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Schmiester, L. ; Weindl, D. ; Hasenauer, J.

Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach.

J. Math. Biol. 81, 603–623 (2020)

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Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Dynamical Modeling ; Optimization ; Parameter Estimation ; Qualitative Data ; Systems Biology; Identifiability Analysis; Systems; Identification

ISSN (print) / ISBN 0303-6812

e-ISSN 1432-1416

Journal Journal of Mathematical Biology

Quellenangaben Volume: 81, Pages: 603–623

Publisher Springer

Publishing Place Tiergartenstrasse 17, D-69121 Heidelberg, Germany

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)