Environmental variability and modularity of bacterial metabolic networks

Parter, Merav; Kashtan, Nadav; Alon, Uri

doi:10.1186/1471-2148-7-169

Cited by 182 publications

(256 citation statements)

References 58 publications

(52 reference statements)

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“…chromatin), and surrounded by transcriptional changes Corresponding author: Ralser, M. (ralser@molgen.mpg.de) requirements drive the evolution of modular structures [14]. Moreover, bacteria that live in complex environments, such as soil, possess greater network modularity than do bacterial parasites that live in constant environments [15].…”

Section: Glossarymentioning

confidence: 99%

Regulatory crosstalk of the metabolic network

Grüning

Lehrach

Ralser

2010

Trends in Biochemical Sciences

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The metabolic network has a modular architecture, is robust to perturbations, and responds to biological stimuli and environmental conditions. Through monitoring by metabolite responsive macromolecules, metabolic pathways interact with the transcriptome and proteome. Whereas pathway interconnecting cofactors and substrates report on the overall state of the network, specialised intermediates measure the activity of individual functional units. Transitions in the network affect many of these regulatory metabolites, facilitating the parallel regulation of the timing and control of diverse biological processes. The metabolic network controls its own balance, chromatin structure and the biosynthesis of molecular cofactors; moreover, metabolic shifts are crucial in the response to oxidative stress and play a regulatory role in cancer.Evolution of the metabolic network and its structure Life probably began with the formation of compartmentalised autocatalytic chemical cycles. With the appearance of complex catalysts (ribonucleic acid-or protein-based enzymes), these cycles gained complexity, and evolved in their effectiveness and robustness [1]. These metabolic pathways now form the basis for life.As was the case for the first primitive life forms, the survival of today's complex organisms depends on the robustness and functionality of the metabolic framework [2]. To prevent and counteract perturbations in the flux, many biological control and sensor systems have evolved around the metabolic network. Metabolic pathways provide the cell with energy and supply macromolecular synthesis pathways with their required intermediates. They also balance metabolic systems under a variety of physiological conditions, and act as transducers and sensor systems for environmental conditions and stimuli. In addition, metabolic pathways are essential for crosstalk between metabolic regulatory components and the cellular transcriptome and proteome.As early as the 1960s, the principle that cells alter their gene expression in response to metabolic requirements has been known. The seminal work of Jacob and Monod, investigating the lac operon, uncovered one of the first examples of chemical-genetic regulation, by which bacterial cells adjust their enzyme production to the nutrient supply [3]. However, only recently have the broader implications of this principle emerged. Indeed, changes in the metabolic network not only control the metabolome, but they also have wide-ranging regulatory functions throughout biology.Most of the biochemical mechanisms that link the metabolic network to the transcriptome and proteome are incompletely understood. However, one type of regulation appears to be common: representative network intermediates bind to and modulate sensor function and activity of transcription factors, translational regulators, chromatin, enzymes, RNA molecules, and ion channels. Significant transcriptional changes around these intermediates identified them as reporter metabolites [4,5]. The use of intermediates as activity reporters ne...

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Section: Glossarymentioning

confidence: 99%

Regulatory crosstalk of the metabolic network

Grüning

Lehrach

Ralser

2010

Trends in Biochemical Sciences

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“…The next interesting observation is that the percolation thresholds for the specialised and aquatic classes are almost identical. This is perhaps not too surprising, however, since these two classes are often considered to be equivalent in terms of their environmental variability (Parter et al, 2007;Crofts and Estrada, 2014;Pearcy et al, 2015). Therefore, the bacteria from these two classes are likely to have a similar tolerance towards random errors, despite these bacteria being exposed to quite different conditions.…”

Section: Cohort Studymentioning

confidence: 99%

Complexity and robustness in hypernetwork models of metabolism

Pearcy

Chuzhanova

Crofts

2016

Journal of Theoretical Biology

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Metabolic reaction data is commonly modelled using a complex network approach, whereby nodes represent the chemical species present within the organism of interest, and connections are formed between those nodes participating in the same chemical reaction. Unfortunately, such an approach provides an inadequate description of the metabolic process in general, as a typical chemical reaction will involve more than two nodes, thus risking oversimplification of the the system of interest in a potentially significant way. In this paper, we employ a complex hypernetwork formalism to investigate the robustness of bacterial metabolic hypernetworks by extending the concept of a percolation process to hypernetworks. Importantly, this provides a novel method for determining the robustness of these systems and thus for quantifying their resilience to random attacks/errors. Moreover, we performed a site percolation analysis on a large cohort of bacterial metabolic networks and found that hypernetworks that evolved in more variable environments displayed increased levels of robustness and topological complexity.

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“…Tel: +44 (0)115 848 8419; E-mail: jonathan.crofts@ntu.ac.uk structural units, has been shown to be a prevalent feature within metabolic networks 14,15 , and has, for example, been related to important biological properties such as robustness 16,17 and evolvability 10,11,16 . However, metabolic networks are by no means perfectly modular; their inter-module connectivity is relatively high, leading some authors to conclude that these networks are better described as being hierarchically structured 14 , that is metabolic networks may be considered to possess fractal-like properties, such as self-similarity.…”

Section: Introductionmentioning

confidence: 99%

Network motif frequency vectors reveal evolving metabolic network organisation

2015

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At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

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Environmental variability and modularity of bacterial metabolic networks

Cited by 182 publications

References 58 publications

Regulatory crosstalk of the metabolic network

Regulatory crosstalk of the metabolic network

Complexity and robustness in hypernetwork models of metabolism

Network motif frequency vectors reveal evolving metabolic network organisation

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