scholarly journals Metabolic states with maximal specific rate carry flux through an elementary flux mode

FEBS Journal ◽  
2014 ◽  
Vol 281 (6) ◽  
pp. 1547-1555 ◽  
Author(s):  
Meike T. Wortel ◽  
Han Peters ◽  
Josephus Hulshof  ◽  
Bas Teusink ◽  
Frank J. Bruggeman
Keyword(s):  
Specific Rate ◽  
Elementary Flux Mode ◽  
Metabolic States ◽  
Flux Mode

Elementary Flux Mode

2013 ◽  
pp. 653-653
Keyword(s):  
Elementary Flux Mode ◽  
Flux Mode

scholarly journals Metabolomics integrated elementary flux mode analysis in large metabolic networks

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Matthias P. Gerstl ◽  
David E. Ruckerbauer ◽  
Diethard Mattanovich ◽  
Christian Jungreuthmayer ◽  
Jürgen Zanghellini
Keyword(s):  
Metabolic Networks ◽  
Mode Analysis ◽  
Elementary Flux Mode ◽  
Flux Mode

scholarly journals Towards scaling elementary flux mode computation

2019 ◽  
Vol 21 (6) ◽  
pp. 1875-1885
Author(s):  
Ehsan Ullah ◽  
Mona Yosafshahi ◽  
Soha Hassoun
Keyword(s):  
Pathway Analysis ◽  
Alternative Pathway ◽  
Solution Space ◽  
Computational Technique ◽  
Elementary Flux Mode ◽  
Scale Models ◽  
Network Compression ◽  
Genome Scale ◽  
The Impact ◽  
Flux Mode

Abstract While elementary flux mode (EFM) analysis is now recognized as a cornerstone computational technique for cellular pathway analysis and engineering, EFM application to genome-scale models remains computationally prohibitive. This article provides a review of aspects of EFM computation that elucidates bottlenecks in scaling EFM computation. First, algorithms for computing EFMs are reviewed. Next, the impact of redundant constraints, sensitivity to constraint ordering and network compression are evaluated. Then, the advantages and limitations of recent parallelization and GPU-based efforts are highlighted. The article then reviews alternative pathway analysis approaches that aim to reduce the EFM solution space. Despite advances in EFM computation, our review concludes that continued scaling of EFM computation is necessary to apply EFM to genome-scale models. Further, our review concludes that pathway analysis methods that target specific pathway properties can provide powerful alternatives to EFM analysis.

scholarly journals Elementary Flux Mode Analysis of Acetyl-CoA Pathway in Carboxydothermus hydrogenoformans Z-2901

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Rajadurai Chinnasamy Perumal ◽  
Ashok Selvaraj ◽  
Gopal Ramesh Kumar
Keyword(s):  
Carbon Monoxide ◽  
Hydrogen Production ◽  
Gene Knockout ◽  
Mode Analysis ◽  
Hydrogen Yield ◽  
Acetyl Coa ◽  
Elementary Flux Mode ◽  
Acetyl Coa Pathway ◽  
Carboxydothermus Hydrogenoformans ◽  
Flux Mode

Carboxydothermus hydrogenoformans is a carboxydotrophic hydrogenogenic bacterium species that produces hydrogen molecule by utilizing carbon monoxide (CO) or pyruvate as a carbon source. To investigate the underlying biochemical mechanism of hydrogen production, an elementary mode analysis of acetyl-CoA pathway was performed to determine the intermediate fluxes by combining linear programming (LP) method available in CellNetAnalyzer software. We hypothesized that addition of enzymes necessary for carbon monoxide fixation and pyruvate dissimilation would enhance the theoretical yield of hydrogen. An in silico gene knockout of pyk, pykC, and mdh genes of modeled acetyl-CoA pathway allows the maximum theoretical hydrogen yield of 47.62 mmol/gCDW/h for 1 mole of carbon monoxide (CO) uptake. The obtained hydrogen yield is comparatively two times greater than the previous experimental data. Therefore, it could be concluded that this elementary flux mode analysis is a crucial way to achieve efficient hydrogen production through acetyl-CoA pathway and act as a model for strain improvement.

regEfmtool: Speeding up elementary flux mode calculation using transcriptional regulatory rules in the form of three-state logic

Biosystems ◽  
2013 ◽  
Vol 113 (1) ◽  
pp. 37-39 ◽  
Author(s):  
Christian Jungreuthmayer ◽  
David E. Ruckerbauer ◽  
Jürgen Zanghellini
Keyword(s):  
Elementary Flux Mode ◽  
Transcriptional Regulatory ◽  
Flux Mode

scholarly journals EFMlrs: a Python package for elementary flux mode enumeration via lexicographic reverse search

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Bianca A Buchner ◽  
Jürgen Zanghellini
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Metabolic Networks ◽  
Stoichiometric Matrix ◽  
Elementary Flux Mode ◽  
Source Program ◽  
Metabolic Models ◽  
Performance Computing ◽  
Python Package ◽  
Flux Mode

Abstract Background Elementary flux mode (EFM) analysis is a well-established, yet computationally challenging approach to characterize metabolic networks. Standard algorithms require huge amounts of memory and lack scalability which limits their application to single servers and consequently limits a comprehensive analysis to medium-scale networks. Recently, Avis et al. developed —a parallel version of the lexicographic reverse search (lrs) algorithm, which, in principle, enables an EFM analysis on high-performance computing environments (Avis and Jordan. mplrs: a scalable parallel vertex/facet enumeration code. arXiv:1511.06487, 2017). Here we test its applicability for EFM enumeration. Results We developed , a Python package that gives users access to the enumeration capabilities of . uses COBRApy to process metabolic models from sbml files, performs loss-free compressions of the stoichiometric matrix, and generates suitable inputs for as well as , providing support not only for our proposed new method for EFM enumeration but also for already established tools. By leveraging COBRApy, also allows the application of additional reaction boundaries and seamlessly integrates into existing workflows. Conclusion We show that due to ’s properties, the algorithm is perfectly suited for high-performance computing (HPC) and thus offers new possibilities for the unbiased analysis of substantially larger metabolic models via EFM analyses. is an open-source program that comes together with a designated workflow and can be easily installed via pip.

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