The lower bound on the precision of transcriptional regulation

TL;DR

This paper derives an analytical lower bound on the precision of transcriptional regulation based on diffusion processes, validated by simulations, and shows how sliding enhances regulation precision.

Contribution

It provides a new analytical expression for the lower bound on transcriptional regulation precision considering 1D and 3D diffusion, with implications for modeling and understanding gene regulation.

Findings

The theory accurately predicts the lower bound under biological conditions.

Promoter switching can be approximated as a Markov process even with sliding.

Sliding can double the precision of transcriptional regulation.

Abstract

The diffusive arrival of transcription factors at the promoter sites on the DNA sets a lower bound on how accurately a cell can regulate its protein levels. Using results from the literature on diffusion-influenced reactions, we derive an analytical expression for the lower bound on the precision of transcriptional regulation. In our theory, transcription factors can perform multiple rounds of 1D diffusion along the DNA and 3D diffusion in the cytoplasm before binding to the promoter. Comparing our expression for the lower bound on the precision against results from Green's Function Reaction Dynamics simulations shows that the theory is highly accurate under biologically relevant conditions. Our results demonstrate that, to an excellent approximation, the promoter switches between the transcription-factor bound and unbound state in a Markovian fashion. This remains true even in the presence of sliding, i.e. with 1D diffusion along the DNA. This has two important implications: (1) minimizing the noise in the promoter state is equivalent to minimizing the search time of transcription factors for their promoters; (2) the complicated dynamics of 3D diffusion in the cytoplasm and 1D diffusion along the DNA can be captured in a well-stirred model by renormalizing the promoter association and dissociation rates, making it possible to efficiently simulate the promoter dynamics using Gillespie simulations. Based on the recent experimental observation that sliding can speed up the promoter search by a factor of 4, our theory predicts that sliding can enhance the precision of transcriptional regulation by a factor of 2.