title: Thermodynamics-based models of transcriptional regulation with gene sequence.
abstract: Quantitative models of gene regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled or heuristic approximations of the underlying regulatory mechanisms. In this work, we have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence. The proposed model relies on a continuous time, differential equation description of transcriptional dynamics. The sequence features of the promoter are exploited to derive the binding affinity which is derived based on statistical molecular thermodynamics. Experimental results show that the proposed model can effectively identify the activity levels of transcription factors and the regulatory parameters. Comparing with the previous models, the proposed model can reveal more biological sense.
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. sq.wang@siat.ac.cn.
authors.forename: Shuqiang
authors.initials: S
authors.lastname: Wang
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Yanyan
authors.initials: Y
authors.lastname: Shen
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Jinxing
authors.initials: J
authors.lastname: Hu
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002940:Circadian Rhythm; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D051379:Mice; D008810:Mice, Inbred C57BL; D008962:Models, Theoretical; D013493:Suprachiasmatic Nucleus / Q000378:metabolism; D013816:Thermodynamics; D014158:Transcription, Genetic
keywords: Binding energy; Chemical thermodynamics; Gene sequence feature; Transcriptional regulation