Dataset Information

Environmental versatility promotes modularity in genome-scale metabolic networks.

ABSTRACT:

Background

The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks.

Results

Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks.

Conclusions

Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple environments. This organizational principle is insensitive to the environments we consider and to the number of reactions in a metabolic network. Because we observe this principle not just in one or few biological networks, but in large random samples of networks, we propose that it may be a generic principle of metabolic network organization.

SUBMITTER: Samal A

PROVIDER: S-EPMC3184077 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Environmental versatility promotes modularity in genome-scale metabolic networks.

Samal Areejit A Wagner Andreas A Martin Olivier C OC

BMC systems biology 20110824

<h4>Background</h4>The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical ...[more]

PMID: 21864340

Similar Datasets

Project description:The genus Sulfobacillus is a cohort of mildly thermophilic or thermotolerant acidophiles within the phylum Firmicutes and requires extremely acidic environments and hypersalinity for optimal growth. However, our understanding of them is still preliminary partly because few genome sequences are available. Here, the draft genome of Sulfobacillus thermosulfidooxidans strain ST was deciphered to obtain a comprehensive insight into the genetic content and to understand the cellular mechanisms necessary for its survival. Furthermore, the expressions of key genes related with iron and sulfur oxidation were verified by semi-quantitative RT-PCR analysis. The draft genome sequence of Sulfobacillus thermosulfidooxidans strain ST, which encodes 3225 predicted coding genes on a total length of 3,333,554 bp and a 48.35% G+C, revealed the high degree of heterogeneity with other Sulfobacillus species. The presence of numerous transposases, genomic islands and complete CRISPR/Cas defence systems testifies to its dynamic evolution consistent with the genome heterogeneity. As expected, S. thermosulfidooxidans encodes a suit of conserved enzymes required for the oxidation of inorganic sulfur compounds (ISCs). The model of sulfur oxidation in S. thermosulfidooxidans was proposed, which showed some different characteristics from the sulfur oxidation of Gram-negative A. ferrooxidans. Sulfur oxygenase reductase and heterodisulfide reductase were suggested to play important roles in the sulfur oxidation. Although the iron oxidation ability was observed, some key proteins cannot be identified in S. thermosulfidooxidans. Unexpectedly, a predicted sulfocyanin is proposed to transfer electrons in the iron oxidation. Furthermore, its carbon metabolism is rather flexible, can perform the transformation of pentose through the oxidative and non-oxidative pentose phosphate pathways and has the ability to take up small organic compounds. It encodes a multitude of heavy metal resistance systems to adapt the heavy metal-containing environments.

Project description:BackgroundThe genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks.ResultsWe have evaluated the method using Lactococcus lactis IL1403, for which a genome-scale metabolic network was published recently. We recovered most of the gene-reaction associations (i.e. 74 - 85%) which are incorporated in the published network. Moreover, we predicted over 200 additional genes to be associated to reactions, including genes with unknown function, genes for transporters and genes with specific metabolic reactions, which are good candidates for an extension to the previously published network. In a comparison of our developed method with the well-established approach Pathologic, we predicted 186 additional genes to be associated to reactions. We also predicted a relatively high number of complete conserved protein complexes, which are derived from curated metabolic networks, illustrating the potential predictive power of our method for protein complexes.ConclusionWe show that our methodology can be applied to accelerate the reconstruction of genome-scale metabolic networks by taking optimal advantage of existing, manually curated networks. As orthology detection is the first step in the method, only the translated open reading frames (ORFs) of a newly sequenced genome are necessary to reconstruct a metabolic network. When more manually curated metabolic networks will become available in the near future, the usefulness of our method in network prediction is likely to increase.

Project description:BackgroundA central problem in computational metabolic modelling is how to find biochemically plausible pathways between metabolites in a metabolic network. Two general, complementary frameworks have been utilized to find metabolic pathways: constraint-based modelling and graph-theoretical path finding approaches. In constraint-based modelling, one aims to find pathways where metabolites are balanced in a pseudo steady-state. Constraint-based methods, such as elementary flux mode analysis, have typically a high computational cost stemming from a large number of steady-state pathways in a typical metabolic network. On the other hand, graph-theoretical approaches avoid the computational complexity of constraint-based methods by solving a simpler problem of finding shortest paths. However, while scaling well with network size, graph-theoretic methods generally tend to return more false positive pathways than constraint-based methods.ResultsIn this paper, we introduce a computational method, ReTrace, for finding biochemically relevant, branching metabolic pathways in an atom-level representation of metabolic networks. The method finds compact pathways which transfer a high fraction of atoms from source to target metabolites by considering combinations of linear shortest paths. In contrast to current steady-state pathway analysis methods, our method scales up well and is able to operate on genome-scale models. Further, we show that the pathways produced are biochemically meaningful by an example involving the biosynthesis of inosine 5'-monophosphate (IMP). In particular, the method is able to avoid typical problems associated with graph-theoretic approaches such as the need to define side metabolites or pathways not carrying any net carbon flux appearing in results. Finally, we discuss an application involving reconstruction of amino acid pathways of a recently sequenced organism demonstrating how measurement data can be easily incorporated into ReTrace analysis. ReTrace is licensed under GPL and is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/retrace/.ConclusionReTrace is a useful method in metabolic path finding tasks, combining some of the best aspects in constraint-based and graph-theoretic methods. It finds use in a multitude of tasks ranging from metabolic engineering to metabolic reconstruction of recently sequenced organisms.

Dataset Information

Environmental versatility promotes modularity in genome-scale metabolic networks.

Background

Results

Conclusions

Publications

Environmental versatility promotes modularity in genome-scale metabolic networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets