CoBAMP: a Python framework for metabolic pathway analysis in constraint-based models

2019 ◽  
Vol 35 (24) ◽  
pp. 5361-5362 ◽  
Author(s):  
Vítor Vieira ◽  
Miguel Rocha
Keyword(s):  
Operating Systems ◽  
Pathway Analysis ◽  
Mixed Integer Linear Programming ◽  
Source Code ◽  
Mixed Integer ◽  
Metabolic Pathway Analysis ◽  
Minimal Cut Sets ◽  
Genome Scale ◽  
Modular Framework ◽  
Constraint Based Models

Abstract Summary CoBAMP is a modular framework for the enumeration of pathway analysis concepts, such as elementary flux modes (EFM) and minimal cut sets in genome-scale constraint-based models (CBMs) of metabolism. It currently includes the K-shortest EFM algorithm and facilitates integration with other frameworks involving reading, manipulation and analysis of CBMs. Availability and implementation The software is implemented in Python 3, supported on most operating systems and requires a mixed-integer linear programming optimizer supported by the optlang framework. Source-code is available at https://github.com/BioSystemsUM/cobamp.

Toward Genome-Scale Metabolic Pathway Analysis

2016 ◽  
pp. 111-123 ◽  
Author(s):  
Jürgen Zanghellini ◽  
Matthias P. Gerstl ◽  
Michael Hanscho ◽  
Govind Nair ◽  
Georg Regensburger ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Metabolic Pathway ◽  
Metabolic Pathway Analysis ◽  
Genome Scale

scholarly journals Metabolic Pathway Analysis of Nitrogen and Phosphorus Uptake by the Consortium between C. vulgaris and P. aeruginosa

2019 ◽  
Vol 20 (8) ◽  
pp. 1978 ◽  
Author(s):  
A. Suggey Guerra-Renteria ◽  
M. Alberto García-Ramírez ◽  
César Gómez-Hermosillo ◽  
Abril Gómez-Guzmán ◽  
Yolanda González-García ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Metabolic Pathway ◽  
Phosphorus Uptake ◽  
Reaction Network ◽  
Biochemical Reaction ◽  
Urban Wastewater ◽  
Metabolic Pathway Analysis ◽  
Biochemical Reaction Network ◽  
A Genome ◽  
Genome Scale

Anthropogenic activities have increased the amount of urban wastewater discharged into natural aquatic reservoirs containing a high amount of nutrients such as phosphorus (Pi and PO 4 − 3 ), nitrogen (NH 3 and NO 3 − ) and organic contaminants. Most of the urban wastewater in Mexico do not receive any treatment to remove nutrients. Several studies have reported that an alternative to reduce those contaminants is using consortiums of microalgae and endogenous bacteria. In this research, a genome-scale biochemical reaction network is reconstructed for the co-culture between the microalga Chlorella vulgaris and the bacterium Pseudomonas aeruginosa. Metabolic Pathway Analysis (MPA), is applied to understand the metabolic capabilities of the co-culture and to elucidate the best conditions in removing nutrients. Theoretical yields for phosphorus removal under photoheterotrophic conditions are calculated, determining their values as 0.042 mmol of PO 4 − 3 per g DW of C. vulgaris, 19.43 mmol of phosphorus (Pi) per g DW of C. vulgaris and 4.90 mmol of phosphorus (Pi) per g DW of P. aeruginosa. Similarly, according to the genome-scale biochemical reaction network the theoretical yields for nitrogen removal are 10.3 mmol of NH 3 per g DW of P. aeruginosa and 7.19 mmol of NO 3 − per g DW of C. vulgaris. Thus, this research proves the metabolic capacity of these microorganisms in removing nutrients and their theoretical yields are calculated.

scholarly journals Speeding up the core algorithm for the dual calculation of minimal cut sets in large metabolic networks

