title: Global parameter estimation for thermodynamic models of transcriptional regulation.
abstract: Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort.
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. suleime1@illinois.edu
authors.forename: Yerzhan
authors.initials: Y
authors.lastname: Suleimenov
authors.forename: Ahmet
authors.initials: A
authors.lastname: Ay
authors.forename: Md Abul Hassan
authors.initials: MA
authors.lastname: Samee
authors.forename: Jacqueline M
authors.initials: JM
authors.lastname: Dresch
authors.forename: Saurabh
authors.initials: S
authors.lastname: Sinha
authors.forename: David N
authors.initials: DN
authors.lastname: Arnosti
mesh_terms: D000465:Algorithms; D000818:Animals; D004331:Drosophila melanogaster / Q000196:embryology / Q000235:genetics* / Q000378:metabolism; D004625:Embryo, Nonmammalian / Q000166:cytology / Q000378:metabolism*; D020869:Gene Expression Profiling; D018507:Gene Expression Regulation, Developmental; D006801:Humans; D008957:Models, Genetic; D049490:Systems Biology / Q000379:methods*; D013816:Thermodynamics; D014158:Transcription, Genetic
keywords: CMA–ES: Covariance matrix adaptation–evolutionary strategy; Covariance matrix adaptation–evolutionary strategy; Gene regulation; Nelder-Mead simplex method; Parameter estimation; QN/NMS: Quasi-Newton/Nelder-Mead simplex; Quasi-Newton method; Thermodynamic modeling