title: Model discrimination in dynamic molecular systems: application to parotid de-differentiation network.
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.
authors.affiliation: Department of Biostatistics and Cancer Research Center, Georgia Regents University, Augusta, Georgia 30912, USA.
authors.forename: Jaejik
authors.initials: J
authors.lastname: Kim
authors.forename: Jiaxu
authors.initials: J
authors.lastname: Li
authors.forename: Srirangapatnam G
authors.initials: SG
authors.lastname: Venkatesh
authors.forename: Douglas S
authors.initials: DS
authors.lastname: Darling
authors.forename: Grzegorz A
authors.initials: GA
authors.lastname: Rempala
mesh_terms: D000465:Algorithms; D000681:Amylases / Q000235:genetics* / Q000378:metabolism; D051792:Basic Helix-Loop-Helix Proteins / Q000235:genetics* / Q000378:metabolism; D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D056004:Molecular Dynamics Simulation; D009010:Monte Carlo Method; D010306:Parotid Gland / Q000166:cytology* / Q000378:metabolism; D012333:RNA, Messenger / Q000235:genetics* / Q000378:metabolism; D060888:Real-Time Polymerase Chain Reaction; D020133:Reverse Transcriptase Polymerase Chain Reaction; D012471:Salivary Proteins and Peptides / Q000235:genetics* / Q000378:metabolism; D013997:Time Factors
chemical_list: D000681:Amylases; D051792:Basic Helix-Loop-Helix Proteins; D012333:RNA, Messenger; D012471:Salivary Proteins and Peptides; C559697:BHLHA15 protein, human; C045623:BPIFA2 protein, human