Precise physical models of protein-DNA interaction from high-throughput data.

ABSTRACT: A cell's ability to regulate gene transcription depends in large part on the energy with which transcription factors (TFs) bind their DNA regulatory sites. Obtaining accurate models of this binding energy is therefore an important goal for quantitative biology. In this article, we present a principled likelihood-based approach for inferring physical models of TF-DNA binding energy from the data produced by modern high-throughput binding assays. Central to our analysis is the ability to assess the relative likelihood of different model parameters given experimental observations. We take a unique approach to this problem and show how to compute likelihood without any explicit assumptions about the noise that inevitably corrupts such measurements. Sampling possible choices for model parameter

SUBMITTER: Kinney JB 

PROVIDER: S-EPMC1766414 | biostudies-literature | 2007 Jan

REPOSITORIES: biostudies-literature