A proof for loop-law constraints in stoichiometric metabolic networks

Noor, Elad; Lewis, Nathan E.; Milo, Ron

doi:10.1186/1752-0509-6-140

Cited by 26 publications

(23 citation statements)

References 17 publications

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“…This inhibits the net production of phosphatidylinositol by most cell line-specific models (Figures 4d–e). Thus, even if a metabolite is present in a cell line-specific model, the model may not be able to predict the physiological metabolic function of producing the metabolite due to “loops” resulting from the steady state assumption of COBRA models (Noor et al, 2012). …”

Section: Resultsmentioning

confidence: 99%

A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models

et al. 2017

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Genome-scale models of metabolism can illuminate the molecular basis of cell phenotypes. Since many enzymes are only active in specific cell types, several algorithms use omics data to construct cell line- and tissue-specific metabolic models from genome-scale models. However, these methods have not been rigorously benchmarked, and it is unclear how algorithm and parameter selection (e.g., gene expression thresholds, metabolic constraints) impacts model content and predictive accuracy. To investigate this, we built hundreds of models of four different cancer cell lines using six algorithms, four gene expression thresholds and three sets of metabolic constraints. Model content varied substantially across different parameter sets, but model extraction method choice had the largest impact on the accuracy of model-predicted gene essentiality. We further highlight how assumptions during model development influence the accuracy of model prediction. These insights will guide further development of context-specific models, thus more accurately resolving genotype-phenotype relationships.

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Section: Resultsmentioning

confidence: 99%

A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models

et al. 2017

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“…ll-FBA, ll-FVA, LD and LR) using a single optimization problem based on suitable LP, MILP or MIQP formulations subject to the loopless constraints (Equation 5). Importantly, such constraints have recently been algebraically proven to always yield thermodynamically feasible flux solutions (Noor et al , 2012). …”

Section: Discussionmentioning

confidence: 99%

Fast-SNP: a fast matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models

Saa

Nielsen

2016

Bioinformatics

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Motivation: Computation of steady-state flux solutions in large metabolic models is routinely performed using flux balance analysis based on a simple LP (Linear Programming) formulation. A minimal requirement for thermodynamic feasibility of the flux solution is the absence of internal loops, which are enforced using ‘loopless constraints’. The resulting loopless flux problem is a substantially harder MILP (Mixed Integer Linear Programming) problem, which is computationally expensive for large metabolic models.Results: We developed a pre-processing algorithm that significantly reduces the size of the original loopless problem into an easier and equivalent MILP problem. The pre-processing step employs a fast matrix sparsification algorithm—Fast- sparse null-space pursuit (SNP)—inspired by recent results on SNP. By finding a reduced feasible ‘loop-law’ matrix subject to known directionalities, Fast-SNP considerably improves the computational efficiency in several metabolic models running different loopless optimization problems. Furthermore, analysis of the topology encoded in the reduced loop matrix enabled identification of key directional constraints for the potential permanent elimination of infeasible loops in the underlying model. Overall, Fast-SNP is an effective and simple algorithm for efficient formulation of loop-law constraints, making loopless flux optimization feasible and numerically tractable at large scale.Availability and Implementation: Source code for MATLAB including examples is freely available for download at http://www.aibn.uq.edu.au/cssb-resources under Software. Optimization uses Gurobi, CPLEX or GLPK (the latter is included with the algorithm).Contact: lars.nielsen@uq.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.

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“…This method imposes the second law of thermodynamics by using a mixed-integer linear programming (MILP) approach to constrain flux solutions so that they obey the loop law and does not require additional data such as metabolite concentrations or thermodynamic parameters. This method was mathematically proved not to over-constrain the problem beyond the elimination of the loops themselves [79]. However, such a method was unable to be conducted with the commonly used solver GNU Linear Programming Kit (GLPK) and necessitates a commercial solver (TOMLAB/CPLEX package (Tomlab Research, Pullman, WA)).…”

Section: Futile Loopsmentioning

confidence: 99%

The Problem of Futile Cycles in Metabolic Flux Modeling: Flux Space Characterization and Practical Approaches to Its Solution

Verwoerd

Mao

2014

Simulation Foundations, Methods and Applications

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A metabolic flux model of an organism can be developed from the genome-scale metabolic network (GEM) for quantitatively understanding and simulating the phenotypes of metabolic systems. For the development of the flux model, the GEM serves as a framework that integrate all of the massive "omics" data derived from systems biology research, comprising (i) gene detection (genomics), (ii) gene expression (transcriptomics), (iii) protein expression and modifications (proteomics), (iv) primary and secondary metabolites production (metabolomics), (v) measurement and estimation reaction rates (fluxes) for a network of reactions that occur in an organism (fluxomics) [1], and (vi) large-scale literature mining (bibliomics) [1][2][3].Due to the advances in the high-throughput "omics" technologies and computer capabilities, there has been an exponential increase in GEMs reconstructed for a wide variety of organisms since the first GEM was built in 1999 [4]. As the GEMs intend to include as large part of the cell metabolism as possible and as much biological information as possible [5], they provide a detailed representation of biological reaction networks and their functional states [6], and can be used as analysis platforms for computational systems approaches such as constraint-based modeling [3], to characterize the flux profiles of the microbial phenotypes. This type of analysis can generate new knowledge that facilitates metabolic engineering of interesting biotechnological processes at the whole-cell level and can overcome the difficulties experienced in reductionist investigative strategies. Therefore, using in silico modeling approaches to develop strains has been considered as a promising area in the field of systems biology.

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A proof for loop-law constraints in stoichiometric metabolic networks

Cited by 26 publications

References 17 publications

A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models

A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models

Fast-SNP: a fast matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models

The Problem of Futile Cycles in Metabolic Flux Modeling: Flux Space Characterization and Practical Approaches to Its Solution

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