Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI

Andrea Degasperi, Lan K. Nguyen, Dirk Fey, Boris N. Kholodenko

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-review

Abstract

Ordinary differential equation models are used to represent intracellular signaling pathways in silico, aiding and guiding biological experiments to elucidate intracellular regulation. To construct such quantitative and predictive models of intracellular interactions, unknown parameters need to be estimated. Most of biological data are expressed in relative or arbitrary units, raising the question of how to compare model simulations with data. It has recently been shown that for models with large number of unknown parameters, fitting algorithms using a data-driven normalization of the simulations (DNS) performs best in terms of the convergence time and parameter identifiability. DNS approach compares model simulations and corresponding data both normalized by the same normalization procedure, without requiring additional parameters to be estimated, as necessary for widely used scaling factor-based methods. However, currently there is no parameter estimation software that directly supports DNS. In this chapter, we show how to apply DNS to dynamic models of systems and synthetic biology using PEPSSBI (Parameter Estimation Pipeline for Systems and Synthetic Biology). PEPSSBI is the first software that supports DNS, through algorithmically supported data normalization and objective function construction. PEPSSBI also supports model import using SBML and repeated parameter estimation runs executed in parallel either on a personal computer or a multi-CPU cluster.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
EditorsQuentin Vanhaelen
Place of PublicationNew York NY USA
PublisherHumana Press
Chapter5
Pages91-115
Number of pages25
Edition1st
ISBN (Electronic)9781071617670
ISBN (Print)9781071617663
DOIs
Publication statusPublished - 2022

Publication series

NameMethods in Molecular Biology
Volume2385
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Data normalization
  • ODE models
  • Parameter estimation
  • Relative data
  • Signaling pathways

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