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Steffen Klamt ◽  
Radhakrishnan Mahadevan ◽  
Axel von Kamp
Keyword(s):  
Metabolic Networks ◽  
Dual System ◽  
Mixed Integer ◽  
Mathematical Framework ◽  
Stoichiometric Matrix ◽  
Farkas Lemma ◽  
Minimal Cut Sets ◽  
Minimal Cut ◽  
General Importance ◽  
Genome Scale

Abstract Background The concept of minimal cut sets (MCS) has become an important mathematical framework for analyzing and (re)designing metabolic networks. However, the calculation of MCS in genome-scale metabolic models is a complex computational problem. The development of duality-based algorithms in the last years allowed the enumeration of thousands of MCS in genome-scale networks by solving mixed-integer linear problems (MILP). A recent advancement in this field was the introduction of the MCS2 approach. In contrast to the Farkas-lemma-based dual system used in earlier studies, the MCS2 approach employs a more condensed representation of the dual system based on the nullspace of the stoichiometric matrix, which, due to its reduced dimension, holds promise to further enhance MCS computations. Results In this work, we introduce several new variants and modifications of duality-based MCS algorithms and benchmark their effects on the overall performance. As one major result, we generalize the original MCS2 approach (which was limited to blocking the operation of certain target reactions) to the most general case of MCS computations with arbitrary target and desired regions. Building upon these developments, we introduce a new MILP variant which allows maximal flexibility in the formulation of MCS problems and fully leverages the reduced size of the nullspace-based dual system. With a comprehensive set of benchmarks, we show that the MILP with the nullspace-based dual system outperforms the MILP with the Farkas-lemma-based dual system speeding up MCS computation with an averaged factor of approximately 2.5. We furthermore present several simplifications in the formulation of constraints, mainly related to binary variables, which further enhance the performance of MCS-related MILP. However, the benchmarks also reveal that some highly condensed formulations of constraints, especially on reversible reactions, may lead to worse behavior when compared to variants with a larger number of (more explicit) constraints and involved variables. Conclusions Our results further enhance the algorithmic toolbox for MCS calculations and are of general importance for theoretical developments as well as for practical applications of the MCS framework.

scholarly journals Gapsplit: efficient random sampling for non-convex constraint-based models

2020 ◽  
Vol 36 (8) ◽  
pp. 2623-2625 ◽  
Author(s):  
Thomas C Keaty ◽  
Paul A Jensen
Keyword(s):  
Random Sampling ◽  
Linear Models ◽  
Source Code ◽  
Solution Space ◽  
Supplementary Information ◽  
Mixed Integer ◽  
Supplementary Data ◽  
Convex Constraint ◽  
Random Samples ◽  
Constraint Based Models

Abstract Summary Gapsplit generates random samples from convex and non-convex constraint-based models by targeting under-sampled regions of the solution space. Gapsplit provides uniform coverage of linear, mixed-integer and general non-linear models. Availability and implementation Python and Matlab source code are freely available at http://jensenlab.net/tools. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Metabolic Pathway Analysis for Nutrient Removal of the Consortium between C. vulgaris and P. aeruginosa

2019 ◽  
Author(s):  
A. Suggey Guerra-Rentería ◽  
Mario García-Ramírez ◽  
César Gómez-Hermosillo ◽  
Abril Goméz-Guzmán ◽  
Yolanda González-García ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Metabolic Pathway ◽  
Anthropogenic Activities ◽  
Reaction Network ◽  
Inorganic Phosphorus ◽  
Biochemical Reaction ◽  
Metabolic Pathway Analysis ◽  
Biochemical Reaction Network ◽  
A Genome ◽  
Genome Scale

