Literature DB >> 23643643

A primer on thermodynamic-based models for deciphering transcriptional regulatory logic.

Jacqueline M Dresch1, Megan Richards, Ahmet Ay.   

Abstract

A rigorous analysis of transcriptional regulation at the DNA level is crucial to the understanding of many biological systems. Mathematical modeling has offered researchers a new approach to understanding this central process. In particular, thermodynamic-based modeling represents the most biophysically informed approach aimed at connecting DNA level regulatory sequences to the expression of specific genes. The goal of this review is to give biologists a thorough description of the steps involved in building, analyzing, and implementing a thermodynamic-based model of transcriptional regulation. The data requirements for this modeling approach are described, the derivation for a specific regulatory region is shown, and the challenges and future directions for the quantitative modeling of gene regulation are discussed.
Copyright © 2013 Elsevier B.V. All rights reserved.

Keywords:  Enhancer; Parameter estimation; Sensitivity analysis; Thermodynamic modeling; Transcriptional regulation

Mesh:

Year:  2013        PMID: 23643643     DOI: 10.1016/j.bbagrm.2013.04.011

Source DB:  PubMed          Journal:  Biochim Biophys Acta        ISSN: 0006-3002


  3 in total

1.  Fitting thermodynamic-based models: Incorporating parameter sensitivity improves the performance of an evolutionary algorithm.

Authors:  Michael J Gaiewski; Robert A Drewell; Jacqueline M Dresch
Journal:  Math Biosci       Date:  2021-10-21       Impact factor: 2.144

2.  Expression pattern determines regulatory logic.

Authors:  Carlos Mora-Martinez
Journal:  PLoS One       Date:  2021-01-04       Impact factor: 3.240

3.  Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network.

Authors:  Vitaly V Gursky; Konstantin N Kozlov; Ivan V Kulakovskiy; Asif Zubair; Paul Marjoram; David S Lawrie; Sergey V Nuzhdin; Maria G Samsonova
Journal:  PLoS One       Date:  2017-09-12       Impact factor: 3.240

  3 in total

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