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

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.

Metabolic Efficiency of Sugar Co-Metabolism and Phenol Degradation in Alicyclobacillus acidocaldarius for Improved Lignocellulose Processing

Substrate availability plays a key role in dictating metabolic strategies. Most microorganisms consume carbon/energy sources in a sequential, preferential order. The presented study investigates metabolic strategies of Alicyclobacillus acidocaldarius, a thermoacidophilic bacterium that has been shown to co-utilize glucose and xylose, as well as degrade phenolic compounds. An existing metabolic model was expanded to include phenol degradation and was analyzed with both metabolic pathway and constraint-based analysis methods. Elementary flux mode analysis was used in conjunction with resource allocation theory to investigate ecologically optimal metabolic pathways for different carbon substrate combinations. Additionally, a dynamic version of flux balance analysis was used to generate time-resolved simulations of growth on phenol and xylose. Results showed that availability of xylose along with glucose did not predict enhanced growth efficiency beyond that of glucose alone, but did predict some differences in pathway utilization and byproduct profiles. In contrast, addition of phenol as a co-substrate with xylose predicted lower growth efficiency. Dynamic simulations predicted co-consumption of xylose and phenol in a similar pattern as previously reported experiments. Altogether, this work serves as a case study for combining both elementary flux mode and flux balance analyses to probe unique metabolic features, and also demonstrates the versatility of A. acidocaldarius for lignocellulosic biomass processing applications.

Towards scaling elementary flux mode computation

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.

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

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.