Anthropogenic activities have increased the amount of urban wastewater discharged into natural aquatic reservoirs confining in them a high amount of nutrients and organics contaminants. Several studies have reported that an alternative to reduce those contaminants is using consortiums of microalgae and endogenous bacteria. In this research, a genome-scale biochemical reaction network is reconstructed for the co-culture between the microalga Chlorella vulgaris and the bacterium Pesudomonas aeruginosa. Metabolic Pathway Analysis (MPA), is applied to understand the metabolic capabilities of the co-culture and to elucidate the best conditions in removing nutrients such as Phosphorus (inorganic phosphorous and phosphate) and Nitrogen (nitrates and ammonia). Theoretical yields for Phosphorus removal under photoheterotrophic conditions are calculated, determining their values as 0.042 mmol of PO4/ g DW of C. vulgaris, 19.53 mmol of inorganic Phosphorus /g DW of C. vulgaris and 4.90 mmol of inorganic Phosphorus/ g DW of P. aeruginosa. Similarly, according to the genome-scale biochemical reaction network the theoretical yields for Nitrogen removal are 10.3 mmol of NH3/g DW of P. aeruginosa and 7.19 mmol of NO3 /g DW of C. vulgaris. Thus, this research proves the metabolic capacity of these microorganisms in removing nutrients and their theoretical yields are calculated.

scholarly journals Scalable metabolic pathway analysis

2020 ◽  
Author(s):  
Ove Øyås ◽  
Jörg Stelling
Keyword(s):  
Pathway Analysis ◽  
Metabolic Pathway ◽  
Large Scale ◽  
Metabolic Networks ◽  
Central Carbon Metabolism ◽  
Graph Representation ◽  
Metabolic Pathway Analysis ◽  
A Genome ◽  
Scope Of Application ◽  
Genome Scale

The scope of application of genome-scale constraint-based models (CBMs) of metabolic networks rapidly expands toward multicellular systems. However, comprehensive analysis of CBMs through metabolic pathway analysis remains a major computational challenge because pathway numbers grow combinatorially with model sizes. Here, we define the minimal pathways (MPs) of a metabolic (sub)network as a subset of its elementary flux vectors. We enumerate or sample them efficiently using iterative minimization and a simple graph representation of MPs. These methods outperform the state of the art and they allow scalable pathway analysis for microbial and mammalian CBMs. Sampling random MPs from Escherichia coli’s central carbon metabolism in the context of a genome-scale CBM improves predictions of gene importance, and enumerating all minimal exchanges in a host-microbe model of the human gut predicts exchanges of metabolites associated with host-microbiota homeostasis and human health. MPs thereby open up new possibilities for the detailed analysis of large-scale metabolic networks.

scholarly journals Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-integer Linear Programming for Metabolite Production in Constraint-based Models: gDel_minRN

2021 ◽  
Author(s):  
Takeyuki Tamura ◽  
Ai Muto-Fujita ◽  
Yukako Tohsato ◽  
Tomoyuki Kosaka
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Gene Deletion ◽  
Reaction Network ◽  
Mixed Integer ◽  
Metabolite Production ◽  
Source Codes ◽  
Genome Scale ◽  
Protein Reaction

Abstract Background: Genome-scale constraint-based metabolic networks play an important role in the simulation of growth coupling, which means that cell growth and target metabolite production are simultaneously achieved. To achieve growth coupling, a minimal reaction-network-based design is known to be effective. However, the obtained reaction networks often fail to be realized by gene deletions due to conflicts with gene-protein-reaction relations.Results: Here, we developed gDel_minRN that determines gene deletion strategies using mixed-integer linear programming to achieve growth coupling by repressing the maximum number of reactions via gene-protein-reaction relations. Computational experiments were conducted in which gDel_minRN was applied to iML1515, a genome-scale model of Escherichia coli. The target metabolites were three vitamins that are highly valuable and require cost-effective bioprocesses for economics and the environment. gDel_minRN successfully calculated gene deletion strategies that achieve growth coupling for the production of biotin (vitamin B7), riboflavin (vitamin B2), and pantothenate (vitaminB5).Conclusion: Since gDel_minRN calculates a constraint-based model of the minimum number of gene-associated reactions without conflict with gene-protein-reaction relations, it helps biological analysis of the core parts essential for growth coupling for each target metabolite. The source codes are implemented in MATLAB, CPLEX, and COBRA Toolbox. The obtained data and source codes are available on https://github.com/taketam/gDel-minRN

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