Computing irreversible minimal cut sets in genome-scale metabolic networks via flux cone projection

2018 ◽  
Vol 35 (15) ◽  
pp. 2618-2625 ◽  
Author(s):  
Annika Röhl ◽  
Tanguy Riou ◽  
Alexander Bockmayr
Keyword(s):  
Metabolic Networks ◽  
Supplementary Information ◽  
Oriented Matroid ◽  
Matroid Theory ◽  
Supplementary Data ◽  
Large Genome ◽  
Minimal Cut Sets ◽  
Minimal Cut ◽  
Genome Scale ◽  
Target Reaction

Abstract Motivation Minimal cut sets (MCSs) for metabolic networks are sets of reactions which, if they are removed from the network, prevent a target reaction from carrying flux. To compute MCSs different methods exist, which may fail to find sufficiently many MCSs for larger genome-scale networks. Results Here we introduce irreversible minimal cut sets (iMCSs). These are MCSs that consist of irreversible reactions only. The advantage of iMCSs is that they can be computed by projecting the flux cone of the metabolic network on the set of irreversible reactions, which usually leads to a smaller cone. Using oriented matroid theory, we show how the projected cone can be computed efficiently and how this can be applied to find iMCSs even in large genome-scale networks. Availability and implementation Software is freely available at https://sourceforge.net/projects/irreversibleminimalcutsets/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MCS2: minimal coordinated supports for fast enumeration of minimal cut sets in metabolic networks

2019 ◽  
Vol 35 (14) ◽  
pp. i615-i623 ◽  
Author(s):  
Reza Miraskarshahi ◽  
Hooman Zabeti ◽  
Tamon Stephen ◽  
Leonid Chindelevitch
Keyword(s):  
Metabolic Networks ◽  
Supplementary Information ◽  
Intermediate Step ◽  
Original Network ◽  
Practical Advantage ◽  
Minimal Cut Sets ◽  
Minimal Cut ◽  
Constraint Based Modeling ◽  
Target Reaction ◽  
Insight Into

Abstract Motivation Constraint-based modeling of metabolic networks helps researchers gain insight into the metabolic processes of many organisms, both prokaryotic and eukaryotic. Minimal cut sets (MCSs) are minimal sets of reactions whose inhibition blocks a target reaction in a metabolic network. Most approaches for finding the MCSs in constrained-based models require, either as an intermediate step or as a byproduct of the calculation, the computation of the set of elementary flux modes (EFMs), a convex basis for the valid flux vectors in the network. Recently, Ballerstein et al. proposed a method for computing the MCSs of a network without first computing its EFMs, by creating a dual network whose EFMs are a superset of the MCSs of the original network. However, their dual network is always larger than the original network and depends on the target reaction. Here we propose the construction of a different dual network, which is typically smaller than the original network and is independent of the target reaction, for the same purpose. We prove the correctness of our approach, minimal coordinated support (MCS2), and describe how it can be modified to compute the few smallest MCSs for a given target reaction. Results We compare MCS2 to the method of Ballerstein et al. and two other existing methods. We show that MCS2 succeeds in calculating the full set of MCSs in many models where other approaches cannot finish within a reasonable amount of time. Thus, in addition to its theoretical novelty, our approach provides a practical advantage over existing methods. Availability and implementation MCS2 is freely available at https://github.com/RezaMash/MCS under the GNU 3.0 license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach

2020 ◽  
Vol 36 (14) ◽  
pp. 4163-4170
Author(s):  
Francisco Guil ◽  
José F Hidalgo ◽  
José M García
Keyword(s):  
Linear Programming ◽  
Metabolic Networks ◽  
Supplementary Information ◽  
Programming Approach ◽  
Supplementary Data ◽  
Main Interest ◽  
Elementary Flux Modes ◽  
Order Of Magnitude ◽  
Genome Scale ◽  
Efficiency Rate

Abstract Motivation Elementary flux modes (EFMs) are a key tool for analyzing genome-scale metabolic networks, and several methods have been proposed to compute them. Among them, those based on solving linear programming (LP) problems are known to be very efficient if the main interest lies in computing large enough sets of EFMs. Results Here, we propose a new method called EFM-Ta that boosts the efficiency rate by analyzing the information provided by the LP solver. We base our method on a further study of the final tableau of the simplex method. By performing additional elementary steps and avoiding trivial solutions consisting of two cycles, we obtain many more EFMs for each LP problem posed, improving the efficiency rate of previously proposed methods by more than one order of magnitude. Availability and implementation Software is freely available at https://github.com/biogacop/Boost_LP_EFM. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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 MinReact: a systematic approach for identifying minimal metabolic networks

2020 ◽  
Vol 36 (15) ◽  
pp. 4309-4315
Author(s):  
Gayathri Sambamoorthy ◽  
Karthik Raman
Keyword(s):  
Network Structure ◽  
Metabolic Networks ◽  
Systematic Approach ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Original Network ◽  
Compensatory Reactions ◽  
Genome Scale ◽  
Minimal Networks

