Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Heirendt, Laurent; Arreckx, Sylvain; Pfau, Thomas; Mendoza, Sebastián N.; Richelle, Anne; Heinken, Almut; Haraldsdóttir, Hulda S.; Wachowiak, Jacek; Keating, Sarah M.; Vlasov, Vanja; Magnúsdóttir, Stefanía; Ng, C. Y.; Preciat, German; Žagare, Alise; Chan, Siu Hung Joshua; Aurich, Maike Kathrin; Clancy, Catherine M.; Modamio, Jennifer; Sauls, John T.; Noronha, Alberto; Bordbar, Aarash; Cousins, Benjamin; Assal, Diana Charles El; Valcárcel, Luis Vítores; Apaolaza, Iñigo; Ghaderi, Susan; Ahookhosh, Masoud; Guebila, Marouen Ben; Kostromins, Andrejs; Sompairac, Nicolas; Le, Hoai M.; Ma, Ding; Sun, Yuekai; Wang, Lin; Yurkovich, James T.; Oliveira, Miguel A. P.; Vuong, Phan Tu; Assal, Lemmer P. El; Kuperstein, Inna; Zinovyev, Andreï; Hinton, H.S.; Bryant, William A; Artacho, Francisco J. Aragón; Planes, Francisco J.; Stalidzāns, Egils; Maass, Alejandro; Vempala, Santosh; Hucka, Michael; Saunders, Michael A.; Maranas, Costas D.; Lewis, Nathan E.; Sauter, Thomas; Palsson, Bernhard Ø.; Thiele, Ines; Fleming, Ronan M. T.

doi:10.1038/s41596-018-0098-2

Cited by 927 publications

(867 citation statements)

References 159 publications

Supporting

Mentioning

857

Contrasting

Unclassified

Order By: Relevance

“…Various computational approaches, featured by flux balance analysis and its variations, have been developed to characterize the steady‐state flux solution space. The community has also developed high‐quality tools to help maximize the applications of GEMs (Ebrahim, Lerman, Palsson, & Hyduke, ; Heirendt et al, ; H. Wang et al, ). Nevertheless, the optimal solution from these GEMs can easily deviate from the real flux distribution.…”

Section: Quantitative Metabolomics and Its Application In Systems Metmentioning

confidence: 99%

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

View full text Add to dashboard Cite

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

show abstract

Section: Quantitative Metabolomics and Its Application In Systems Metmentioning

confidence: 99%

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

View full text Add to dashboard Cite

show abstract

“…Flux variability analysis 68 was carried out to determine bounds for reactions in metabolic networks. We used COBRA toolbox v3.0 69 for metabolic analysis that was implemented in MATLAB R2018a and academic licenses of Gurobi optimizer v7.5 and IBM CPLEX v12.7.1 were used to solve LP and MILP problems.…”

Section: Brain Region-specific Metabolic Reconstructionmentioning

confidence: 99%

Identifying differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease using metabolic networks of human brain regions

Baloni

Funk

Yan

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Alzheimer's disease (AD) is the leading cause of dementia, with metabolic dysfunction seen years before the emergence of clinical symptoms. Increasing evidence suggests a role for primary and secondary bile acids, the end-product of cholesterol metabolism, influencing pathophysiology in AD. In this study, we analyzed transcriptomes from 2114 post-mortem brain samples from three independent cohorts and identified that the genes involved in alternative bile acid synthesis pathway was expressed in brain compared to the classical pathway. These results were supported by targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals. We reconstructed brain region-specific metabolic networks using data from three independent cohorts to assess the role of bile acid metabolism in AD pathophysiology. Our metabolic network analysis suggested that taurine transport, bile acid synthesis and cholesterol metabolism differed in AD and cognitively normal individuals. Using the brain transcriptional regulatory network, we identified putative transcription factors regulating these metabolic genes and influencing altered metabolism in AD. Intriguingly, we find bile acids from the brain metabolomics whose synthesis cannot be explained by enzymes we find in the brain, suggesting they may originate from an external source such as the gut microbiome. These findings motivate further research into bile acid metabolism and transport in AD to elucidate their possible connection to cognitive decline.

show abstract

“…Lastly, the 'EX_phyt(e)' reaction upper boundary was set to -0.1 mmol·gDW -1 ·h -1 to force the model to utilise phytanic acid at a minimum rate of 0.1 mmol·gDW -1 ·h -1 for the simulations resembling fibroblasts with phytol added to the medium. To sample the solution space of generated models, ACHR algorithm [33] implemented in the COBRA Toolbox 3.0 [65] was used. Randomly selected 10000 sampled points were saved with from the total of 50000 sampled points with a 500 step size.…”

Section: Model Analysis Refsum Simulationsmentioning

confidence: 99%

“…Model curation and all simulations were carried out with MatLab R2019a (MathWorks Inc., Natick, MA) using the Gurobi8.1 (Gurobi Optimization Inc., Houston TX) linear programming solver and the COBRA 3.0 toolbox [65]. Refsum disease (RD) fibroblasts were incubated with or without phytol, the precursor of phytanic acid, for the indicated time points.…”

Section: Softwarementioning

confidence: 99%

See 1 more Smart Citation

Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease

Wegrzyn

Herzog

Gerding

et al. 2019

Preprint

View full text Add to dashboard Cite

Refsum disease is an inborn error of metabolism that is characterised by a defect in peroxisomal α oxidation of the branched-chain fatty acid phytanic acid. The disorder presents with late-onset progressive retinitis pigmentosa and polyneuropathy and can be diagnosed biochemically by elevated levels of phytanic acid in plasma and tissues of patients. To date, no cure exists for Refsum disease, but phytanic acid levels in patients can be reduced by plasmapheresis and a strict diet.In recent years, computational models have become valuable tools to provide insight into the complex behaviour of metabolic networks. Besides the comprehensive models that contain all known metabolic reactions within the human body, more than a few tissue-and cell-type-specific models have been developed. However, while systems biology approaches are widely used for complex diseases, only few studies have been published for inborn errors of metabolism.In this study, we reconstructed a fibroblast-specific genome-scale model based on the recently published, FAD-curated model, based on Recon3D reconstruction. We used transcriptomics, metabolomics, and proteomics data, which we obtained from healthy controls and Refsum disease patient fibroblasts incubated with phytol, a precursor of phytanic acid. Our model correctly represents the metabolism of phytanic acid and displays fibroblast-specific metabolic functions. Using this model, we investigated the metabolic phenotype of Refsum disease at the genome-scale, and we studied the effect of phytanic acid on cell metabolism. We identified 20 metabolites that were predicted to discriminate between Healthy and Refsum disease patients, several of which with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.

show abstract

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Cited by 927 publications

References 159 publications

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Identifying differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease using metabolic networks of human brain regions

Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease

Contact Info

Product

Resources

About