7,648 research outputs found
On the effects of alternative optima in context-specific metabolic model predictions
Recent methodological developments have facilitated the integration of
high-throughput data into genome-scale models to obtain context-specific
metabolic reconstructions. A unique solution to this data integration problem
often may not be guaranteed, leading to a multitude of context-specific
predictions equally concordant with the integrated data. Yet, little attention
has been paid to the alternative optima resulting from the integration of
context-specific data. Here we present computational approaches to analyze
alternative optima for different context-specific data integration instances.
By using these approaches on metabolic reconstructions for the leaf of
Arabidopsis thaliana and the human liver, we show that the analysis of
alternative optima is key to adequately evaluating the specificity of the
predictions in particular cellular contexts. While we provide several ways to
reduce the ambiguity in the context-specific predictions, our findings indicate
that the existence of alternative optimal solutions warrant caution in detailed
context-specific analyses of metabolism
Dynamic optimization of metabolic networks coupled with gene expression
The regulation of metabolic activity by tuning enzyme expression levels is
crucial to sustain cellular growth in changing environments. Metabolic networks
are often studied at steady state using constraint-based models and
optimization techniques. However, metabolic adaptations driven by changes in
gene expression cannot be analyzed by steady state models, as these do not
account for temporal changes in biomass composition. Here we present a dynamic
optimization framework that integrates the metabolic network with the dynamics
of biomass production and composition, explicitly taking into account enzyme
production costs and enzymatic capacity. In contrast to the established dynamic
flux balance analysis, our approach allows predicting dynamic changes in both
the metabolic fluxes and the biomass composition during metabolic adaptations.
We applied our algorithm in two case studies: a minimal nutrient uptake
network, and an abstraction of core metabolic processes in bacteria. In the
minimal model, we show that the optimized uptake rates reproduce the empirical
Monod growth for bacterial cultures. For the network of core metabolic
processes, the dynamic optimization algorithm predicted commonly observed
metabolic adaptations, such as a diauxic switch with a preference ranking for
different nutrients, re-utilization of waste products after depletion of the
original substrate, and metabolic adaptation to an impending nutrient
depletion. These examples illustrate how dynamic adaptations of enzyme
expression can be predicted solely from an optimization principle
MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models
Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads
Constrained Allocation Flux Balance Analysis
New experimental results on bacterial growth inspire a novel top-down
approach to study cell metabolism, combining mass balance and proteomic
constraints to extend and complement Flux Balance Analysis. We introduce here
Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic
costs associated to growth are accounted for in an effective way through a
single additional genome-wide constraint. Its roots lie in the experimentally
observed pattern of proteome allocation for metabolic functions, allowing to
bridge regulation and metabolism in a transparent way under the principle of
growth-rate maximization. We provide a simple method to solve CAFBA efficiently
and propose an "ensemble averaging" procedure to account for unknown protein
costs. Applying this approach to modeling E. coli metabolism, we find that, as
the growth rate increases, CAFBA solutions cross over from respiratory,
growth-yield maximizing states (preferred at slow growth) to fermentative
states with carbon overflow (preferred at fast growth). In addition, CAFBA
allows for quantitatively accurate predictions on the rate of acetate excretion
and growth yield based on only 3 parameters determined by empirical growth
laws.Comment: 21 pages, 6 figures (main) + 33 pages, various figures and tables
(supporting); for the supplementary MatLab code, see
http://tinyurl.com/h763es
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