Iain G. Johnston

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Author response: Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism

Iain G. Johnston, Joerg P Burgstaller, Vitezslav Havlicek, 2015 .

Toward Precision Healthcare: Context and Mathematical Challenges

Iain G. Johnston, Nick Jones, Caroline Colijn, 2017, Front. Physiol..

Data-Driven Inference Reveals Distinct and Conserved Dynamic Pathways of Tool Use Emergence across Animal Taxa

Iain G. Johnston, Ellen C. Røyrvik, I. Johnston, 2020, iScience.

Supplementary material from "Efficient vasculature investment in tissues can be determined without global information"

Iain G. Johnston, Salva Duran-Nebreda, George W. Bassel, 2020 .

Exploration, exploitation & complexity in biological evolution and self-assembly

Iain G. Johnston, 2010 .

Data-driven modelling and characterisation of task completion sequences in online courses pdf

Iain G. Johnston, Mauricio Barahona, Robert L. Peach, 2020, ArXiv.

HyperTraPS: Inferring Probabilistic Patterns of Trait Acquisition in Evolutionary and Disease Progression Pathways.

Iain G. Johnston, Mauricio Barahona, Sam F. Greenbury, 2019, Cell systems.

Model selection and parameter estimation for root architecture models using likelihood-free inference

Iain G. Johnston, Clare Ziegler, Rosemary J. Dyson, 2019, Journal of the Royal Society Interface.

Efficient vasculature investment in tissues can be determined without global information

Iain G. Johnston, Salva Duran-Nebreda, George W. Bassel, 2020, Journal of the Royal Society Interface.

Inferring high-dimensional pathways of trait acquisition in evolution and disease

Iain G. Johnston, Mauricio Barahona, Sam F. Greenbury, 2018, bioRxiv.

Closed-form stochastic solutions for non-equilibrium dynamics and inheritance of cellular components over many cell divisions

Iain G. Johnston, Nick S. Jones, I. Johnston, 2015, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Mitochondrial Variability as a Source of Extrinsic Cellular Noise

Iain G. Johnston, Nick S. Jones, Tariq Enver, 2011, PLoS Comput. Biol..

Supplementary material from "Model selection and parameter estimation for root architecture models using likelihood-free inference"

Iain G. Johnston, Clare Ziegler, Rosemary J. Dyson, 2019 .

What is quantitative plant biology?

Iain G. Johnston, Dorota Kwiatkowska, Olivier Hamant, 2021, Quantitative Plant Biology.

A tractable genotype–phenotype map modelling the self-assembly of protein quaternary structure

Iain G. Johnston, Sebastian E. Ahnert, Ard A. Louis, 2014, Journal of The Royal Society Interface.

The chaos within: exploring noise in cellular biology

Iain G. Johnston, I. Johnston, 2012 .

Modelling the self-assembly of virus capsids

Iain G. Johnston, J. Doye, A. Louis, 2009, Journal of physics. Condensed matter : an Institute of Physics journal.

Tension and Resolution: Dynamic, Evolving Populations of Organelle Genomes within Plant Cells.

Iain G. Johnston, I. Johnston, 2019, Molecular plant.

Phenotypic landscape inference reveals multiple evolutionary paths to C4 photosynthesis

Iain G. Johnston, I. Johnston, J. Hibberd, 2013, eLife.

Metabolic role of the hepatic valine/3-hydroxyisobutyrate (3-HIB) pathway in fatty liver disease

Iain G. Johnston, I. Johnston, S. Dankel, 2023, EBioMedicine.

Mitochondrial Heterogeneity

Iain G. Johnston, I. Johnston, N. Jones, 2018, Front. Genet..

The ‘mitoflash’ probe cpYFP does not respond to superoxide

Iain G. Johnston, M. Fricker, M. Duchen, 2014, Nature.

Pulsing of Membrane Potential in Individual Mitochondria: A Stress-Induced Mechanism to Regulate Respiratory Bioenergetics in Arabidopsis[W]

Iain G. Johnston, M. Fricker, N. Jones, 2012, Plant Cell.

