Mary J. Dunlop

Gene circuit-driven amplified selection enables evolution of fast-growing Escherichia coli

This paper shows that a synthetic gene circuit can break a growth rate plateau in E. coli by selectively suppressing slow-growing cells, leading to faster growth.

Engineering a light-responsive, orthogonal Lon protease in E. coli for targeted protein degradation

The paper develops a light-responsive, orthogonal Lon protease in E. coli for targeted protein degradation, showing it can degrade proteins in both growth phases and respond to light intensity.

Synthetic cell-cell adhesion provides benefits of proximity without diffusion-related costs

This study shows that synthetic cell-cell adhesion in yeast allows large aggregates to grow as well as unicellular strains, suggesting benefits from nutrient sharing rather than diffusion limitations.

Dynamic heterogeneity in an E. coli stress response regulon mediates gene activation and antimicrobial peptide tolerance

The paper investigates how heterogeneity in PhoP expression affects downstream gene activation and antimicrobial peptide tolerance in E. coli.

Single-cell characterization of bacterial optogenetic Cre recombinases

This study compares three bacterial optogenetic Cre recombinase variants at the single-cell level, revealing heterogeneous activity and reporter-dependent performance.

Cell-TRACTR: A transformer-based model for end-to-end segmentation and tracking of cells

Cell-TRACTR is a transformer-based model that segments and tracks cells in microscopy images, outperforming existing methods in tracking and division accuracy.

Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival

The paper uses Bayesian inference to predict antibiotic survival in E. coli by integrating gene expression and growth rate data from single cells.

Red light responsive Cre recombinase for bacterial optogenetics

This paper introduces OptoCre-REDMAP, a red light inducible Cre recombinase system for bacteria, demonstrating its reliability and flexibility in gene expression control.

Generating information-dense promoter sequences with optimal string packing

The paper introduces a method to optimally pack DNA-protein binding sites into dense nucleotide sequences, solving an NP-hard problem using integer linear programming.

Comprehensive screening of a light-inducible split Cre recombinase with domain insertion profiling

The paper develops a pooled library screening method with a Bayesian approach to improve prediction of split protein behavior using light-inducible Cre recombinase.

Deep neural networks for predicting single cell responses and probability landscapes

This paper uses deep neural networks to predict single-cell responses and probability landscapes, showing they can infer cell dynamics despite noise and stochasticity.

Longitudinal single-cell chemical imaging of engineered strains reveals heterogeneity in fatty acid production

This study uses longitudinal single-cell chemical imaging to reveal fatty acid production heterogeneity in engineered E. coli strains over time.

Controlled protein activities with viral proteases, antiviral peptides, and antiviral drugs

The paper develops a system using viral proteases and antiviral drugs to control protein activity and gene modulation through allosteric regulation of Cre recombinase.

Light inducible protein degradation in E. coli with the LOVdeg tag

This paper introduces LOVdeg, a light-inducible protein degradation tag for E. coli, demonstrating its modularity and utility in optogenetic control.

An Optogenetic Toolkit for Light-Inducible Antibiotic Resistance

This paper develops light-inducible antibiotic resistance in E. coli using optogenetic tools, enabling spatiotemporal control of cell survival.

Deep model predictive control of gene expression in thousands of single cells

This study uses deep learning to control gene expression dynamics in E. coli cells, enabling real-time, cell-specific regulation of gene expression patterns.

Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response

This study uses single-cell time-lapse microscopy to show that gene expression and growth dynamics in Escherichia coli influence survival during antibiotic stress.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

DeLTA 2.0 is a deep learning tool for analyzing time-lapse microscopy data, enabling accurate segmentation and tracking of single cells in two-dimensional environments.

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

The paper introduces a deep learning framework for automated cell segmentation, tracking, and lineage reconstruction in time-lapse microscopy data with high accuracy and speed.

Forecasting cell fate during antibiotic exposure using stochastic gene expression

The paper shows that gene expression variability before antibiotic exposure predicts cell survival times, highlighting non-uniform antibiotic killing and identifying key genes involved.

Salicylate increases fitness cost associated with MarA-mediated antibiotic resistance

This study shows that salicylate increases the fitness cost of MarA-mediated antibiotic resistance, leading to the rapid exclusion of resistant bacteria from mixed populations.

Distinct timescales of RNA regulators enable the construction of a genetic pulse generator

The paper characterizes two RNA regulators with distinct timescales and demonstrates their use in creating a genetic pulse generator through computational modeling and experimental validation.

Active degradation of a regulator controls coordination of downstream genes

The paper investigates how active degradation of the MarA regulator affects coordination of downstream genes in E. coli, finding trade-offs between coordination speed and maximum coordination.