relationships and intervention strategies for biotechnological or medical purposes (McCloskey et al. 2013). In particular, flux balance analysis and variations thereof have been successfully applied to the metabolic networks of unicellular organisms to predict their metabolic and cellular phenotypes (Varma and Palsson 1994). Yet, those approaches are biased (Lewis et al. 2012) because they restrict the flux space to an a priori specified reference state by assuming a cellular objective to be optimized by the organism (Schuetz et al. 2007). While optimization of biomass yield has proven useful for unicellular organisms, identification of a suitable objective for multicellular organisms remains a nontrivial endeavor (Sweetlove and Ratcliffe 2011). Other approaches, e.g., elementary flux modes (Schuster and Schuster 1993) and extreme pathways analyses (Schilling et al. 2000), do not assume a cellular objective and hence are unbiased. However, despite extensive studies and recent advances (Terzer and Stelling 2008), these unbiased approaches are limited to rather small networks due to their intrinsic computational complexity. We still lack an unbiased computational approach for systematically studying the control of large-scale metabolic networks.Here, we develop such an approach by employing the flux coupling between reactio...

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“…Full, partial, and directional coupling are calculated using the F2C2 tool (Larhlimi et al 2012). To define anti-coupling, we restrict the set of feasible flux distributions to…”

Section: Calculation Of Flux Coupling Relationsmentioning

confidence: 99%

Control of fluxes in metabolic networks

et al. 2016

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“…Table 3 shows the amount of the possible sub-graphs that are balanced, pathway and, finally, EFMs. To build this table it has purged the model quitting the blocked reactions proposed by [30]. Reaction P ps is one of those blocked reactions that do not appear except in one EFM with its respective reversible pair.…”

Section: Case Study: Tca Cyclementioning

confidence: 99%

A new approach to obtaining EFMs using graph methods based on the shortest path between end nodes

Hidalgo¹,

Guil²,

García³

2016

Genomics Comput Biol

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Genome-scale metabolic networks let us understand the behaviour of the metabolism in the cells of living organisms. The availability of great amounts of such data gives the scientific community the opportunity to infer in silico new metabolic knowledge. Elementary Flux Modes (EFM) are minimal contained pathways or subsets of a metabolic network that are very useful to achieving the comprehension of a very specific metabolic function (as well as dysfunctions), and to get the knowledge to develop new drugs. Metabolic networks can have large connectivity and, therefore, EFMs resolution faces a combinational explosion challenge to be solved. In this paper we propose a new approach to obtain EFMs based on graph theory, the balanced graph concept and the shortest path between end nodes.Our proposal uses the shortest path between end nodes (input and output nodes) that finds all the pathways in the metabolic network and is able to prioritise the pathway search accounting the biological mean pursued. Our technique has two phases, the exploration phase and the characterisation one, and we show how it works in a well-known case study. We also demonstrate the relevance of the concept of balanced graph to achieve to the full list of EFMs.

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“…S4 (ESI †) includes the complete list of TS-reactions and O-reactions (288 O-reactions and 46 TS-reactions). Next, we used F2C2 software 36 to determine the blocked reactions of Recon1. The set of blocked reactions was omitted from the list of TS-reactions and O-reactions.…”

Section: Reconstruction Of B2014 a Generic Metabolic Network For Canmentioning

confidence: 99%

Reconstruction of a generic metabolic network model of cancer cells

Hadi

Marashi

2014

Mol. BioSyst.

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A promising strategy for finding new cancer drugs is to use metabolic network models to investigate the essential reactions or genes in cancer cells. In this study, we present a generic constraint-based model of cancer metabolism, which is able to successfully predict the metabolic phenotypes of cancer cells. This model is reconstructed by collecting the available data on tumor suppressor genes. Notably, we show that the activation of oncogene related reactions can be explained by the inactivation of tumor suppressor genes. We show that in a simulated growth medium similar to the body fluids, our model outperforms the previously proposed model of cancer metabolism in predicting expressed genes.

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F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

Cited by 64 publications

References 36 publications

Control of fluxes in metabolic networks

Control of fluxes in metabolic networks

A new approach to obtaining EFMs using graph methods based on the shortest path between end nodes

Reconstruction of a generic metabolic network model of cancer cells

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