FRIENDLY Regulates Mitochondrial Distribution, Fusion, and Quality Control in Arabidopsis1[W][OPEN]

Iain G. Johnston, I. Johnston, N. Jones, 2014, Plant Physiology.

Stochastic Models for Evolving Cellular Populations of Mitochondria: Disease, Development, and Ageing

Iain G. Johnston, I. Johnston, N. Jones, 2017 .

Varied Mechanisms and Models for the Varying Mitochondrial Bottleneck

Iain G. Johnston, I. Johnston, 2019, Front. Cell Dev. Biol..

A novel quantitative assay of mitophagy: Combining high content fluorescence microscopy and mitochondrial DNA load to quantify mitophagy and identify novel pharmacological tools against pathogenic heteroplasmic mtDNA.

Iain G. Johnston, I. Johnston, C. Sue, 2015, Pharmacological research.

Stochastic modelling, Bayesian inference, and new in vivo measurements elucidate the debated mtDNA bottleneck mechanism

Iain G. Johnston, Joerg P Burgstaller, Vitezslav Havlicek, 2015, eLife.

What is the function of mitochondrial networks? A theoretical assessment of hypotheses and proposal for future research

Iain G. Johnston, I. Johnston, N. Jones, 2015, BioEssays : news and reviews in molecular, cellular and developmental biology.

using a stable isotope-mass spectrometric technique Measurement of mitochondrial DNA synthesis in vivo

Iain G. Johnston, D. Matthews, M. Kjaer, 2015 .

Intracellular Energy Variability Modulates Cellular Decision-Making Capacity

Iain G. Johnston, I. Johnston, S. Jabbari, 2019, Scientific Reports.

Identification of a bet-hedging network motif generating noise in hormone concentrations and germination propensity in Arabidopsis

Iain G. Johnston, G. Bassel, I. Johnston, 2018, Journal of The Royal Society Interface.

Variability in seeds: biological, ecological, and agricultural implications

Iain G. Johnston, G. Bassel, I. Johnston, 2016, Journal of experimental botany.

Evolutionary inference across eukaryotes identifies universal features shaping organelle gene retention.

Iain G. Johnston, I. Johnston, E. C. Røyrvik, 2022, Cell systems.

Universal features shaping organelle gene retention

Iain G. Johnston, I. Johnston, E. C. Røyrvik, 2021, bioRxiv.

Energetic costs of cellular and therapeutic control of stochastic mtDNA populations

Iain G. Johnston, M. Minczuk, I. Johnston, 2017, bioRxiv.

Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations

Iain G. Johnston, Hanne Hoitzing, Nick S Jones, 2019, PLoS Comput. Biol..

2-Deoxy-D-glucose couples mitochondrial DNA replication with mitochondrial fitness and promotes the selection of wild-type over mutant mitochondrial DNA

Iain G. Johnston, Robert W. Taylor, I. Johnston, 2021, Nature Communications.

MtDNA sequence features associated with ‘selfish genomes’ predict tissue-specific segregation and reversion

Iain G. Johnston, I. Johnston, E. C. Røyrvik, 2020, Nucleic acids research.

Cell identity and nucleo-mitochondrial genetic context modulate OXPHOS performance and determine somatic heteroplasmy dynamics

Iain G. Johnston, S. Priori, I. Johnston, 2020, Science Advances.

Evolving mtDNA populations within cells

Iain G. Johnston, I. Johnston, J. Burgstaller, 2019, Biochemical Society transactions.

Large-scale genetic analysis reveals mammalian mtDNA heteroplasmy dynamics and variance increase through lifetimes and generations

Iain G. Johnston, J. Piálek, T. Rülicke, 2018, Nature Communications.

Evolution of Cell-to-Cell Variability in Stochastic, Controlled, Heteroplasmic mtDNA Populations.

Iain G. Johnston, I. Johnston, N. Jones, 2016, American journal of human genetics.

