Griffin Chure, PhD


Below is a short-ish 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.

Postdoctoral Research

Leveraging proteomics to identify limiting processes for bacterial growth

Manuscript in preparation.

Recent years have seen an experimental deluge interrogating the relationship between bacterial growth rate, cell size, and protein content, quantifying the abundance of proteins across growth conditions with unprecedented resolution. However, we still lack a rigorous understanding of what sets the scale of these 18 quantities and when protein abundances should (or should not) depend on growth rate. In this forthcoming work, we seek to quantitatively understand this relationship across a collection of Escherichia coli proteomic data sets covering ≈ 4000 proteins and 36 growth rates. We estimate the basic requirements for steady-state growth by considering key processes in nutrient transport, energy generation, cell envelope biogenesis, and the central dogma. From these estimates, ribosome biogenesis emerges as a primary determinant of growth rate. We expand on this assessment by exploring a model of proteomic regulation as a function of the nutrient supply, revealing a mechanism that ties cell size and growth rate to ribosomal content. The figure below illustrates this mechanism where the amino acid accumulation rate (difference between the supply and consumption) can be related to the total ribosome copy number per cell. The plot on the right-hand side shows how the cellular growth rate scales with changing ribosome copy number and amino acid supply rate.

Human Impacts by the Numbers

Manuscript in preparation.

The greatest experiment of the last 10,000 years is the presence and action of modern human beings on planet Earth. At this point, the con- sequences of this experiment are being felt on many fronts. Yet, many people still hold the view that because the world is so “huge”, humans cannot really make a substantial impact. In this ongoing research we present a collection of what we have come to view as essential numbers that summarize the broad reach of human action across the planet, presenting a view of the impact of human presence on Earth. These numbers include recent estimates/measurements of the volume of meltwater released from ice-sheets on an annual basis, the year change in ocean acidity from the absorption of CO2 , the background plutonium isotope reactivity found in soils stemming from nuclear weapons testing in the 1960’s, to the number of livestock on the planet to give a few of many examples. In collecting and scrutinizing these data, we are also establishing the ’Human Impacts Database’, a internet database similar in spirit to the BioNumbers website that we hope will be used by scientists and the general public alike. The figure below shows how one can arrive at a reasonable order-of-magnitude estimate for the amount of land appropriated by humans for urban centers. Here, we use ‘ƒ’ tongue-in-cheek to mathematically denote ‘a few’.

Graduate Research

Allosteric control in transcriptional regulation

Publication in Cell Systems Website GitHub Repository

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.

Using shifts in free-energy as a classifier of mutant phenotypes

Publication in PNAS Website GitHub Repository

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 figure below gives a schematic representation of how mutations influence phenotypes by translations in free energy space.

Continuing Research

Developing Pipelines For Open and Reproducible Research

Template 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.