Biology subjects

Ireland, W. T.

Publications and source records attributed to Ireland, W. T..

3 recordsLinked to original sources

bioRxiv2020

MAVE-NN: Quantitative Modeling of Genotype-Phenotype Maps as Information Bottlenecks

Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning (DMS) experiments on proteins and massively parallel reporter assays (MPRAs) on gene regulatory sequences. However, a general strategy for inferring quantitative models of genotype-phenotype (G-P) maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning G-P maps--including biophysically interpretable models--from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.

bioinformatics↗

bioRxiv2018

Mapping DNA sequence to transcription factor binding energy in vivo

Despite the central importance of transcriptional regulation in systems biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to analyze a promoter sequence and identify the locations, regulatory roles, and energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for interpreting transcriptional regulatory sequences using in vivo methods (i.e. the massively parallel reporter assay Sort-Seq) to formulate quantitative models that map a transcription factor binding sites DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 kBT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factors sequence specificity.

biophysics↗

bioRxiv2017

A systematic approach for dissecting the molecular mechanisms of transcriptional regulation in bacteria

Gene regulation is one of the most ubiquitous processes in biology. But while the catalog of bacterial genomes continues to expand rapidly, we remain ignorant about how almost all of the genes in these genomes are regulated. At present, characterizing the molecular mechanisms by which individual regulatory sequences operate requires focused efforts using low-throughput methods. Here we show how a combination of massively parallel reporter assays, mass spectrometry, and information-theoretic modeling can be used to dissect bacterial promoters in a systematic and scalable way. We demonstrate this method on both well-studied and previously uncharacterized promoters in the enteric bacterium Escherichia coli. In all cases we recover nucleotide-resolution models of promoter mechanism. For some promoters, including previously unannotated ones, the approach allowed us to further extract quantitative biophysical models describing input-output relationships. This method opens up the possibility of exhaustively dissecting the mechanisms of promoter function in E. coli and a wide range of other bacteria.

biophysics↗