Effective Single-Step Posttranscriptional Dynamics Allowing for a Direct Maximum Likelihood Estimation of Transcriptional Activity and the Quantification of Sources of Gene Expression Variability with an Illustration for the Hypoxia and TNFα Regulated Inflammatory Pathway. (9th September 2013)

• Record Type: Journal Article
• Authors: Frank, T. D.; Collins, A. J. F.; Cheong, A.
• Other Names: Kalyana-Sundaram S. Academic Editor.; Oliva B. Academic Editor.
• Is Part Of: ISRN computational biology. Volume 2013(2013)
• Publication Date: 2013-09-09
• Subjects: Computational biology -- Periodicals; Computational biology; Electronic journals; Periodicals; 570.285
• DOI: 10.1155/2013/719138
• Languages: English
• ISSNs: 2314-5420

Abstract

Abstract : Data analysis methods for estimating promoter activity from gene reporter data frequently involve the reconstruction of the dynamics of unobserved species and numerical search algorithms for determining optimal model parameters. In contrast, we argue that posttranscriptional dynamics effectively behave like a singlestep stochastic process when gene expression variability is relatively low and, half-lives of the unobserved species are relatively small compared to characteristic observation time scales. In this case, by means of maximum likelihood estimators, for which analytical expressions exist, transcriptional activity of gene promoters can be estimated directly from observed gene reporter data without the need for numerical search algorithms and the reconstruction of unobserved variables. In addition, the model-based data analysis approach yields a single variable that measures the effective strength of the sources that give rise to gene expression variability. The approach is applied to conduct a model-based analysis of the inflammatory pathway under hypoxia condition and stimulation with tumor necrosis factor alpha in HEK293 cells.