Transcriptional Regulation by the Numbers 1: Models

By Lacramioara Bintu, Nicolas E. Buchler, Hernan G. Garcia, U. Gerland, Terence Hwa, J. Kondev, Rob Phillips. 2004

542 citations recorded by the source corpus.

The study of gene regulation and expression is often discussed in quantitative terms. In particular, the expression of genes is regularly characterized with respect to how much, how fast, when and where. Whether discussing the level of gene expression in a bacterium or its precise location within a developing embryo, the natural language for these experiments is that of numbers. Such quantitative data demands quantitative models. We review a class of models ("thermodynamic models") which exploit statistical mechanics to compute the probability that RNA polymerase is at the appropriate promoter. This provides a mathematically precise elaboration of the idea that activators are agents of recruitment which increase the probability that RNA polymerase will be found at the promoter of interest. We discuss a framework which describes the interactions of repressors, activators, helper molecules and RNA polymerase using the concept of effective concentrations, expressed in terms of a function we call the "regulation factor". This analysis culminates in an expression for the probability of RNA polymerase binding at the promoter of interest as a function of the number of regulatory proteins in the cell. In a companion paper [1], these ideas are applied to several case studies which illustrate the use of the general formalism.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ecd24af095118bf178cb5b8f09d6d139c7ce623b

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Citations in the corpus

Transcriptional Regulation by the Numbers 2: Applications (2004, methodology): “On the other hand, the thermodynamic model reviewed in the preceding article [9] yields several general mathematical forms for the dependence of the fold-change in gene expression on the concentration(s) of the TF(s) regulating transcription.”
Growth-rate dependent global effects on gene expression in bacteria (2009, background): “With the emergence of a ''system-level'' focus in biology, there has been an increasing emphasis on characterizing gene expression and its regulation in a quantitative fashion (Bintu et al., 2005; Golding et al., 2005; Hasty et al., 2002; Kaplan et al., 2008; Kuhlman et al., 2007).”
Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence (2010, result): “measured transcriptional activity resulting from different in vivo concentrations of active CRP and showed that the resulting functional form of this activity was consistent with a simple thermodynamic model (13).”
Implications of the Hybrid Epithelial/Mesenchymal Phenotype in Metastasis (2015, background): “…of these two loops – two binding sites of miR-34 on SNAIL mRNA vs. six binding sites of miR-200 on ZEB mRNA (55, 56), self-inhibition of SNAIL vs. (indirect) self-activation of ZEB (57, 58), and finally the difference in transcriptional regulation vs. translational regulation by miRs (59–63).”
Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective (2012, methodology): “RNAP binding has been treated within the framework of equilibrium statistical mechanics [1492, 1493].”
Network inference using steady-state data and Goldbeter-Koshland kinetics (2012, background): “Kinetic formulations have been widely studied in the systems biology literature (Bintu et al., 2005) and recently there has been much interest in statistical inference for such systems, with examples including Chen et al. (2009); Xu et al. (2010).”