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The Zeeman Institute

The Zeeman Institute

Biological Understanding

Fundamental to our work is the development of novel understanding of biological and ecological phenomona, ranging from within cellular processes to large spatial-temporal patterns in dryland plants. Our approaches include phenomenogical models as well as systems biology. Both combine powerful statistical and mathematical tools with cutting edge experimental work to understand the rich and complex dynamics that occur within a biological and ecological systems.

Till Bretschneider

  • Systems cell biology
  • Cell motility and the cytoskeleton
  • Image-based modelling of cellular dynamics
  • Applied machine learning and automated 3D microscopy

Nigel Burroughs

  • Human cell division
  • Human egg development and the first cell divisions
  • Cancer therapy optimisation using PMP, HJB and dynamic programming; potentially in AML, lung cancer
  • Predicting recurrent miscarriage
  • Model inference from imaging data, eg PDEs and stochastic PDEs

Radu Cimpeanu

  • interfacial flows
  • particle and drop dynamics for agricultural sprays
  • bioreactors and cultivated meat modelling
  • microfluidics and lab-on-a-chip devices
  • flow-based needle design

Robert Dallmann

  • Circadian systems biology
  • Chronotherapy
  • Personalised medicine

Louise Dyson

  • Sources of variability of mRNA expression in cell populations
  • Noise-induced bistable states in biological systems
  • Cranial neural crest cell migration in the developing embryo

Lukas Eigentler

  • dryland vegetation patterns
  • bacterial biofilms
  • evolution of individual variability
  • competition and coexistence

Richard Everitt

  • Inference for models in statistical genetics, particularly for pathogens
  • Inference for environmental and ecological models, including individual-based models
  • Inference for models in neuroscience
  • Stock assessment for fisheries

Miriam Gifford

  • Plant systems biology
  • Gene regulatory network inference
  • Modelling plant-microbe interactions

Sara Kalvala

  • Agent-based modeling of microbial communities
  • Compiler techniques in synthetic biology
  • Logic and semantics-based approaches in cell biology

Tom Montenegro-Johnson

  • Biological fluid dynamics
  • Responsive Hydrogels
  • Microbots and Soft Matter
  • Mathematics of Touch

David Rand

  • Developmental biology
  • Circadian clocks in health and disease (notably cancer)

Massimiliano Tamborrino

  • mathematical modelling in neuroscience and biology;
  • stochastic numerics for dynamical systems;
  • inference for neuronal models;
  • probabilistic parallel-in-time numerical schemes for complex ODEs/PDEs.