J. G. Scott

A low-footprint, fluorescence-based bacterial time-kill assay for estimating dose-dependent cell death dynamics

This paper introduces a fluorescence-based time-kill assay using resazurin to measure dose-dependent cell death in bacteria, showing improved accuracy and reduced resource use compared to existing methods.

Diverse mutant selection windows shape spatialheterogeneity in evolving populations

This paper introduces fitness seascapes to model genotype-by-environment interactions, enabling more accurate prediction of drug resistance evolution by analyzing spatially heterogeneous mutant selection windows.

Evolution under vancomycin selection drives divergent collateral sensitivity patterns in Staphylococcus aureus

This study evolves Staphylococcus aureus under vancomycin selection, revealing two distinct adaptive pathways that lead to divergent collateral sensitivity patterns to first-line antibiotics.

Alamar Blue assay optimization to minimize druginterference and inter-assay viability

The paper optimizes the Alamar Blue assay to reduce drug interference and inter-assay viability variability by modifying protocol steps and adding rhodamine B calibration.

Frequency-dependent ecological interactions increase the prevalence and shape the distribution of pre-existing drug resistance

This study shows frequency-dependent ecological interactions help pre-existing drug resistance persist despite fitness costs, using simulations and engineered mutants in NSCLC.

Hidden Interactions: Frequency-Dependence Emulates Selection-Driven Dynamics in Evolving Populations

This paper shows that frequency-dependent interactions can mask or mimic frequency-independent selection in evolving populations, emphasizing the need to account for interactions in evolutionary inference.

Cuticular profiling of insecticide resistant Aedes aegypti

This study investigates cuticular resistance mechanisms in insecticide-resistant Aedes aegypti using ssNMR, GC-MS, and TEM, finding increased cuticle thickness and polysaccharide abundance.

Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance.

This study uses reinforcement learning to develop optimal antibiotic treatment strategies that reduce antimicrobial resistance in simulated E. coli populations.

A low-cost, open-source evolutionary bioreactor and its educational use

The paper introduces a low-cost, open-source morbidostat for educational use, validated through bacterial resistance experiments and student engagement.

Threshold-awareness in adaptive cancer therapy

This paper introduces a threshold-aware method for optimizing adaptive cancer therapies under stochastic tumor evolution, improving success chances within a drug budget.

Fitness seascapes are necessary for realistic modeling of the evolutionary response to drug therapy

The paper introduces a fitness seascape model to study drug resistance in E. coli by mapping genotypes to dose-response functions and shows that higher mutation supply and early drug adherence reduce resistance.

Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death Processes

The paper develops a method to disambiguate density-dependent mechanisms in birth-death processes using time series data, applied to understand drug resistance in cell populations.

Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems

This paper introduces spatial cumulant models to analyze and improve treatment strategies for cancer cell populations modeled as spatio-temporal point processes.

Range expansion shifts clonal interference patterns in evolving populations

This study examines how range expansion affects clonal interference patterns in evolving populations, finding that mutant fixation dynamics vary with population size, mutation rate, and spatial structure.

Measuring competitive exclusion in non-small cell lung cancer

This study measures frequency-dependent interactions between gefitinib-resistant and sensitive NSCLC populations to show that competitive exclusion depends on growth rate data, not just cost of resistance.

Modeling collaterally sensitive drug cycles: shaping heterogeneity to allow adaptive therapy

The paper develops a dynamical model for optimal sequential drug therapies with collaterally sensitive drugs and shows that shaping tumor heterogeneity followed by drug switching maintains subpopulations effectively.

Controlling the speed and trajectory of evolution with counterdiabatic driving

This paper introduces a method to control evolutionary speed and trajectory using counterdiabatic driving, with applications in disease treatment and climate adaptation.

Development and Characterization of Patient-Derived Xenografts from Non-Small Cell Lung Cancer Brain Metastases

This study establishes and characterizes patient-derived xenografts of NSCLC brain metastases, showing strong phenotypic and molecular correlation with original tumors and therapeutic response to targeted treatments.

Identifying states of collateral sensitivity during the evolution of therapeutic resistance in Ewings sarcoma

This study maps the temporal evolution of collateral sensitivity and resistance in Ewings sarcoma, showing unpredictable patterns in drug response and highlighting transcriptomic changes associated with these states.

Impact of immediate cryopreservation on the establishment of patient derived xenografts from head and neck cancer patients.

This study shows that fresh human cancer tissue can be cryopreserved and later implanted to establish PDXs without affecting growth rates or histologic parameters, enabling molecular profiling before implantation.

Recasting the cancer stem cell hypothesis: unification using a continuum model of microenvironmental forces

The paper unifies the cancer stem cell and stochastic models using a continuum model of microenvironmental forces, supported by clonogenic assay results.

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory

This paper optimizes adaptive cancer therapy using dynamic programming and evolutionary game theory to reduce drug usage and improve recovery outcomes compared to standard MTD-based treatment.

Inferring Tumour Proliferative Organisation from Phylogenetic Tree Measures in a Computational Model

This study uses computational modeling to infer tumour proliferative hierarchy by analyzing phylogenetic tree measures under cancer stem cell and neutral evolution assumptions.

Fibroblasts and Alectinib switch the evolutionary games played by non-small cell lung cancer

The paper develops a game assay to measure evolutionary games in cancer cell co-cultures, showing that fibroblasts and Alectinib shift the game type from Leader to Deadlock.

Evolutionary Dynamics Of Incubation Periods

The paper explains the lognormal distribution of incubation periods using evolutionary dynamics on graphs, showing that skewness arises from stochastic mechanisms in simple models.

Takeover times for a simple model of network infection

The paper analyzes the takeover time for a stochastic network infection model, showing different distribution patterns across various network structures.