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 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.

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 Minimal Cut Sets and the Use of Failure Modes in Metabolic Networks

Metabolites ◽  
2012 ◽  
Vol 2 (3) ◽  
pp. 567-595 ◽  
Author(s):  
Sangaalofa T. Clark ◽  
Wynand S. Verwoerd
Keyword(s):  
Metabolic Networks ◽  
Failure Modes ◽  
Minimal Cut Sets ◽  
Minimal Cut

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 SAMMI: a semi-automated tool for the visualization of metabolic networks

2019 ◽  
Vol 36 (8) ◽  
pp. 2616-2617
Author(s):  
Andre Schultz ◽  
Rehan Akbani
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Source Code ◽  
Supplementary Information ◽  
Network Partitioning ◽  
Web Based ◽  
Constraint Based Modeling ◽  
Context Specific ◽  
Genome Scale ◽  
Automated Tool

Abstract Summary Here we present a browser-based Semi-Automated Metabolic Map Illustrator (SAMMI) for the visualization of metabolic networks. While automated features allow for easy network partitioning, navigation, and node positioning, SAMMI also offers a wide array of manual map editing features. This combination allows for fast, context-specific visualization of metabolic networks as well as the development of standardized, large-scale, visually appealing maps. The implementation of SAMMI with popular constraint-based modeling toolboxes also allows for effortless visualization of simulation results of genome-scale metabolic models. Availability and implementation SAMMI has been implemented as a standalone web-based tool and as plug-ins for the COBRA and COBRApy toolboxes. SAMMI and its COBRA plugins are available under the GPL 3.0 license and are available along with documentation, tutorials, and source code at www.SammiTool.com. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Surrogate minimal depth as an importance measure for variables in random forests

2019 ◽  
Vol 35 (19) ◽  
pp. 3663-3671 ◽  
Author(s):  
Stephan Seifert ◽  
Sven Gundlach ◽  
Silke Szymczak
Keyword(s):  
Variable Selection ◽  
Supplementary Information ◽  
Predictor Variables ◽  
Importance Measure ◽  
Surrogate Variables ◽  
Machine Learning Approach ◽  
Complex Relationships ◽  
Minimal Depth ◽  
Insight Into ◽  
Causal Variables

Abstract Motivation It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult. Results Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improves variable selection. When compared with existing state-of-the-art methods and MD, SMD has higher empirical power to identify causal variables while the resulting variable lists are equally stable. In conclusion, SMD is a promising approach to get more insight into the complex interplay of predictor variables and outcome in a high-dimensional data setting. Availability and implementation https://github.com/StephanSeifert/SurrogateMinimalDepth. Supplementary information Supplementary data are available at Bioinformatics online.

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