1,450 research outputs found
MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models
Metabolomic data sets provide a direct read-out of cellular phenotypes and
are increasingly generated to study biological questions. Our previous work
revealed the potential of analyzing extracellular metabolomic data in the
context of the metabolic model using constraint-based modeling. Through this
work, which consists of a protocol, a toolbox, and tutorials of two use cases,
we make our methods available to the broader scientific community. The protocol
describes, in a step-wise manner, the workflow of data integration and
computational analysis. The MetaboTools comprise the Matlab code required to
complete the workflow described in the protocol. Tutorials explain the
computational steps for integration of two different data sets and demonstrate
a comprehensive set of methods for the computational analysis of metabolic
models and stratification thereof into different phenotypes. The presented
workflow supports integrative analysis of multiple omics data sets.
Importantly, all analysis tools can be applied to metabolic models without
performing the entire workflow. Taken together, this protocol constitutes a
comprehensive guide to the intra-model analysis of extracellular metabolomic
data and a resource offering a broad set of computational analysis tools for a
wide biomedical and non-biomedical research community
Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome
The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.T32GM008764 - NIGMS NIH HHS; T32 GM008764 - NIGMS NIH HHS; R01 DE024468 - NIDCR NIH HHS; R01 GM121950 - NIGMS NIH HHS; DE-SC0012627 - Biological and Environmental Research; RGP0020/2016 - Human Frontier Science Program; NSFOCE-BSF 1635070 - National Science Foundation; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; R37DE016937 - NIDCR NIH HHS; R37 DE016937 - NIDCR NIH HHS; R01GM121950 - NIGMS NIH HHS; R01DE024468 - NIDCR NIH HHS; 1457695 - National Science FoundationPublished versio
Quantifying biosynthetic network robustness across the human oral microbiome
Metabolic interactions, such as cross-feeding, play a prominent role in microbial communitystructure. For example, they may underlie the ubiquity of uncultivated microorganisms. We investigated this phenomenon in the human oral microbiome, by analyzing microbial metabolic networks derived from sequenced genomes. Specifically, we devised a probabilistic biosynthetic network robustness metric that describes the chance that an organism could produce a given metabolite, and used it to assemble a comprehensive atlas of biosynthetic capabilities for 88 metabolites across 456 human oral microbiome strains. A cluster of organisms characterized by reduced biosynthetic capabilities stood out within this atlas. This cluster included several uncultivated taxa and three recently co-cultured Saccharibacteria (TM7) phylum species. Comparison across strains also allowed us to systematically identify specific putative metabolic interdependences between organisms. Our method, which provides a new way of converting annotated genomes into metabolic predictions, is easily extendible to other microbial communities and metabolic products.https://www.biorxiv.org/content/10.1101/392621v1First author draf
Prediction from Enzymatic Rate Laws and Cost Minimization
Bacterial growth depends crucially on metabolic fluxes, which are limited by
the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount
per unit flux is a major determinant of metabolic strategies both in evolution
and bioengineering. It depends on enzyme parameters (such as kcat and KM
constants), but also on metabolite concentrations. Moreover, similar amounts
of different enzymes might incur different costs for the cell, depending on
enzyme-specific properties such as protein size and half-life. Here, we
developed enzyme cost minimization (ECM), a scalable method for computing
enzyme amounts that support a given metabolic flux at a minimal protein cost.
The complex interplay of enzyme and metabolite concentrations, e.g. through
thermodynamic driving forces and enzyme saturation, would make it hard to
solve this optimization problem directly. By treating enzyme cost as a
function of metabolite levels, we formulated ECM as a numerically tractable,
convex optimization problem. Its tiered approach allows for building models at
different levels of detail, depending on the amount of available data.
Validating our method with measured metabolite and protein levels in E. coli
central metabolism, we found typical prediction fold errors of 4.1 and 2.6,
respectively, for the two kinds of data. This result from the cost-optimized
metabolic state is significantly better than randomly sampled metabolite
profiles, supporting the hypothesis that enzyme cost is important for the
fitness of E. coli. ECM can be used to predict enzyme levels and protein cost
in natural and engineered pathways, and could be a valuable computational tool
to assist metabolic engineering projects. Furthermore, it establishes a direct
connection between protein cost and thermodynamics, and provides a physically
plausible and computationally tractable way to include enzyme kinetics into
constraint-based metabolic models, where kinetics have usually been ignored or
oversimplified
A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information
An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism
Elasticity sampling links thermodynamics to metabolic control
Metabolic networks can be turned into kinetic models in a predefined steady
state by sampling the reaction elasticities in this state. Elasticities for
many reversible rate laws can be computed from the reaction Gibbs free
energies, which are determined by the state, and from physically unconstrained
saturation values. Starting from a network structure with allosteric regulation
and consistent metabolic fluxes and concentrations, one can sample the
elasticities, compute the control coefficients, and reconstruct a kinetic model
with consistent reversible rate laws. Some of the model variables are manually
chosen, fitted to data, or optimised, while the others are computed from them.
The resulting model ensemble allows for probabilistic predictions, for
instance, about possible dynamic behaviour. By adding more data or tighter
constraints, the predictions can be made more precise. Model variants differing
in network structure, flux distributions, thermodynamic forces, regulation, or
rate laws can be realised by different model ensembles and compared by
significance tests. The thermodynamic forces have specific effects on flux
control, on the synergisms between enzymes, and on the emergence and
propagation of metabolite fluctuations. Large kinetic models could help to
simulate global metabolic dynamics and to predict the effects of enzyme
inhibition, differential expression, genetic modifications, and their
combinations on metabolic fluxes. MATLAB code for elasticity sampling is freely
available
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
Systems approaches to modelling pathways and networks.
Peer reviewedPreprin
Flux balance analysis of metabolic models: a review of recent advances and applications
2016 Spring.Includes bibliographical references.Genome-level reconstructions of metabolic networks have provided new insight into the cellular functions of many organisms. These metabolic models are massive constructs, often including thousands of metabolic and transport reactions and metabolite species for even the most basic organisms. Construction of these models has typically involved an initial genomic analysis to identify known genes or genes with homologous structures for which the function may be inferred, followed by an intensive process of literature searching and experimental validation to refine the model. A number of automated algorithms have been developed to assist with this process. Once the model has been constructed, optimization techniques are applied to predict the distribution of fluxes through the reaction network. The systems then studied by FBA are generally static systems, assumed to be operating at a steady state, and thus constrained by the stoichiometries of the reactions rather than the kinetics. While these assumptions have shown to be valid under select laboratory conditions, evidence indicates that most organisms are not always at this steady state. A number of model improvements have been considered to bring predicted results more in line with experimental data, including the addition of regulatory controls, more detailed incorporation of thermodynamics, and the consideration of metabolite pool and flux data from metabolomics and labeled carbon studies, respectively. The improved predictive capabilities of these models readily find application in metabolic engineering in the custom strain design of organisms. Often this purpose is the production of some valuable bioproduct. This review seeks to give overview the advances made on both the model construction and application ends, with particular emphasis on model improvements via more complex constraints and the incorporation of experimental data
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