# Research

Below is a list of my past, current, and future research projects. Where available, links have been provided to the publisher, paper website, and GitHub repository. If you cannot access one of the papers, let me know.

## Allosteric control in transcriptional regulation

Allosteric regulation is found across all domains of life, yet we still lack simple, predictive theories that directly link the experimentally tunable parameters of a system to its input-output response. Through use of a statistical mechanical depiction of the Monod-Wyman-Changeux model of allostery, one can derive analytical expressions for the fold-change in gene expression of a transcriptional regulatory circuit in which the transcription factor is the allosteric regulator. In Razo-Mejia and Chure, et al., we put forward such a theory and rigorously test it experimentally in the context of the most ubiquitous bacterial regulatory architecture - simple repression. The result of this extensive theory and experiment dialogue is the simple input-output function schematized below which defines the fold-change in gene expression.

## The Energetics of Molecular Adaptation

Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses. In this work, Ij present a framework to link individual mutations in a transcriptional repressor to the parameters which govern its response through measuring changes in the free energy of the system. Our findings are that the energetic effects of the mutations can be categorized into several classes which have stereotypical curves as a function of the inducer concentration. These diagnostic predictions are tested experimentally well-characterized LacI repressor of Escherichia coli, probing several mutations in the DNA binding and inducer binding domains. The change in gene expression in response to a point mutation can be captured by modifying a subset of the model parameters which describe the respective domain of the wild-type protein. These parameters appear to be insulated, with mutations in the DNA binding domain altering only the DNA affinity and those in the inducer binding domain altering only the allosteric parameters. Changing these subsets of parameters tunes the free energy of the system in a way that is concordant with theoretical expectations. Finally, I show that the induction profiles and resulting free energies associated with double mutants can be predicted with quantitative accuracy given knowledge of the single mutants, providing an avenue for identifying and quantifying epistatic interactions. The interactive figure below gives a sense of how changing the various biophysical parameters alters the induction profile and the free energy difference.

Bokeh Plot

## Mechanosensation and survival under osmotic shock

Mechanosensitive (MS) channels are transmembrane protein complexes which open and close in response to changes in membrane tension as a result of osmotic shock. Despite extensive biophysical characterization, the contribution of these channels to cell survival remains largely unknown. In Chure, Lee, and Phillips 2018 we use quantitative video microscopy to measure the abundance of a single species of MS channel in single cells followed by their survival after a large osmotic shock. We observe total death of the population with less than 100 channels per cell and determine that approximately 500 - 700 channels are needed for 80% survival. The number of channels we find to confer nearly full survival is consistent with the counts of the number of channels in wild type cells in several earlier studies. These results prompt further studies to dissect the contribution of other channel species to survival. The .gif below shows a representative experiment with a field of cells recovering after a strong osmotic shock.

## Developing Pipelines For Open and Reproducible Research

GitHub Repository

As biological data becomes bigger and bigger and analysis routines become more computationally sophisticated, the scientific process must be adapted such that research is reproducible. I’ve committed myself to using the git version control system coupled with the hosting service GitHub as a laboratory notebook and data repository. Each project I am involved in has its own repository (linked above) and I have created Reproducible Research: A Template For Using Git and GitHub As A Scientific Research Tool which sets out the bare bones of how I structure my projects. If you are interested in doing the same, feel free to fork the repository and modify as you see fit.