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PLoS One. 2013 Sep 30;8(9):e74335. doi: 10.1371/journal.pone.0074335. eCollection 2013.

Lessons learned from quantitative dynamical modeling in systems biology.

Author information

1
Institute of Physics, University of Freiburg, Freiburg, Germany ; Institute of Computational Biology, Helmholtz Center, Munich, Germany.

Erratum in

  • PLoS One. 2013;8(12). doi:10.1371/annotation/ea0193d8-1f7f-492a-b0b7-d877629fdcee. Schelke, Max [corrected to Schelker, Max].

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.

PMID:
24098642
PMCID:
PMC3787051
DOI:
10.1371/journal.pone.0074335
[Indexed for MEDLINE]
Free PMC Article

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