Iain's research group is interested in combining mathematical, statistical, and experimental approaches to learn about the biological world, and how we can interact with it to improve human lives. We often work on questions where "blue skies" biological knowledge can be translated into strategies to address disease and crop production. We're particularly interested in the bioenergetic organelles (mitochondria and chloroplasts) that provide the energy that powers complex life.
One major current focus of the group is EvoConBiO -- an ERC-funded project exploring the evolution and cellular control of mitochondria and chloroplasts, using approaches from stochastic modelling, bioinformatics, and molecular biology.
Some keywords related to our research -- mathematical: stochastic modelling, discrete population processes, inference and model selection, uncertainty quantification, pathway inference; biological: stochastic biology, cell heterogeneity, mitochondria, chloroplasts, mtDNA, cpDNA, evolution, disease progression, bioinformatics, plant systems biology.
The CRIStin profile below is currently being updated.
- 2020. Data-Driven Inference Reveals Distinct and Conserved Dynamic Pathways of Tool Use Emergence across Animal Taxa. iScience.
- 2019. Varied mechanisms and models for the varying mitochondrial bottleneck. Frontiers in Cell and Developmental Biology. 1-16.
- 2019. Tension and resolution: dynamic, evolving populations of organelle genomes within plant cells. Molecular Plant. 764.
- 2019. Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data. npj Digital Medicine. 1.
- 2019. Model selection and parameter estimation for root architecture models using likelihood-free inference. Journal of the Royal Society Interface. 1-10.
- 2019. Mitochondrial network state scales mtDNA genetic dynamics. Genetics. 1429-1443.
- 2019. Intracellular Energy Variability Modulates Cellular Decision-Making Capacity. Scientific Reports. 1-12.
- 2019. HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways. Cell Systems. 1.
- 2019. Evolving mtDNA populations within cells. Biochemical Society Transactions. 1367.
- 2019. Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations. PLoS Computational Biology. e1007023.
- 2018. Identification of a bet-hedging network motif generating noise in hormone concentrations and germination propensity in Arabidopsis. Journal of the Royal Society Interface. 20180042.
EvoConBiO (ERC StG, Iain PI) -- evolution and control of bioenergetic organelles