Cargando…
Lessons Learned from Quantitative Dynamical Modeling in Systems Biology
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...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3787051/ https://www.ncbi.nlm.nih.gov/pubmed/24098642 http://dx.doi.org/10.1371/journal.pone.0074335 |
_version_ | 1782286132819525632 |
---|---|
author | Raue, Andreas Schilling, Marcel Bachmann, Julie Matteson, Andrew Schelke, Max Kaschek, Daniel Hug, Sabine Kreutz, Clemens Harms, Brian D. Theis, Fabian J. Klingmüller, Ursula Timmer, Jens |
author_facet | Raue, Andreas Schilling, Marcel Bachmann, Julie Matteson, Andrew Schelke, Max Kaschek, Daniel Hug, Sabine Kreutz, Clemens Harms, Brian D. Theis, Fabian J. Klingmüller, Ursula Timmer, Jens |
author_sort | Raue, Andreas |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3787051 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-37870512013-10-04 Lessons Learned from Quantitative Dynamical Modeling in Systems Biology Raue, Andreas Schilling, Marcel Bachmann, Julie Matteson, Andrew Schelke, Max Kaschek, Daniel Hug, Sabine Kreutz, Clemens Harms, Brian D. Theis, Fabian J. Klingmüller, Ursula Timmer, Jens PLoS One Research Article 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. Public Library of Science 2013-09-30 /pmc/articles/PMC3787051/ /pubmed/24098642 http://dx.doi.org/10.1371/journal.pone.0074335 Text en © 2013 Raue et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Raue, Andreas Schilling, Marcel Bachmann, Julie Matteson, Andrew Schelke, Max Kaschek, Daniel Hug, Sabine Kreutz, Clemens Harms, Brian D. Theis, Fabian J. Klingmüller, Ursula Timmer, Jens Lessons Learned from Quantitative Dynamical Modeling in Systems Biology |
title | Lessons Learned from Quantitative Dynamical Modeling in Systems Biology |
title_full | Lessons Learned from Quantitative Dynamical Modeling in Systems Biology |
title_fullStr | Lessons Learned from Quantitative Dynamical Modeling in Systems Biology |
title_full_unstemmed | Lessons Learned from Quantitative Dynamical Modeling in Systems Biology |
title_short | Lessons Learned from Quantitative Dynamical Modeling in Systems Biology |
title_sort | lessons learned from quantitative dynamical modeling in systems biology |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3787051/ https://www.ncbi.nlm.nih.gov/pubmed/24098642 http://dx.doi.org/10.1371/journal.pone.0074335 |
work_keys_str_mv | AT raueandreas lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT schillingmarcel lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT bachmannjulie lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT mattesonandrew lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT schelkemax lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT kaschekdaniel lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT hugsabine lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT kreutzclemens lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT harmsbriand lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT theisfabianj lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT klingmullerursula lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology AT timmerjens lessonslearnedfromquantitativedynamicalmodelinginsystemsbiology |