Iain G. Johnston

This study models how mitochondrial dynamics in plants enhance DNA repair efficiency by improving stoichiometry through targeted import, fusion, and metabolon exchange.

The paper uses multi-objective optimisation to model mitochondrial dynamics in plant cells, showing they adapt to maintain physical spacing and support biomolecular exchange under different conditions.

The paper uses evolutionary accumulation modelling to infer and forecast shared evolutionary pathways to multi-drug resistance across global pathogens.

The paper develops a method to identify the most parsimonious pathways of binary feature accumulation and convergent evolution, using a hypercubic transition network and cost function minimization.

The paper models how different organelle DNA inheritance strategies balance mutation mitigation and adaptability under varying environmental pressures.

The paper introduces HyperMk2, a method to efficiently model the coevolution of multiple binary characters by reducing state space, enabling analysis of larger systems than previous methods.

This paper uses genomic data from 47k Klebsiella pneumoniae genomes to predict AMR evolutionary pathways across 102 countries, showing how resistance traits vary with geography and drug policy.

The paper uses reaction-diffusion modeling to investigate conditions under which ATP concentration gradients can form in eukaryotic cells, finding that spatial clustering of sources and sinks enables large gradients.

The paper uses hypercubic inference to analyze convergent mitochondrial reduction pathways in eukaryotes, identifying two main evolutionary routes and their metabolic implications.

The paper introduces a clustering-based method to model dynamic accumulation processes in disease progression and antimicrobial resistance evolution using hypercubic hidden Markov models.

This paper introduces a hypercubic Mk model framework to infer accumulation dynamics with reversibility in disease, cancer, and evolutionary contexts using synthetic and real data.

The paper models the tradeoffs of decision-making and feedback control in organisms under changing environments, showing when control mechanisms are most beneficial.

HyperTraPS-CT infers and predicts accumulation pathways in complex systems with flexible data and model structures, demonstrating its application to cancer and tuberculosis resistance.

The paper studies how feeding and starvation affect body size and cellular processes in sea anemones, finding that cell cycle shifts and growth rates are nutritionally regulated.

The paper examines the relationship between mitochondrial and plastid genome reduction across eukaryotic species, finding a generally positive correlation in gene retention but limited evidence for coevolution of specific gene pairs.

The paper investigates ecological predictors of organelle genome evolution by analyzing phylogenetic correlations across sparse, unsystematized data from eukaryotes.

The paper introduces a method to compare transition graphs from different datasets using a metric called WFCC, demonstrating its application to antimicrobial resistance data.

This study uses fine-grained mathematical models to explore how ATP levels affect decision-making capacity in a two-gene regulatory network, showing higher energy supports more cell states.

The paper quantifies how mitochondrial network structure influences mtDNA variability during cell divisions and identifies mechanisms to control heteroplasmy variance.

The paper introduces a maximum likelihood approach to analyze mtDNA heteroplasmy data, showing that traditional statistics can lead to false positives in detecting selection and bottleneck effects.

The paper models how environmental dynamics influence the number of organelle genes retained across species, predicting higher retention in dynamic environments and lower in stable ones.

The paper shows that organelle bottlenecks increase oDNA variability, enabling adaptation to new environments by overcoming the fitness costs of heteroplasmic populations.

This paper introduces HyperHMM, a faster Bayesian method for inferring evolutionary dynamics on hypercubic transition graphs, applied to synthetic and biological datasets.

The paper uses dynamic Boolean modeling to show how ATP variability affects efflux pump gene expression in bacteria, revealing energy-dependent heterogeneity in antimicrobial resistance.

This study shows that intracellular energy variability affects the number and ease of cellular decisions by altering the stability of phenotypes.

This paper introduces a statistical method to infer dynamic pathways of trait acquisition in evolution and disease using HyperTraPS, applied to ovarian cancer and tuberculosis resistance.