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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.
EMU Generator
Significantly decreases, with flux coupling, the computational burden of metabolic flux analysis (MFA) when applied to large-scale metabolic models. EMU Generator was applied to a previously published isotope mapping model of Escherichia coli accounting for 238 reactions. It was find that the combined use of EMU and flux coupling analysis leads to a ten-fold decrease in the number of variables in comparison to the original isotope distribution vector (IDV) version of the model. The observed computational savings reveal the rapid progress in performing MFA with increasingly larger isotope models with the ultimate goal of handling genome-scale models of metabolism.
INCA / Isotopomer Network Compartmental Analysis
A software package that can perform both steady-state metabolic flux analysis and isotopically non-stationary metabolic flux analysis. The software provides a framework for comprehensive analysis of metabolic networks using mass balances and elementary metabolite unit balances. The generation of balance equations and their computational solution is completely automated and can be performed on networks of arbitrary complexity. INCA is a part of the MFA suite.
A software suite of applications for the detailed quantification of intracellular (quasi) steady-state fluxes. 13CFLUX contains all tools for composing flexible computational (13)C-MFA workflows to design and evaluate carbon labeling experiments. A specially developed XML language, FluxML, highly efficient data structures and simulation algorithms achieve a maximum of performance and effectiveness. Support of multicore CPUs, as well as compute clusters, enables scalable investigations. 13CFLUX outperforms existing tools in terms of universality, flexibility and built-in features. Therewith, 13CFLUX paves the way for next-generation high-resolution (13)C-MFA applications on the large scale.
A software tool for the visualization of any data in biochemical networks. The unique feature of Omix is: the software is programmable by a scripting language called Omix Visualization Language (OVL). In Omix, the visualization of data coming from experiment or simulation is completely performed by the software user realized in concise OVL scripts. By this, visualization becomes most flexible and adaptable to the requirements of the user and can be adapted to new application fields.
Predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints.
Experimental design is a key step in improving both the number of fluxes that can be calculated from a set of isotopic data and the precision of flux values. IsoDesign that enables these parameters to be maximized by optimizing the isotopic composition of the label input. It can be applied to (13) C-MFA investigations using a broad panel of analytical tools (MS, MS/MS, (1) H NMR, (13) C NMR, etc.) individually or in combination. IsoDesign includes a visualization module to intuitively select the optimal label input depending on the biological question to be addressed.
Automates, standardizes and connects the data flow between mass spectrometric measurements and flux analysis programs. iMS2Flux streamlines the transfer of data from extraction via correction tools to 13C-Flux software by processing MS data from stable isotope labelling experiments. It allows the correction of large and heterogeneous MS datasets for the presence of naturally occurring stable isotopes, initial biomass and several mass spectrometry effects. iMS2Flux removes the limitations on the number of samples that can be processed per tracer experiment, including the number of treatments or genotypes studied, the replication of each experiment, the number of substrate combinations used, and/or the number of time points analyzed.
Facilitates and standardizes the 13C-metabolic flux analysis (13C-MFA) modeling work. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tertbutyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. The software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria in order to simplify 13C-MFA of different prokaryotic species. WUFlux consists of two parts: a software package for 13C-MFA calculation and a carbon transition map and MS correction tool.
jQMM / JBEI Quantitative Metabolic Modeling
Provides a method to calculate fluxes for genome-scale models. jQMM is an open-source modular library for modelling metabolism. This library includes algorithms to measure and predict internal metabolic fluxes using three different techniques: 13C Metabolic Flux Analysis, Flux Balance Analysis and two-scale 13C Metabolic Flux Analysis. It also includes methods to produce actionable insights from -omics data to improve pathway yield, and methodologies for the flux analysis of microbial communities.
FIA / Fluxomer Iterative Algorithm
A powerful algorithm developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability. Fluxomer formulation provides a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.
Provides a software framework for rapid network assembly (flexible pathway browser with local or remote operation mode), network overview (visualization routine and YANAsquare editor) and network performance analysis (calculation of flux modes as well as target and robustness tests). YANAsquare comes as an easy-to-setup program package in Java. It is fully compatible and integrates the programs YANA (translation of gene expression values into flux distributions, metabolite network dissection) and Metatool (elementary mode calculation).
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