Organising metabolic networks: Cycles in flux distributions

Kritz, Maurício Vieira; Santos, M. Tadeu dos; Urrutia, Sebastián; Schwartz, Jean-Marc

doi:10.1016/j.jtbi.2010.04.026

Cited by 10 publications

(13 citation statements)

References 40 publications

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“…Mass imbalances originating from the apparent imbalances described above were solved locally, i.e. by correcting the mass of the species to eliminate the apparent mass imbalance, as described in Kritz et al (2010) . Moreover, the most ubiquitous cofactors were left out from our representation and replaced by gateways (Supplementary file 1, Table S1), except in the thykaloid lumen where reactions were left unchanged.…”

Section: Methodsmentioning

confidence: 99%

“…The final phase was the cyclic decomposition of the flux. The complete algorithm was described in Kritz et al (2010) and is here summarised briefly. First, a critical arc is identified in the network, defined as the arc bearing a minimal mass flux among all cycles.…”

Section: Methodsmentioning

confidence: 99%

“…They can be enumerated in metabolic networks by techniques based on stoichiometric representations such as elementary mode and extreme pathways analysis ( Schuster et al, 2000 , Price et al, 2002 , Gebauer et al, 2012 ). Kritz et al (2010) introduced a way of analysing the cycling of matter in metabolic networks by adapting a pre-existing algorithm used to inspect cycles in ecological food webs ( Ulanowicz, 1983 ). The adapted algorithm represents the metabolic network as a bipartite graph of metabolites and reactions; it first enumerates all graph-theoretical cycles in the network, then uses flux values associated to arcs to iteratively extract the cycles from the graph, leaving a residual acyclic graph in the case of an open network.…”

Section: Introductionmentioning

confidence: 99%

“…The adapted algorithm represents the metabolic network as a bipartite graph of metabolites and reactions; it first enumerates all graph-theoretical cycles in the network, then uses flux values associated to arcs to iteratively extract the cycles from the graph, leaving a residual acyclic graph in the case of an open network. Kritz et al (2010) were able to observe the metabolic changes that occur with gene knockout experiments, unveiling novel hypotheses on how organismal growth can be optimised.…”

Section: Introductionmentioning

confidence: 99%

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Cyclic decomposition explains a photosynthetic down regulation for Chlamydomonas reinhardtii

et al. 2017

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The regulation of metabolic networks has been shown to be distributed and shared through the action of metabolic cycles. Biochemical cycles play important roles in maintaining flux and substrate availability for multiple pathways to supply cellular energy and contribute to dynamic stability. By understanding the cyclic and acyclic flows of matter through a network, we are closer to understanding how complex dynamic systems distribute flux along interconnected pathways. In this work, we have applied a cycle decomposition algorithm to a genome-scale metabolic model of Chlamydomonas reinhardtii to analyse how acetate supply affects the distribution of fluxes that sustain cellular activity. We examined the role of metabolic cycles which explain the down regulation of photosynthesis that is observed when cells are grown in the presence of acetate. Our results suggest that acetate modulates changes in global metabolism, with the pentose phosphate pathway, the Calvin-Benson cycle and mitochondrial respiration activity being affected whilst reducing photosynthesis. These results show how the decomposition of metabolic flux into cyclic and acyclic components helps to understand the impact of metabolic cycling on organismal behaviour at the genome scale.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cyclic decomposition explains a photosynthetic down regulation for Chlamydomonas reinhardtii

et al. 2017

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“…To cite one, molecules of substrates and residues of proteins need to fit the space-time niche where they are, e.g., by adjusting vibrations. Besides that, connections between chemical reactions occurring in distinct regions of a cell are often fake, since different molecules of the same substrate take part in each of them and there is no real connection between the reactions [100,140].…”

Section: Interacting Organisations: Biological and Complex Phenomenamentioning

confidence: 99%

From Systems to Organisations

Kritz

2017

Systems

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Warren Weaver, writing about the function that science should have in mankind's developing future, ideas and ideals, proposed to classify scientific problems into 'problems of simplicity', 'problems of disorganised complexity', and 'problems of organised complexity'-the huge complementary class to which all biological, human, and social problems belong. Problems of simplicity have few components and variables and have been extensively addressed in the last 400 years. Problems of disorganised complexity have a huge number of individually erratic components and variables, but possess collective regularities that can be analysed by resourcing to stochastic methods. Yet, 'problems of organised complexity' do not yield easily to classical or statistical treatment. Interrelations among phenomenon elements change during its evolution alongside commonly used state variables. This invalidates independence and additivity assumptions that support reductionism and affect behaviour and outcome. Moreover, organisation, the focal point in this complementary class, is still an elusive concept despite gigantic efforts undertaken since a century ago to tame it. This paper addresses the description, representation and study of phenomena in the 'problems of organised complexity' class, arguing that they should be treated as a collection of interacting organisations. Furthermore, grounded on relational mathematical constructs, a formal theoretical framework that provides operational definitions, schemes for representing organisations and their changes, as well as interactions of organisations is introduced. Organisations formally extend the general systems concept and suggest a novel perspective for addressing organised complexity phenomena as a collection of interacting organisations.

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Modelling as a process

Kritz

2023

Comp. Appl. Math.

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Broadly speaking, models are representations of something concrete or not. In science, models have always a purpose related to understanding and explaining phenomena. This requires focus and selecting what to represent and what not to represent and how to represent, among other things. Thus, a side effect of developing the scientific method is the development of a well-structured modelling paradigm. Starting from phenomena and objects, I discuss many decision-abstraction steps in the modelling process that leads to models of phenomena expressed mathematically or computationally, highlighting underlining contexts and procedures. This discourse is undertaken centred on a cross- and trans-disciplinary system science perspective. It grounds on a personal perspective and may be considered as a model of the modelling process.

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Organising metabolic networks: Cycles in flux distributions

Cited by 10 publications

References 40 publications

Cyclic decomposition explains a photosynthetic down regulation for Chlamydomonas reinhardtii

Cyclic decomposition explains a photosynthetic down regulation for Chlamydomonas reinhardtii

From Systems to Organisations

Modelling as a process

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