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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.
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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.
COBRA Toolbox / COnstraints Based Reconstruction and Analysis
A software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules.
DFBA / Dynamic Flux Balance Analysis
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.
ROOM / Regulatory On/Off Minimization
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.
integrated flux-balance analysis
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.
ll-ACHRB / Artificially Centered Hit-and-Run on a Box
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 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.
iReMet-flux / integration of Relative Metabolite Levels for Flux prediction
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.
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.
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.
GIMME / Gene Inactivity Moderated by Metabolism and Expression
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.
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,...)
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.
MultiGEM / Multi-tissue GEM framework
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.
RegrEx / Regularized context-specific model Extraction method
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.
F2C2 / Fast flux coupling calculator
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.
OptFlux visualization plugin
An open-source software that aims to fill the gap between metabolic engineering (ME) and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks.
Allows the analysis of both qualitative regulatory networks and genome-scale metabolic networks. FlexFlux is the first metabolic flux analysis tool that integrates regulatory networks in all of its functions. This tool provides a method that permits the analysis of regulatory and metabolic networks separately or together. It supports qualitative multi-state regulatory networks, and helps users to translate the discrete qualitative states of the regulatory networks into user-defined continuous intervals.
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