3,498 research outputs found
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
Dispensability of Escherichia coli's latent pathways
Gene-knockout experiments on single-cell organisms have established that
expression of a substantial fraction of genes is not needed for optimal growth.
This problem acquired a new dimension with the recent discovery that
environmental and genetic perturbations of the bacterium Escherichia coli are
followed by the temporary activation of a large number of latent metabolic
pathways, which suggests the hypothesis that temporarily activated reactions
impact growth and hence facilitate adaptation in the presence of perturbations.
Here we test this hypothesis computationally and find, surprisingly, that the
availability of latent pathways consistently offers no growth advantage, and
tends in fact to inhibit growth after genetic perturbations. This is shown to
be true even for latent pathways with a known function in alternate conditions,
thus extending the significance of this adverse effect beyond apparently
nonessential genes. These findings raise the possibility that latent pathway
activation is in fact derivative of another, potentially suboptimal, adaptive
response
Flux networks in metabolic graphs
A metabolic model can be represented as bipartite graph comprising linked
reaction and metabolite nodes. Here it is shown how a network of conserved
fluxes can be assigned to the edges of such a graph by combining the reaction
fluxes with a conserved metabolite property such as molecular weight. A similar
flux network can be constructed by combining the primal and dual solutions to
the linear programming problem that typically arises in constraint-based
modelling. Such constructions may help with the visualisation of flux
distributions in complex metabolic networks. The analysis also explains the
strong correlation observed between metabolite shadow prices (the dual linear
programming variables) and conserved metabolite properties. The methods were
applied to recent metabolic models for Escherichia coli, Saccharomyces
cerevisiae, and Methanosarcina barkeri. Detailed results are reported for E.
coli; similar results were found for the other organisms.Comment: 9 pages, 4 figures, RevTeX 4.0, supplementary data available (excel
Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding
The uniform sampling of convex polytopes is an interesting computational
problem with many applications in inference from linear constraints, but the
performances of sampling algorithms can be affected by ill-conditioning. This
is the case of inferring the feasible steady states in models of metabolic
networks, since they can show heterogeneous time scales . In this work we focus
on rounding procedures based on building an ellipsoid that closely matches the
sampling space, that can be used to define an efficient hit-and-run (HR) Markov
Chain Monte Carlo. In this way the uniformity of the sampling of the convex
space of interest is rigorously guaranteed, at odds with non markovian methods.
We analyze and compare three rounding methods in order to sample the feasible
steady states of metabolic networks of three models of growing size up to
genomic scale. The first is based on principal component analysis (PCA), the
second on linear programming (LP) and finally we employ the lovasz ellipsoid
method (LEM). Our results show that a rounding procedure is mandatory for the
application of the HR in these inference problem and suggest that a combination
of LEM or LP with a subsequent PCA perform the best. We finally compare the
distributions of the HR with that of two heuristics based on the Artificially
Centered hit-and-run (ACHR), gpSampler and optGpSampler. They show a good
agreement with the results of the HR for the small network, while on genome
scale models present inconsistencies.Comment: Replacement with major revision
Systems approaches to modelling pathways and networks.
Peer reviewedPreprin
Analysis of complex metabolic behavior through pathway decomposition
<p>Abstract</p> <p>Background</p> <p>Understanding complex systems through decomposition into simple interacting components is a pervasive paradigm throughout modern science and engineering. For cellular metabolism, complexity can be reduced by decomposition into pathways with particular biochemical functions, and the concept of elementary flux modes provides a systematic way for organizing metabolic networks into such pathways. While decomposition using elementary flux modes has proven to be a powerful tool for understanding and manipulating cellular metabolism, its utility, however, is severely limited since the number of modes in a network increases exponentially with its size.</p> <p>Results</p> <p>Here, we present a new method for decomposition of metabolic flux distributions into elementary flux modes. Our method can easily operate on large, genome-scale networks since it does not require all relevant modes of the metabolic network to be generated. We illustrate the utility of our method for metabolic engineering of <it>Escherichia coli </it>and for understanding the survival of <it>Mycobacterium tuberculosis </it>(MTB) during infection.</p> <p>Conclusions</p> <p>Our method can achieve computational time improvements exceeding 2000-fold and requires only several seconds to generate elementary mode decompositions on genome-scale networks. These improvements arise from not having to generate all relevant elementary modes prior to initiating the decomposition. The decompositions from our method are useful for understanding complex flux distributions and debugging genome-scale models.</p
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