Lessons Learned from Quantitative Dynamical Modeling in Systems Biology

Raue A, Schilling M, Bachmann J, Matteson A, Schelke M, Kaschek D, Hug S, Kreutz C, Harms BD, Theis FJ, Klingmueller U, Timmer J (2013)

Publication Type: Journal article

Publication year: 2013

Journal

PLoS ONE Public Library of Science

Book Volume: 8

Article Number: e74335

Journal Issue: 9

DOI: 10.1371/journal.pone.0074335

Abstract

Due to the high complexity of biological data it is difficult to disentangle cellular processes relying only on intuitive interpretation of measurements. A Systems Biology approach that combines quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes. Nevertheless, with growing complexity and increasing amount of quantitative experimental data, building realistic and reliable mathematical models can become a challenging task: the quality of experimental data has to be assessed objectively, unknown model parameters need to be estimated from the experimental data, and numerical calculations need to be precise and efficient.Here, we discuss, compare and characterize the performance of computational methods throughout the process of quantitative dynamic modeling using two previously established examples, for which quantitative, dose- and time-resolved experimental data are available. In particular, we present an approach that allows to determine the quality of experimental data in an efficient, objective and automated manner. Using this approach data generated by different measurement techniques and even in single replicates can be reliably used for mathematical modeling. For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically. Our results show that deterministic derivative-based optimization employing the sensitivity equations in combination with a multi-start strategy based on latin hypercube sampling outperforms the other methods by orders of magnitude in accuracy and speed. Finally, we investigated transformations that yield a more efficient parameterization of the model and therefore lead to a further enhancement in optimization performance. We provide a freely available open source software package that implements the algorithms and examples compared here. © 2013 Raue et al.

Involved external institutions

Albert-Ludwigs-Universität Freiburg DE Germany (DE) Deutsches Krebsforschungszentrum (DKFZ) DE Germany (DE) New York University (NYU) US United States (USA) (US) Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Merrimack Pharmaceuticals, Inc. US United States (USA) (US)

How to cite

APA:

Raue, A., Schilling, M., Bachmann, J., Matteson, A., Schelke, M., Kaschek, D.,... Timmer, J. (2013). Lessons Learned from Quantitative Dynamical Modeling in Systems Biology. PLoS ONE, 8(9). https://doi.org/10.1371/journal.pone.0074335

MLA:

Raue, Andreas, et al. "Lessons Learned from Quantitative Dynamical Modeling in Systems Biology." PLoS ONE 8.9 (2013).

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