Use of Randomized Sampling for Analysis of Metabolic Networks*

@article{Schellenberger2009UseOR,
  title={Use of Randomized Sampling for Analysis of Metabolic Networks*},
  author={Jan Schellenberger and Bernhard O. Palsson},
  journal={Journal of Biological Chemistry},
  year={2009},
  volume={284},
  pages={5457 - 5461}
}
Genome-scale metabolic network reconstructions in microorganisms have been formulated and studied for about 8 years. The constraint-based approach has shown great promise in analyzing the systemic properties of these network reconstructions. Notably, constraint-based models have been used successfully to predict the phenotypic effects of knock-outs and for metabolic engineering. The inherent uncertainty in both parameters and variables of large-scale models is significant and is well suited to… 

Figures and Tables from this paper

Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments
TLDR
RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) is developed which uses both parsimony of overall flux and transcriptomic abundances to identify the most cost-effective usage of metabolism that also best reflects the cell’s investments into transcription.
Constraint-based Analysis of Substructures of Metabolic Networks
TLDR
It is shown that two uncoupled reactions in a metabolic network may be detected as directionally, partially or fully coupled in an incomplete version of the same network, which is the opposite of the flux coupling changes that may happen due to the existence of missing reactions.
Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling.
TLDR
A novel, generic algorithm to characterize the entire flux space of GSMR upon application of FBA, leading to the optimal value of the objective (the optimal flux space), which is shown to surpass the commonly used Monte Carlo Sampling in providing a more uniform coverage for a much larger network in less number of samples.
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox
TLDR
This software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules.
Integrated Host-Pathogen Metabolic Reconstructions.
TLDR
The protocol here describes the detailed process of network and stoichiometric matrix merger using a salmonella-mouse macrophage model and discusses the interfacial and objective functions required to actually embark on the analysis of host-pathogen interaction models.
Flux Measurement Selection in Metabolic Networks
TLDR
This article proposes a method that combines a sampling approach with a greedy algorithm for finding a subset of k fluxes that, if measured, are expected to reduce as much as possible the solution space towards the 'true' flux distribution.
Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm
TLDR
Maximum Metabolic Flexibility (MMF) is proposed, a computational method that utilizes observation that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more “flexible” metabolic network, and can be applied to any cell type without requiring prior information.
Scalable metabolic pathway analysis
TLDR
The minimal pathways (MPs) of a metabolic (sub)network as a subset of its elementary flux vectors are defined and enumerated efficiently using iterative minimization and a simple graph representation of MPs open up new possibilities for the detailed analysis of large-scale metabolic networks.
A Scalable Algorithm to Explore the Gibbs Energy Landscape of Genome-Scale Metabolic Networks
TLDR
A fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state is proposed and identified as the origin of thermodynamic infeasibility in a large sample of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility.
Parsimonious transcript data integration improves context-specific predictions of bacterial metabolism in complex environments
TLDR
The new RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both parsimony of overall flux and transcriptomic abundances to identify the most cost-effective usage of metabolism that also best reflects the cell’s investments into transcription.
...
...

References

SHOWING 1-10 OF 35 REFERENCES
Estimating the size of the solution space of metabolic networks
TLDR
A novel efficient distributed algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints being still efficient on the analysis of large biological systems, where exact deterministic methods experience an explosion in algorithmic time.
The Activity Reaction Core and Plasticity of Metabolic Networks
TLDR
Flux-balance analysis is used to thoroughly assess the activity of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments and finds that most current antibiotics interfering with bacterial metabolism target the core enzymes.
Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies.
TLDR
The Monte Carlo sampling procedure provides a broadening of the constraint-based approach by allowing for the unbiased and detailed assessment of the impact of the applied physicochemical constraints on a reconstructed network.
Genome-scale models of microbial cells: evaluating the consequences of constraints
TLDR
This work has shown that a constraint-based reconstruction and analysis approach provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells.
Integrating high-throughput and computational data elucidates bacterial networks
TLDR
This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks.
Structural kinetic modeling of metabolic networks
TLDR
This work proposes a method that aims to give a quantitative account of the dynamical capabilities of a metabolic system, without requiring any explicit information about the functional form of the rate equations.
Metabolic flux analysis and metabolic engineering of microorganisms.
TLDR
Various computational aspects of constraints-based flux analysis including genome-scale stoichiometric models, additional constraints used for the improved accuracy, and several algorithms for identifying the target genes to be manipulated are described.
The stability and robustness of metabolic states: identifying stabilizing sites in metabolic networks
TLDR
The findings show that allosteric enzyme regulation significantly enhances the stability of the network and extends its potential dynamic behavior, and the proposed method represents an important intermediate step on the long way from topological network analysis to detailed kinetic modeling of complex metabolic networks.
Candidate states of Helicobacter pylori's genome-scale metabolic network upon application of "loop law" thermodynamic constraints.
TLDR
A four-step approach is developed here to apply the loop-law to study metabolic network properties: determine linear equality constraints that are necessary (but not necessarily sufficient) for thermodynamic feasibility; tighten V(max) and V(min) constraints to enclose the remaining nonconvex space; uniformly sample the convex space that encloses the nonconcex space using standard Monte Carlo techniques.
...
...