Offers a framework for prediction of cellular behavior. COBRA Toolbox gathers numerous algorithms intending to be applied to any biochemical system with prior mechanistic information, including those with incomplete one. The application proposes more than 30 functions divided into five sections: (i) analysis, that includes features for coupling or deletion; (ii) data integration; (iii) design; (iv) reconstruction and (v) base that includes solvers and utilities.

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Allows to perform structural and qualitative analysis of both mass-flow- and signal-flow-based cellular networks. CellNetAnalyzer is a toolbox for analyzing structure and function of biological networks on the basis of topological, stoichiometric, qualitative (logical) and semi-quantitative modeling approaches requiring no or only few parameters. Metabolic networks can be studied based on stoichiometric and constraint-based modeling approaches whereas signaling and regulatory networks can be explored by qualitative and semi-quantitative modeling approaches.

A generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions.

Allows computation of elementary flux modes (EFMs) of metabolic networks. Efmtool is enables large-scale computation of EFMs using residue arithmetic to exploit multi-core architectures of modern CPUs.

A toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. Unlike other existing software, MONGOOSE uses exact rational arithmetic, which makes its results certifiably accurate.

A flux-based analysis technique similar to FBA and based on the same stoichiometric constraints, but the optimal growth flux for mutants is relaxed. Instead, MoMA provides an approximate solution for a sub-optimal growth flux state, which is nearest in flux distribution to the unperturbed state.

A constraint-based algorithm for predicting the metabolic steady state after gene knockouts. ROOM aims to minimize the number of significant flux changes (hence on/off) with respect to the wild type. ROOM is shown to accurately predict steady-state metabolic fluxes that maintain flux linearity, in agreement with experimental flux measurements, and to correctly identify short alternative pathways used for rerouting metabolic flux in response to gene knockouts.

The simultaneous utilization of efficient respiration and inefficient fermentation even in the presence of abundant oxygen is a puzzling phenomenon commonly observed in bacteria, yeasts, and cancer cells. Unlike the empirically derived uptake constraints used in FBA, the proposed theory of cytoplasmic membrane economics is mechanistic in nature, and is extensively supported by experimental evidences.

An algorithm for the quantitative study of the dynamic reprogramming of metabolic networks. DFBA was used to simulate the batch growth of E. coli on glucose, and the predictions were found to qualitatively match experimental data. The dynamic FBA formalism was also used to study the sensitivity to the objective function. It was found that an instantaneous objective function resulted in better predictions than a terminal-type objective function. The constraints that govern the growth at different phases in the batch culture were also identified. Therefore, DFBA provides a framework for analyzing the transience of metabolism due to metabolic reprogramming and for obtaining insights for the design of metabolic networks.

Consists of a modeling framework that integrates dynamic flux balance analysis with diffusion on a lattice. COMETS implements a dynamic flux balance analysis (FBA) algorithm on a lattice, making it possible to track the spatio-temporal dynamics of multiple microbial species in complex environments with complete genome scale resolution. This dynamic flux balance analysis (dFBA) allows users to perform time-dependent metabolic simulations of microbial ecosystems, bridging the gap between stoichiometric and environmental modeling.

Characterizes and studies the metabolic behavior of an organism or a living cell. The approach aims to supply information on regulation of metabolic pathways. It takes advantage of regulatory information in transcriptome data. This method employs genome-wide measurements of transcription combined with genome-scale stoichiometric models. It permits users to obtain predictive correlations between genotype, environment, and phenotype.

Predicts different and more accurate phenotypes than the ordinary differential equation (ODE) model for 85 of 334 single gene perturbation simulations, as well for the wild-type simulations. iFBA was used to create an integrated model of Escherichia coli which combines a flux-balance-based, central carbon metabolic and transcriptional regulatory model with an ODE-based. This method encapsulates the dynamics of three internal metabolites and three transporters inadequately predicted by rFBA.

Enumerates extreme rays of polyhedral cones. Polco is a program that can be used for enumerating vertices of polytopes, and for the enumeration of facets given the extreme points.

Provides constraint-based simulations and network map visualization in a free, stand-alone software.

A stand-alone program package for the management of metabolic reaction information and quantitative metabolic flux analysis.

A VANTED Plug-in for the constraint-based analysis of metabolic models with special focus on the dynamic and visual exploration of metabolic flux data resulting from model analysis.

Provides functionalities of easy metabolic network drafting and editing, amenable network visualization for experimental data integration and flux balance analysis tools for simulation studies.

A Cytoscape plugin for viewing, manipulating and analyzing metabolic models created using the Model SEED.

