1,092 research outputs found
Low Degree Metabolites Explain Essential Reactions and Enhance Modularity in Biological Networks
Recently there has been a lot of interest in identifying modules at the level
of genetic and metabolic networks of organisms, as well as in identifying
single genes and reactions that are essential for the organism. A goal of
computational and systems biology is to go beyond identification towards an
explanation of specific modules and essential genes and reactions in terms of
specific structural or evolutionary constraints. In the metabolic networks of
E. coli, S. cerevisiae and S. aureus, we identified metabolites with a low
degree of connectivity, particularly those that are produced and/or consumed in
just a single reaction. Using FBA we also determined reactions essential for
growth in these metabolic networks. We find that most reactions identified as
essential in these networks turn out to be those involving the production or
consumption of low degree metabolites. Applying graph theoretic methods to
these metabolic networks, we identified connected clusters of these low degree
metabolites. The genes involved in several operons in E. coli are correctly
predicted as those of enzymes catalyzing the reactions of these clusters. We
independently identified clusters of reactions whose fluxes are perfectly
correlated. We find that the composition of the latter `functional clusters' is
also largely explained in terms of clusters of low degree metabolites in each
of these organisms. Our findings mean that most metabolic reactions that are
essential can be tagged by one or more low degree metabolites. Those reactions
are essential because they are the only ways of producing or consuming their
respective tagged metabolites. Furthermore, reactions whose fluxes are strongly
correlated can be thought of as `glued together' by these low degree
metabolites.Comment: 12 pages main text with 2 figures and 2 tables. 16 pages of
Supplementary material. Revised version has title changed and contains study
of 3 organisms instead of 1 earlie
Complex networks theory for analyzing metabolic networks
One of the main tasks of post-genomic informatics is to systematically
investigate all molecules and their interactions within a living cell so as to
understand how these molecules and the interactions between them relate to the
function of the organism, while networks are appropriate abstract description
of all kinds of interactions. In the past few years, great achievement has been
made in developing theory of complex networks for revealing the organizing
principles that govern the formation and evolution of various complex
biological, technological and social networks. This paper reviews the
accomplishments in constructing genome-based metabolic networks and describes
how the theory of complex networks is applied to analyze metabolic networks.Comment: 13 pages, 2 figure
The compositional and evolutionary logic of metabolism
Metabolism displays striking and robust regularities in the forms of
modularity and hierarchy, whose composition may be compactly described. This
renders metabolic architecture comprehensible as a system, and suggests the
order in which layers of that system emerged. Metabolism also serves as the
foundation in other hierarchies, at least up to cellular integration including
bioenergetics and molecular replication, and trophic ecology. The
recapitulation of patterns first seen in metabolism, in these higher levels,
suggests metabolism as a source of causation or constraint on many forms of
organization in the biosphere.
We identify as modules widely reused subsets of chemicals, reactions, or
functions, each with a conserved internal structure. At the small molecule
substrate level, module boundaries are generally associated with the most
complex reaction mechanisms and the most conserved enzymes. Cofactors form a
structurally and functionally distinctive control layer over the small-molecule
substrate. Complex cofactors are often used at module boundaries of the
substrate level, while simpler ones participate in widely used reactions.
Cofactor functions thus act as "keys" that incorporate classes of organic
reactions within biochemistry.
The same modules that organize the compositional diversity of metabolism are
argued to have governed long-term evolution. Early evolution of core
metabolism, especially carbon-fixation, appears to have required few
innovations among a small number of conserved modules, to produce adaptations
to simple biogeochemical changes of environment. We demonstrate these features
of metabolism at several levels of hierarchy, beginning with the small-molecule
substrate and network architecture, continuing with cofactors and key conserved
reactions, and culminating in the aggregation of multiple diverse physical and
biochemical processes in cells.Comment: 56 pages, 28 figure
Conservation of high-flux backbone in alternate optimal and near-optimal flux distributions of metabolic networks
Constraint-based flux balance analysis (FBA) has proven successful in
predicting the flux distribution of metabolic networks in diverse environmental
conditions. FBA finds one of the alternate optimal solutions that maximizes the
biomass production rate. Almaas et al have shown that the flux distribution
follows a power law, and it is possible to associate with most metabolites two
reactions which maximally produce and consume a give metabolite, respectively.
This observation led to the concept of high-flux backbone (HFB) in metabolic
networks. In previous work, the HFB has been computed using a particular optima
obtained using FBA. In this paper, we investigate the conservation of HFB of a
particular solution for a given medium across different alternate optima and
near-optima in metabolic networks of E. coli and S. cerevisiae. Using flux
variability analysis (FVA), we propose a method to determine reactions that are
guaranteed to be in HFB regardless of alternate solutions. We find that the HFB
of a particular optima is largely conserved across alternate optima in E. coli,
while it is only moderately conserved in S. cerevisiae. However, the HFB of a
particular near-optima shows a large variation across alternate near-optima in
both organisms. We show that the conserved set of reactions in HFB across
alternate near-optima has a large overlap with essential reactions and
reactions which are both uniquely consuming (UC) and uniquely producing (UP).
Our findings suggest that the structure of the metabolic network admits a high
degree of redundancy and plasticity in near-optimal flow patterns enhancing
system robustness for a given environmental condition.Comment: 11 pages, 6 figures, to appear in Systems and Synthetic Biolog
Environmental versatility promotes modularity in genome-scale metabolic networks
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
Opportunities at the interface of network science and metabolic modeling
Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology
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