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Large-Scale Networks in Engineering and Life SciencesHybrid Modeling for Systems Biology: Theory and Practice

Large-Scale Networks in Engineering and Life Sciences: Hybrid Modeling for Systems Biology:... [Whereas bottom-up systems biology relies primarily on parametric mathematical models, which try to infer the system behavior from a priori specified mechanisms, top-down systems biology typically applies nonparametric techniques for system identification based on extensive “omics” data sets. Merging bottom-up and top-down into middle-out strategies is confronted with the challenge of handling and integrating the two types of models efficiently. Hybrid semiparametric models are natural candidates since they combine parametric and nonparametric structures in the same model structure. They enable to blend mechanistic knowledge and data-based identification methods into models with improved performance and broader scope. This chapter aims at giving an overview on theoretical fundaments of hybrid modeling for middle-out systems biology and to provide practical examples of applications, which include hybrid metabolic flux analysis on ill-defined metabolic networks, hybrid dynamic models with unknown reaction kinetics, and hybrid dynamic models of biochemical systems with intrinsic time delays.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Large-Scale Networks in Engineering and Life SciencesHybrid Modeling for Systems Biology: Theory and Practice

Part of the Modeling and Simulation in Science, Engineering and Technology Book Series
Editors: Benner, Peter; Findeisen, Rolf; Flockerzi, Dietrich; Reichl, Udo; Sundmacher, Kai
von Stosch, Moritz; Carinhas, Nuno; Oliveira, Rui
Large-Scale Networks in Engineering and Life Sciences — Jul 30, 2014
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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2014
ISBN
978-3-319-08436-7
Pages
367 –388
DOI
10.1007/978-3-319-08437-4_7
Publisher site
See Chapter on Publisher Site

Abstract

[Whereas bottom-up systems biology relies primarily on parametric mathematical models, which try to infer the system behavior from a priori specified mechanisms, top-down systems biology typically applies nonparametric techniques for system identification based on extensive “omics” data sets. Merging bottom-up and top-down into middle-out strategies is confronted with the challenge of handling and integrating the two types of models efficiently. Hybrid semiparametric models are natural candidates since they combine parametric and nonparametric structures in the same model structure. They enable to blend mechanistic knowledge and data-based identification methods into models with improved performance and broader scope. This chapter aims at giving an overview on theoretical fundaments of hybrid modeling for middle-out systems biology and to provide practical examples of applications, which include hybrid metabolic flux analysis on ill-defined metabolic networks, hybrid dynamic models with unknown reaction kinetics, and hybrid dynamic models of biochemical systems with intrinsic time delays.]

Published: Jul 30, 2014

Keywords: Systems biology; Middle-out systems biology; Hybrid modeling; Hybrid semiparametric modeling; Parametric/nonparametric modeling

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