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The proposed approaches provide efficient model reduction tools in this direction.
Firstly, we suggest a set of model reduction tools that can be systematically applied to a given model.
Using model reduction tools, the number of degrees of freedom are reduced, while still remaining within the same model formalism as the original model.
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The model reduction tool requires three input files.
The use of Add metabolites tool requires two input files: (i) the SBML model, for this case we use the reduced SBML file (output from the model reduction tool) and (ii) the text file with the initial known metabolite concentration and the corresponding name.
5. Similar to add metabolites tool, the set fluxes requires two input files: (i) the reduced kinetic model obtained from step 3 and (ii) a text file with known flux distribution values and the corresponding names (output file from the model reduction tool).
Model reduction is an indispensable tool for computational-based design and optimization, statistical analysis, embedded computing, and real-time optimal control.
Numerous research efforts have focused on the development of tools for model reduction.
One effective tool for model reduction is parameter sensitivity analysis, which elucidates the dependence of system dynamics on the parameters.
Functional principal component analysis and mixture models have become popular dimension reduction tools in microarray studies to cluster genes of similar temporal patterns [6] [12].
However, they are more or less disconnected from the classical literature and tools dealing with model reduction.
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