Abstract Motivation Genome-scale metabolic models are widely constructed and studied for understanding various design principles underlying metabolism, predominantly redundancy. Metabolic networks are highly redundant and it is possible to minimize the metabolic networks into smaller networks that retain the functionality of the original network. Results Here, we establish a new method, MinReact that systematically removes reactions from a given network to identify minimal reactome(s). We show that our method identifies smaller minimal reactomes than existing methods and also scales well to larger metabolic networks. Notably, our method exploits known aspects of network structure and redundancy to identify multiple minimal metabolic networks. We illustrate the utility of MinReact by identifying multiple minimal networks for 77 organisms from the BiGG database. We show that these multiple minimal reactomes arise due to the presence of compensatory reactions/pathways. We further employed MinReact for a case study to identify the minimal reactomes of different organisms in both glucose and xylose minimal environments. Identification of minimal reactomes of these different organisms elucidate that they exhibit varying levels of redundancy. A comparison of the minimal reactomes on glucose and xylose illustrates that the differences in the reactions required to sustain growth on either medium. Overall, our algorithm provides a rapid and reliable way to identify minimal subsets of reactions that are essential for survival, in a systematic manner. Availability and implementation Algorithm is available from https://github.com/RamanLab/MinReact. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MCS2: Minimal coordinated supports for fast enumeration of minimal cut sets in metabolic networks

2018 ◽  
Author(s):  
Reza Miraskarshahi ◽  
Hooman Zabeti ◽  
Tamon Stephen ◽  
Leonid Chindelevitch
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Intermediate Step ◽  
Original Network ◽  
Practical Advantage ◽  
Minimal Cut Sets ◽  
Minimal Cut ◽  
Constraint Based Modeling ◽  
Target Reaction ◽  
Insight Into

AbstractMotivationConstraint-based modeling of metabolic networks helps researchers gain insight into the metabolic processes of many organisms, both prokaryotic and eukaryotic. Minimal Cut Sets (MCSs) are minimal sets of reactions whose inhibition blocks a target reaction in a metabolic network. Most approaches for finding the MCSs in constrained-based models require, either as an intermediate step or as a byproduct of the calculation, the computation of the set of elementary flux modes (EFMs), a convex basis for the valid flux vectors in the network. Recently, Ballerstein et al. [BvKKH11] proposed a method for computing the MCSs of a network without first computing its EFMs, by creating a dual network whose EFMs are a superset of the MCSs of the original network. However, their dual network is always larger than the original network and depends on the target reaction.Here we propose the construction of a different dual network, which is typically smaller than the original network and is independent of the target reaction, for the same purpose. We prove the correctness of our approach, MCS2, and describe how it can be modified to compute the few smallest MCSs for a given target reaction.ResultsWe compare MCS2 to the method of Ballerstein et al. and two other existing methods. We show that MCS2 succeeds in calculating the full set of MCSs in many models where other approaches cannot finish within a reasonable amount of time. Thus, in addition to its theoretical novelty, our approach provides a practical advantage over existing methods.AvailabilityMCS2 is freely available at https://github.com/RezaMash/MCS under the GNU 3.0 license.

Spliceogen: an integrative, scalable tool for the discovery of splice-altering variants

2019 ◽  
Vol 35 (21) ◽  
pp. 4405-4407 ◽  
Author(s):  
Steven Monger ◽  
Michael Troup ◽  
Eddie Ip ◽  
Sally L Dunwoodie ◽  
Eleni Giannoulatou
Keyword(s):  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
In Silico Prediction ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Prediction Tools ◽  
Motif Prediction ◽  
Command Line Tool ◽  
Genome Scale

Abstract Motivation In silico prediction tools are essential for identifying variants which create or disrupt cis-splicing motifs. However, there are limited options for genome-scale discovery of splice-altering variants. Results We have developed Spliceogen, a highly scalable pipeline integrating predictions from some of the individually best performing models for splice motif prediction: MaxEntScan, GeneSplicer, ESRseq and Branchpointer. Availability and implementation Spliceogen is available as a command line tool which accepts VCF/BED inputs and handles both single nucleotide variants (SNVs) and indels (https://github.com/VCCRI/Spliceogen). SNV databases with prediction scores are also available, covering all possible SNVs at all genomic positions within all Gencode-annotated multi-exon transcripts. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome-scale strain designs based on regulatory minimal cut sets

2015 ◽  
Vol 31 (17) ◽  
pp. 2844-2851 ◽  
Author(s):  
Radhakrishnan Mahadevan ◽  
Axel von Kamp ◽  
Steffen Klamt
Keyword(s):  
Minimal Cut Sets ◽  
Minimal Cut ◽  
Genome Scale

scholarly journals Metabolic network-guided binning of metagenomic sequence fragments

2015 ◽  
Vol 32 (6) ◽  
pp. 867-874 ◽  
Author(s):  
Matthew B. Biggs ◽  
Jason A. Papin
Keyword(s):  
Metabolic Network ◽  
Dna Sequences ◽  
Metabolic Networks ◽  
Human Microbiome ◽  
Environmental Dna ◽  
Human Microbiome Project ◽  
Supplementary Information ◽  
Metagenomic Sequence ◽  
Connectivity Score ◽  
Genome Scale

Abstract Motivation: Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health. Results: We present the SOrting by NEtwork Completion (SONEC) approach for assigning reactions to incomplete metabolic networks based on a metabolite connectivity score. We successfully demonstrate proof of concept in a set of 100 genome-scale metabolic network reconstructions, and delineate the variables that impact reaction assignment accuracy. We further demonstrate the integration of SONEC with existing approaches (such as cross-sample scaffold abundance profile clustering) on a set of 94 metagenomic samples from the Human Microbiome Project. We show that not only does SONEC aid in reconstructing species-level genomes, but it also improves functional predictions made with the resulting metabolic networks. Availability and implementation: The datasets and code presented in this work are available at: https://bitbucket.org/mattbiggs/sorting_by_network_completion/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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