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Improved Parameter Estimation in Kinetic Models: Selection and Tuning of Regularization Methods

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Computational Methods in Systems Biology (CMSB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8859))

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Abstract

Kinetic models are being increasingly used as a systematic framework to understand function in biological systems. Calibration of these nonlinear dynamic models remains challenging due to the nonconvexity and ill-conditioning of the associated inverse problems. Nonconvexity can be dealt with suitable global optimization. Here, we focus on simultaneously dealing with ill-conditioning by making use of proper regularization methods. Regularized calibrations ensure the best trade-offs between bias and variance, thus reducing over-fitting. We present a critical comparison of several methods, and guidelines for properly tuning them. The performance of this procedure and its advantages are illustrated with a well known benchmark problem considering several scenarios of data availability and measurement noise.

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Author information

Authors and Affiliations

  1. BioProcess Engineering Group, IIM-CSIC, Vigo, Spain

    Attila Gábor & Julio R. Banga

Authors
  1. Attila Gábor
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  2. Julio R. Banga
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Editor information

Editors and Affiliations

  1. Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK

    Pedro Mendes , Joseph O. Dada  & Kieran Smallbone ,  & 

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Gábor, A., Banga, J.R. (2014). Improved Parameter Estimation in Kinetic Models: Selection and Tuning of Regularization Methods. In: Mendes, P., Dada, J.O., Smallbone, K. (eds) Computational Methods in Systems Biology. CMSB 2014. Lecture Notes in Computer Science(), vol 8859. Springer, Cham. https://doi.org/10.1007/978-3-319-12982-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-12982-2_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12981-5

  • Online ISBN: 978-3-319-12982-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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