Thermodynamic software tools | Metabolic engineering data analysis
The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations.
An algorithmic pipeline for quantitative assignment of reaction directionality in multi-compartmental genome scale models based on an application of the second law of thermodynamics to each reaction. Given experimental or computationally estimated standard metabolite species Gibbs energy and metabolite concentrations, the algorithms bounds reaction Gibbs energy, which is transformed to in vivo pH, temperature, ionic strength and electrical potential. This cross-platform MATLAB extension to the COnstraint-Based Reconstruction and Analysis (COBRA) toolbox is computationally efficient, extensively documented and open source.
Allows users to rapidly integrate quantitative metabolome data obtained from virtually any organism. anNET provides the systems biology and metabolic engineering communities with a mean to proof the quality of metabolome data sets and with all further benefits of the network-embedded thermodynamic (NET) analysis approach.
A package to perform a variety of biomolecular calculations and simulations using molecular mechanic force fields. MOIL contains unique features such reaction path calculations, simulations of long time approximate trajectories, calculations of kinetics and thermodynamics along reaction coordinates, and Locally Enhanced Sampling. The obtained calculations help bridge the gap between structure, dynamics, and function.
Maps out measures of water structure and thermodynamics on solute surfaces. SSTMap is a computational tool that combines thermodynamic analysis with structural analysis, which can aid in understanding and evaluating the displacement of active-site water molecules. The software provides access to individual functions, such as identification of hydration site clusters, calculation of voxel occupancies, energy, entropy, and hydrogen bonding calculations.
Enables users to add thermodynamic information to constraint-based metabolic models. pyTFA implements the thermodynamics-based flux analysis (TFA) framework, which allows to reduce the feasible flux solution space and eliminates thermodynamically-infeasible flux distributions, thus increasing the predictive accuracy of these models. The resulting formulation is amenable to different types of analysis. The software comes with a tutorial that demonstrates the effects of integrating thermodynamic information as well as concentration data. It is available for both for MATLAB, and Python 3.
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