Evolution of cell-to-cell variability in stochastic, controlled, heteroplasmic mtDNA populations

Iain G. Johnston, I. Johnston, N. Jones, 2016, bioRxiv.

Mitochondrial DNA disease and developmental implications for reproductive strategies

Iain G. Johnston, I. Johnston, J. Poulton, 2014, Molecular human reproduction.

MtDNA segregation in heteroplasmic tissues is common in vivo and modulated by haplotype differences and developmental stage.

Iain G. Johnston, A. Futschik, J. Albrechtová, 2014, Cell reports.

The self-assembly of DNA Holliday junctions studied with a minimal model.

Iain G. Johnston, J. Doye, A. Louis, 2008, The Journal of chemical physics.

Sexually antagonistic evolution of mitochondrial and nuclear linkage

Iain G. Johnston, I. Johnston, M. Hall, 2021, bioRxiv.

Paternal leakage of organelles can improve adaptation to changing environments

Iain G. Johnston, I. Johnston, A. Radzvilavicius, 2020, bioRxiv.

Sorting of mitochondrial and plastid heteroplasmy in Arabidopsis is extremely rapid and depends on MSH1 activity

Iain G. Johnston, Daniel B. Sloan, I. Johnston, 2022, bioRxiv.

Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution

Iain G. Johnston, Chico Q. Camargo, Sam F. Greenbury, 2021, bioRxiv.

The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey

Iain G. Johnston, A. Cook, H. Larson, 2016, EBioMedicine.

The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks.

Iain G. Johnston, Sam F. Greenbury, J. Doye, 2010, Journal of theoretical biology.

Mitochondrial heterogeneity, metabolic scaling and cell death

Iain G. Johnston, I. Johnston, N. Jones, 2017, BioEssays : news and reviews in molecular, cellular and developmental biology.

Data science approaches provide a roadmap to understanding the role of abscisic acid in defence

Iain G. Johnston, I. Johnston, Estrella Luna, 2022, bioRxiv.

Quantification and uncertainty of root growth stimulation by elevated CO2 in mature temperate deciduous forest

Iain G. Johnston, I. Johnston, R. Dyson, 2021, bioRxiv.

The Essential Genome of Escherichia coli K-12

Iain G. Johnston, I. Henderson, A. Cunningham, 2017, mBio.

Programme - Day 1

Iain G. Johnston, Sam F. Greenbury, Marjon G. J. de Vos, 2021 .

Comparing structure and dynamics of transition graphs by the symmetric difference metric over an edge-filtration

Iain G. Johnston, Lars M. Salbu, Belén García Pascual, 2024, bioRxiv.

Mitochondrial network structure controls cell-to-cell mtDNA variability generated by cell divisions

Iain G. Johnston, I. Johnston, Robert C. Glastad, 2022, bioRxiv.

Network analysis of Arabidopsis mitochondrial dynamics reveals a resolved tradeoff between physical distribution and social connectivity

Iain G. Johnston, G. Bassel, D. Gibbs, 2021, Cell systems.

Mitochondrial Network State Scales mtDNA Genetic Dynamics

Iain G. Johnston, N. Jones, J. Aryaman, 2018, Genetics.

Mitochondrial Network State Scales mDNA Genetic Dynamics

Iain G. Johnston, N. Jones, J. Aryaman, 2019 .

Mitochondrial network state scales mtDNA genetic dynamics

Iain G. Johnston, I. Johnston, N. Jones, 2019 .

Mitochondrial network fragmentation modulates mutant mtDNA accumulation independently of absolute fission-fusion rates

Iain G. Johnston, I. Johnston, N. Jones, 2018 .

Collective mitochondrial dynamics resolve conflicting cellular tensions: From plants to general principles.

Iain G. Johnston, Joanna M. Chustecki, 2024, Seminars in cell & developmental biology.

Exchange on dynamic encounter networks allows plant mitochondria to collect complete sets of mitochondrial DNA products despite their incomplete genomes

Iain G. Johnston, I. Johnston, Konstantinos Giannakis, 2022, Quantitative Plant Biology.

