Fluctuations in transcription factor binding can explain the graded and binary responses observed in inducible gene expression.
ABSTRACT Inducible genes are expressed in the presence of an external stimulus. Individual cells may exhibit either a binary or graded response to such signals. It has been hypothesized that the chemical kinetics of transcription factor/DNA interactions can account for both these scenarios (EMBO J. 9(9) (1990) 2835; BioEssays 14(5) (1992) 341). To explore this question, we have conducted work based on the experimental results of Fiering et al. (Genes Dev. 4 (10) (1990) 1823). In these experiments, three upstream NF-AT binding sites control transcription of the lacZ gene, which codes for the enzyme beta-Galactosidase. The experimental data show a binary response for this system. We consider the effects of fluctuations in NF-AT binding on the response of the system. Our modeling results are in good qualitative agreement with the experimental data, and illustrate how the binary and graded responses can stem from the same underlying mechanism.
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ABSTRACT: We investigate cellular response to extracellular signals by using information theory techniques motivated by recent experiments. We present results for the steady state of the following gene regulatory models found in both prokaryotic and eukaryotic cells: a linear transcription-translation model and a positive or negative auto-regulatory model. We calculate both the information capacity and the mutual information exactly for simple models and approximately for the full model. We find that (1) small changes in mutual information can lead to potentially important changes in cellular response and (2) there are diminishing returns in the fidelity of response as the mutual information increases. We calculate the information capacity using Gillespie simulations of a model for the TNF-α-NF-κ B network and find good agreement with the measured value for an experimental realization of this network. Our results provide a quantitative understanding of the differences in cellular response when comparing experimentally measured mutual information values of different gene regulatory models. Our calculations demonstrate that Gillespie simulations can be used to compute the mutual information of more complex gene regulatory models, providing a potentially useful tool in synthetic biology.Physical Biology 07/2014; 11(4):046004. · 3.14 Impact Factor
- Journal of Biological Systems - JBS. 01/2009; 17(03).