Mathematical Biology podcasts

Gaute Einevoll talks with Tatjana Tchumatchenko about a mechanistic mathematical model of how neurons minimize energy use by deciding whether ion-channel proteins are produced locally in dendrites or in the soma. The model’s predictions agree with experimental findings.

Steven Niederer and Richard Wilkinson discuss personalised mathematical models of patients’ hearts and their potential use in care. The conversation connects digital twins and the CVDNet project with research on representing, calibrating and using uncertainty in predictions. It also traces how this work develops ideas from the earlier Fickle Heart research programme.

Nicolas Brunel discusses excitation-inhibition balance and mathematical explanations of irregular activity in cortical networks.

Liz Fearon describes a project that involves members of the public as equal participants in creating mathematical models. The discussion asks how models used in public-health policy and other decisions can reflect lived experience, social interactions and community priorities. It explores the motivation, working methods and aims of co-production in epidemiological modelling.

L. Mahadevan investigates how geometry, mechanics and randomness connect form with function. With Steven Strogatz, he considers Möbius strips, brain folds, termite colonies and morphogenesis, showing how experiments with simple materials can illuminate biological organization and the role of noisy processes in geometric intuition.

Hosted by Steven Strogatz, Janna Levin

Gaute Einevoll reflects on his academic career and explains his research in mathematical neuroscience, brain physics and neurotechnology. Anniversary solo episode in Norwegian.

Franziska Michor explains how evolutionary dynamics, statistics and mathematical models can inform cancer treatment. The conversation examines ways to optimize treatment choices, combinations and timing while accounting for tumour complexity and the continual evolution of cancer cells.

Hosted by Steven Strogatz

Eric Shea-Brown joins Paul Middlebrooks to examine what dynamical systems and dimensionality reveal about neural computation. They compare task-related activity with less constrained behaviour, consider how network connection motifs shape collective dynamics, and discuss high-dimensional activity embedded within lower-dimensional descriptions. Research examples connect predictive learning, latent representations and rich versus lazy learning regimes. The conversation also examines how theoretical neuroscientists choose useful abstractions and which assumptions about network structure and function deserve closer scrutiny.

Hosted by Paul Middlebrooks

Carina Curto discusses topology and mathematically tractable models of neural networks. The conversation connects population-level neural geometry with combinatorial linear-threshold networks, examining how graph structure constrains dynamics, how motifs retain properties inside larger systems, and why simpler models may help reveal principles obscured in large neural simulations.

Hosted by Paul Middlebrooks