HOPS: high-performance library for (non-)uniform sampling of convex-constrained models

Jadebeck, Johann Fredrik; Theorell, Axel; Leweke, Samuel; Nöh, Katharina

doi:10.1093/bioinformatics/btaa872

Cited by 17 publications

(17 citation statements)

References 13 publications

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“…Firstly, for a given 13 C-MFA model the feasible flux space is sampled uniformly providing n S flux map samples V n S (SAMPLEFLUXSPACE). In case that prior knowledge about the fluxes exists, e.g., in form of a joint confidence ellipsoid, non-uniform Gaussian sampling can be employed instead (Jadebeck et al, 2020).…”

Section: R-ed Workflowmentioning

confidence: 99%

“…To this end, the ssampler tool of the high performance simulator 13CFLUX2 is called, which implements the Markov Chain Monte Carlo algorithm Hit-and-Run (Bélisle et al, 1993). Another alternative is to call the sampling suite HOPS (Jadebeck et al, 2020), a library which contains various efficient sampling algorithms specially tailored to polytope sampling. Sampling results are collected in a HDF5 file (samples.hdf5).…”

Section: Implementation Of the R-ed Workflowmentioning

confidence: 99%

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Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis

Beyß

Parra-Peña

Ramírez-Malule

et al. 2021

Front. Bioeng. Biotechnol.

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13C metabolic flux analysis (MFA) has become an indispensable tool to measure metabolic reaction rates (fluxes) in living organisms, having an increasingly diverse range of applications. Here, the choice of the13C labeled tracer composition makes the difference between an information-rich experiment and an experiment with only limited insights. To improve the chances for an informative labeling experiment, optimal experimental design approaches have been devised for13C-MFA, all relying on some a priori knowledge about the actual fluxes. If such prior knowledge is unavailable, e.g., for research organisms and producer strains, existing methods are left with a chicken-and-egg problem. In this work, we present a general computational method, termed robustified experimental design (R-ED), to guide the decision making about suitable tracer choices when prior knowledge about the fluxes is lacking. Instead of focusing on one mixture, optimal for specific flux values, we pursue a sampling based approach and introduce a new design criterion, which characterizes the extent to which mixtures are informative in view of all possible flux values. The R-ED workflow enables the exploration of suitable tracer mixtures and provides full flexibility to trade off information and cost metrics. The potential of the R-ED workflow is showcased by applying the approach to the industrially relevant antibiotic producer Streptomyces clavuligerus, where we suggest informative, yet economic labeling strategies.

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Section: R-ed Workflowmentioning

confidence: 99%

Section: Implementation Of the R-ed Workflowmentioning

confidence: 99%

Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis

Beyß

Parra-Peña

Ramírez-Malule

et al. 2021

Front. Bioeng. Biotechnol.

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“…The main advantage of these modeling techniques over other approaches, such as dynamic modeling (Dräger et al, 2009 ), lies in their potential to analyze entire metabolic networks at the scale of all enzymatic capabilities of an organism without the necessity of knowing numerical values of all the kinetic parameters therein. Flux sampling can be used as an unbiased way to characterize the space of stoichiometrically feasible fluxes and solutions (Jadebeck et al, 2020 ). Flux balance analysis (FBA) is a biased method for steady-state analysis of GEMs.…”

Section: Introductionmentioning

confidence: 99%

High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032

et al. 2021

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Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe's productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1042 metabolites, 1539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid's production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.

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“…The field is actively developed, for example, with recent extensions to model inference ( Theorell and Nöh, 2020 ). Algorithmically, Markov Chain Monte Carlo (MCMC) based coordinate hit-and-run with rounding (CHRR) ( Haraldsdóttir et al , 2017 ) showed superior performance in computational benchmarks ( Herrmann et al , 2019 ) and it is available in a highly efficient and modular implementation ( Jadebeck et al , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

PolyRound: polytope rounding for random sampling in metabolic networks

et al. 2021

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Summary Random flux sampling is a powerful tool for the constraint-based analysis of metabolic networks. The most efficient sampling method relies on a rounding transform of the constraint polytope, but no available rounding implementation can round all relevant models. By removing redundant polytope constraints on the go, PolyRound simplifies the numerical problem and rounds all the 108 models in the BiGG database without parameter tuning, compared to about 50% for the state-of-the-art implementation. Availability The implementation is available on gitlab: https://gitlab.com/csb.ethz/PolyRound Supplementary information Supplementary data are available at Bioinformatics online.

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HOPS: high-performance library for (non-)uniform sampling of convex-constrained models

Cited by 17 publications

References 13 publications

Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis

Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis

High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032

PolyRound: polytope rounding for random sampling in metabolic networks

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