Global parameter estimation for thermodynamic models of transcriptional regulation

被引:6
作者
Suleimenov, Yerzhan [1 ]
Ay, Ahmet [2 ,3 ]
Samee, Md. Abul Hassan [1 ]
Dresch, Jacqueline M. [4 ]
Sinha, Saurabh [1 ]
Arnosti, David N. [5 ]
机构
[1] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA
[2] Colgate Univ, Dept Biol, Hamilton, NY 13346 USA
[3] Colgate Univ, Dept Math, Hamilton, NY 13346 USA
[4] Amherst Coll, Dept Math, Amherst, MA 01002 USA
[5] Michigan State Univ, Dept Biochem & Mol Biol, E Lansing, MI 48824 USA
基金
美国国家科学基金会;
关键词
Gene regulation; Thermodynamic modeling; Parameter estimation; Covariance matrix adaptation-evolutionary strategy; Quasi-Newton method; Nelder-Mead simplex method; BIOCHEMICAL PATHWAYS; GENE; EXPRESSION; OPTIMIZATION; PATTERN; SEGMENTATION; INFORMATION; REPRESSION; DIFFUSION; NETWORK;
D O I
10.1016/j.ymeth.2013.05.012
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:99 / 108
页数:10
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