ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MATLAB™ - based toolbox for model invalidation, state and parameter estimation using unknown-but-bounded quantitative data or qualitative information given as if-then statements. Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties or even only of qualitative nature. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. ADMIT implements novel algorithms for modeling and analysis of various types of biological networks such as signaling, metabolic or gene-regulation networks, and networks involving discrete variables. Nonlinear constraint satisfaction problems are easily constructed given the quantitative, qualitative information, and model descriptions, as illustrated in the tutorial and in the examples. A detailed understanding of the underlying mathematical concepts is not needed to run the examples or to produce own ones. Compared to approaches based on samples, the set-based approach allows definite statements on entire regions in the parameter space to be made. Furthermore, since only unknown- but-bounded uncertainties are assumed, no assumptions on statistics of measurements have to be made.
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References in zbMATH (referenced in 1 article )
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- Rumschinski, Philipp; Streif, Stefan; Findeisen, Rolf: Combining qualitative information and semi-quantitative data for guaranteed invalidation of biochemical network models (2012)