Model discrimination in dynamic molecular systems: Application to parotid de-differentiation network

Jaejik Kim, Jiaxu Li, Srirangapatnam G. Venkatesh, Douglas S. Darling, Grzegorz A. Rempala

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

In modern systems biology the modeling of longitudinal data, such as changes in mRNA concentrations, is often of interest. Fully parametric, ordinary differential equations (ODE)-based models are typically developed for the purpose, but their lack of fit in some examples indicates that more flexible Bayesian models may be beneficial, particularly when there are relatively few data points available. However, under such sparse data scenarios it is often difficult to identify the most suitable model. The process of falsifying inappropriate candidate models is called model discrimination. We propose here a formal method of discrimination between competing Bayesian mixture-type longitudinal models that is both sensitive and sufficiently flexible to account for the complex variability of the longitudinal molecular data. The ideas from the field of Bayesian analysis of computer model validation are applied, along with modern Markov Chain Monte Carlo (MCMC) algorithms, in order to derive an appropriate Bayes discriminant rule. We restrict attention to the two-model comparison problem and present the application of the proposed rule to the mRNA data in the de-differentiation network of three mRNA concentrations in mammalian salivary glands as well as to a large synthetic dataset derived from the model used in the recent DREAM6 competition.

Original languageEnglish (US)
Pages (from-to)524-539
Number of pages16
JournalJournal of Computational Biology
Volume20
Issue number7
DOIs
Publication statusPublished - Jul 1 2013

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Keywords

  • Bayesian factor
  • ODE model
  • parameter estimation
  • parotid dedifferentiation

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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