Encounter networks from collective mitochondrial dynamics support the emergence of effective mtDNA genomes in plant cells

Iain G. Johnston, I. Johnston, Konstantinos Giannakis, 2021, bioRxiv.

Mitochondrial DNA density homeostasis accounts for a threshold effect in a cybrid model of a human mitochondrial disease

Iain G. Johnston, I. Johnston, N. Jones, 2017, The Biochemical journal.

Temperature variability is integrated by a spatially embedded decision-making center to break dormancy in Arabidopsis seeds

Iain G. Johnston, Eiji Nambara, G. Bassel, 2017, Proceedings of the National Academy of Sciences.

Evolutionary Inference across Eukaryotes Identifies Specific Pressures Favoring Mitochondrial Gene Retention.

Iain G. Johnston, I. Johnston, Ben P. Williams, 2016, Cell systems.

Evolutionary inference across eukaryotes identifies specific pressures favoring mtDNA gene retention

Iain G. Johnston, I. Johnston, Ben P. Williams, 2016, bioRxiv.

Modulating mitochondrial quality in disease transmission: towards enabling mitochondrial DNA disease carriers to have healthy children

Iain G. Johnston, I. Johnston, D. Wells, 2016, Biochemical Society transactions.

HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs

Iain G. Johnston, I. Johnston, Marcus T. Moen, 2022, bioRxiv.

Global Topological Order Emerges through Local Mechanical Control of Cell Divisions in the Arabidopsis Shoot Apical Meristem

Iain G. Johnston, G. Bassel, Matthew D B Jackson, 2019, Cell systems.

Organelle bottlenecks facilitate evolvability by traversing heteroplasmic fitness valleys

Iain G. Johnston, I. Johnston, A. Radzvilavicius, 2021, bioRxiv.

Regulation of Mother-to-Offspring Transmission of mtDNA Heteroplasmy

Iain G. Johnston, I. Johnston, N. Jones, 2019, Cell metabolism.

S100A4 mRNA-protein relationship uncovered by measurement noise reduction

Iain G. Johnston, I. Johnston, T. Nussbaumer, 2020, Journal of Molecular Medicine.

Altered collective mitochondrial dynamics in the Arabidopsis msh1 mutant compromising organelle DNA maintenance

Iain G. Johnston, D. Gibbs, I. Johnston, 2021, bioRxiv.

HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures

Iain G. Johnston, K. Dauda, Konstantinos Giannakis, 2024, bioRxiv.

Avoiding misleading estimates using mtDNA heteroplasmy statistics to study bottleneck size and selection

Iain G. Johnston, Daniel B. Sloan, I. Johnston, 2022, bioRxiv.

Connecting Species-Specific Extents of Genome Reduction in Mitochondria and Plastids

Iain G. Johnston, Konstantinos Giannakis, Luke Richards, 2024, bioRxiv.

Cellular and environmental dynamics influence species-specific extents of organelle gene retention

Iain G. Johnston, J. Nordbotten, I. Johnston, 2022, bioRxiv.

Multiple hypothesis correction is vital and undermines reported mtDNA links to diseases including AIDS, cancer, and Huntingdon’s

Iain G. Johnston, 2015, bioRxiv.

Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years.

Iain G. Johnston, H. Larson, I. Johnston, 2016, The Lancet. Global health.

Explicit Tracking of Uncertainty Increases the Power of Quantitative Rule-of-Thumb Reasoning in Cell Biology

Iain G. Johnston, N. Jones, Benjamin C Rickett, 2014, Biophysical journal.

ATP dependence of decision-making capacity in a fine-grained model of gene regulatory networks

Iain G. Johnston, Rajneesh Kumar, 2024, Journal of the Royal Society Interface.

Fine-grained modelling of ATP dependence of decision-making capacity in genetic regulatory networks

Iain G. Johnston, Rajneesh Kumar, 2023, bioRxiv.

Dynamic Boolean modelling reveals the influence of energy supply on bacterial efflux pump expression

Iain G. Johnston, I. Johnston, S. Jabbari, 2021, bioRxiv.