A scalable algorithm for sampling the feasible solution space of metabolic networks. ll-ACHRB is inspired by the Hit-and-Run on a Box algorithm for uniform sampling from general regions, but employs the directions of choice approach of Artificially Centered Hit-and-Run. A novel strategy for generating feasible warmup points improved both sampling efficiency and mixing. ll-ACHRB shows overall better performance than current strategies to generate feasible flux samples across several models.

A web application dedicated to in silico experiments with genome-scale metabolic models coupled to the exploration of knowledge from BioCyc and KEGG.

A Matlab package extending the scope of established COBRA metabolic modelling.

A simple postprocessing of constraint-based solutions, which removes internal cycles from any given flux distribution v(0) without disturbing other fluxes not involved in the loops. CycleFreeFlux works by minimizing the sum of absolute fluxes ||v|| while (i) conserving the exchange fluxes and (ii) using the fluxes of the original solution to bound the new flux distribution. This strategy reduces internal fluxes until at least one reaction of every possible internal cycle is inactive, a necessary and sufficient condition for the thermodynamic feasibility of a flux distribution.

A tool for performing flux mode analysis in stoichiometric models. FluxModeCalculator enables large-scale elementary flux mode (EFM) computation on ordinary desktop computers. Our implementation uses the OpenMP API to optimally exploit processor architectures with multiple cores. It is more efficient than existing tools and enables EFM computation in models with up to an order of magnitude more EFMs.

Relies on relative metabolomics data alongside the assumptions that reaction kinetics follows the ubiquitous mass action law and that organisms tend to minimize change in flux between two scenarios. The key advantage of the approach is in maintaining the simplicity of constraint-based framework and its (largely) parameter-independent nature, while allowing for inclusion of data on the relative metabolite levels that are closer to the physiological state. We show that iReMet-Flux provides the means for directed biological interpretation of observed changes in metabolite levels which goes beyond classical analysis of differential behavior, and thus opens a new avenue for systems biology approaches centered on metabolomics technologies.

An efficient algorithm to identify synthetic lethal gene/reaction sets in genome-scale metabolic models. Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets.

Solves multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes. DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis. The multi-nodal performance of this method is unparalleled: the scalability matches theoretical predictions, and resources are optimally used. This application is part of a COBRA.jl package and makes use of the high-level interface MathProgBase.jl.

An easy-to-use PyMOL plug-in that allows setup of constraint network analysis (CNA) runs and analysis of CNA results linking plots with molecular graphics representations. From a practical viewpoint, the most striking feature of VisualCNA is that it facilitates interactive protein engineering aimed at improving thermostability.

Reduces the size of the original loopless problem into an easier and equivalent mixed integer linear programming (MILP) problem. Fast-SNP considerably improves the computational efficiency in several metabolic models running different loopless optimization problems. Furthermore, analysis of the topology encoded in the reduced loop matrix enabled identification of key directional constraints for the potential permanent elimination of infeasible loops in the underlying model. Overall, Fast-SNP is an effective and simple algorithm for efficient formulation of loop-law constraints, making loopless flux optimization feasible and numerically tractable at large scale.

Converges to a uniform stationary sampling distribution. CHRR is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. It enables reliable and tractable sampling of genome-scale biochemical networks. The tool was applied to metabolic networks of increasing dimensionality.

Determines intracellular fluxes for transcriptomics/proteomics data. LBFMA is based on a constraint-based modeling (CBM) that utilizes expression data to work. It can diminish the flux variability across alternate solutions. This method parametrizes the flux bounds using 4 or 5 sets of experimental measurements of transcriptomics/proteomics and fluxomics. It enables analysis of eukaryotes and prokaryotes, and of both wildtype and knockout strains.

An algorithm that uses a linear programming-based tree search and efficiently enumerates a subset of elementary flux modes (EFMs) in genome-scale metabolic networks (GSMNs). The stand-alone software TreeEFM is implemented in C++ and interacts with the open-source linear solver COIN-OR Linear program Solver (CLP).

Permits localized loop-less flux variability analysis for genome-scale metabolic models.

Uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells.

Constructs genome scale models in a tissue-specific manner. CODRA based on a method that identifies the dependency of desirable reactions on undesirable reactions. It returns a concise, functional tissue-specific reconstruction, and features a flexible reactions core. The tool is also able to return reaction associations that assist in any manual curation to be performed following the automated reconstruction process.

Allows to analyze microbial metabolism where individual-based modeling has been employed to examine complex dynamics about interacting organisms. BacArena extends the integration of flux balance analysis (FBA) and individual based modeling proposed by MatNet to model multispecies communities. It enables the analysis of interaction dynamics on the level of individuals and can contribute to current efforts to move from correlative to functional explanations.

Finds a globally optimal network, by identifying the minimal set of network changes needed to correctly predict all experimentally observed growth and non-growth cases simultaneously. GlobalFit is an R package that refines metabolic network models by making networks changes (i.e., removing, adding, changing reversibility of reactions; adding and removing biomass metabolites) and simultaneously matching sets of experimental growth and non-growth data (e.g., KO-mutants, mutants grown under different media conditions,...)

Determines metabolic capacity based on expression data. E-flux integrates gene expression data into the metabolic flux constraints. It employs expression data to model the maximum possible flux through metabolic reactions. This tool is useful for the investigation of metabolic state from gene expression state. It can be generalized to take modulation of enzyme activity into account.

A mathematical modeling tool that integrates multiple constraint-based metabolic models into a single dynamic community metabolic model. The DyMMM framework was formerly known as the DMMM framework.

It is numerical integration scheme for large-scale systems of differential equations encountered in dynamic flux balance analysis (dFBA). DFBSIM provides efficient simulation of multi-culture of microbial species based on genome-scale metabolic network reconstructions for analysis, control and optimization of biochemical processes. As such, it generates dynamic predictions of substrate, biomass, and product concentrations for growth in batch or fed-batch cultures. dFBA provides a structured model of a biochemical process, where the reaction pathways within the microorganism change depending on the environmental conditions, which is effectively represented by changes in the functional dependency on the substrate concentrations.

A multi-tissue genome-scale metabolic modelling framework for the analysis of whole plant systems. Six tissue compartments were created, each with their own distinct set of metabolic capabilities, and hence a reliance on other compartments for support. We used the multi-tissue framework to explore differences in the "division-of-labor" between the sources and sink tissues in response to: (a) the energy demand for the translocation of C and N species in between tissues; and (b) the use of two distinct nitrogen sources (NO(-) 3 or NH(+) 4). MultiGEM also enabled assessing the impact of energetic constraints in resource (redox and ATP) allocation between leaf, stem, and root tissues required for efficient carbon and nitrogen assimilation, including the diurnal cycle constraint forcing the plant to set aside resources during the day and defer metabolic processes that are more efficiently performed at night.

Allows bioinformaticians to build and explore flux-balance analysis (FBA) models using the Python programming language. PyFBA is a python module that also permits to build models from genomes, gapfill models, and run flux-balance-analysis on that model. This method can be easily extended and modified to provide new metabolic modelling capabilities.

Constructs and analyzes kinetic and constraint-based models of biochemical reactions systems. MASS Toolbox is based on an omics and data driven approach. It can reconstruct networks that can be observed in an in vivo setting, and fluxomic, metabolomic, and proteomic data. This tool builds a mass action kinetic representation of the stoichiometric network to formulate a dynamic model of network functions.

Extracts a context-specific model and provides a flux distribution that maximizes correlation between data. RegrEx is based on regularized least squares optimization to automatically run without bias. It is able to generate context specific flux distributions with the high correlation values, enriched in reactions with high associated data values. The tool can provide a larger correlation between predicted fluxes and experimental data, as well as models that capture the general pattern of differences and similarities in reaction activity across contexts expressed by data.

An efficient Matlab implementation of flux variability analysis (FVA) optimized for the GLPK and CPLEX solvers. Compared to a direct implementation of FVA in Matlab, fastFVA results in speedup ranging from 30 to 220 times faster for GLPK and from 20 to 120 times faster for CPLEX.

A Python package that provides support for basic COnstraint-Based Reconstruction and Analysis (COBRA) methods.

A command line oriented software for the computation of flux distributions using a variety of the most common FBA algorithms.

Computes the flux coupling for every pair of reactions in a network. F2C2 is a software which outperforms previous analysis methods by orders of magnitude. It is based on two main principles: (i) it reduces the stoichiometric model as much as possible when parsing the stoichiometric matrix, and (ii) it uses inference rules to minimize the number of linear programming problems that have to be solved. With this method, flux coupling analysis of genome-scale metabolic networks can now be performed in a routine manner. This algorithm also significantly speeds up the calculation of flux coupling.

A MATLAB-based code that performs numerical integration of dynamic flux balance analysis (dFBA) systems. DFBAlab provides efficient simulation of multi-culture of microbial species based on genome-scale metabolic network reconstructions for analysis, control and optimization of biochemical processes. As such, it generates dynamic predictions of substrate, biomass, and product concentrations for growth in batch or fed-batch cultures.

A Mathematica toolbox for stoichiometric network analysis.

Web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program.

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