Computational Biology podcasts

Neurobiologist Tom Clandinin joins Nicholas Weiler to explore how interacting genetic, cellular and metabolic systems maintain brain health over a lifetime, and how their breakdown contributes to neurodegeneration. The discussion moves beyond single explanations such as inflammation or waste clearance to a systems-level study of brain resilience. Clandinin describes a collaborative programme combining large-scale genetic perturbations in flies and mice with molecular, circuit and behavioural measurements. Computational models will connect these observations across cell types, predict interactions and guide experiments toward potential ways to strengthen the aging brain’s maintenance systems.

Hosted by Nicholas Weiler

Rafael Rosengarten and BioPharmaTrend co-founder Andrii Buvailo discuss how to assess progress in AI-assisted drug discovery. They trace the move from individual computational tasks to connected research workflows and question whether counting AI-associated drug candidates captures the technology’s value. Topics include research productivity, trial design, biomarkers, human expertise and the use of patient-derived data, organoids and other representative biological systems to improve translation. The conversation also considers why AI has become polarising and how specialised models may fit into pharmaceutical research.

Vincent Racaniello, Kathy Spindler and Brianne Barker discuss research using generative AI to design complete bacteriophage genomes, including reported viable phages active against antibiotic-resistant bacteria. They also examine a study linking recombinant shingles vaccination with lower cardiovascular-event risk. The discussion connects genome modelling, experimental virology and vaccine research, alongside listener letters and weekly selections.

Hosted by Vincent Racaniello, Brianne Barker

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.

Daphne Koller of insitro discusses the differences between finding predictive patterns and identifying interventions that improve disease outcomes. The conversation examines biological data scarcity, structure and causality, failures in drug development and the limits of speeding up wet-lab work with AI agents. It also considers foundation models for biology, generalization beyond observed data, human evidence and scientific discovery. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Jure Leskovec connects data-driven biological modelling with relational deep learning. Topics include multiscale virtual-cell models, treating databases as graphs and using in-context prediction across multiple tables. The discussion also covers benchmark evaluation, explanations, agent integration and the practical limitations of relational foundation models.

Hosted by Sam Charrington

Mariana Kneppers, Mason Wakley and Neil Withers discuss two questions about genetic information. They examine AlphaGenome, a machine learning model designed to predict molecular effects of DNA variants across long sequences, and consider its potential use in genetics research. The episode then turns to the RNA world hypothesis: the proposal that RNA-based systems preceded DNA genomes and cells during the early history of life. The discussion links contemporary tools for studying the genome with chemical questions about life's beginnings.

Hosted by Mariana Kneppers

Polina Shichkova discusses models of brain metabolism, including energy supply and the cellular processes that maintain neuronal function.

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.

Markus Covert discusses comprehensive bacterial cell simulations and what whole-cell modelling could contribute to future studies of neurons.

Heather Harrington explains topological data analysis and the construction of barcodes that summarize the shape of a dataset across scales. She describes how these tools reveal structure in complex information and how they can support studies of proteins and diseases such as cancer. The interview was recorded at the European Congress of Mathematics.

Surya Ganguli and Bowen Liu join Vijay Pande to examine how machine learning can support biological discovery and drug development. They compare physics-based modelling, learned molecular representations and self-supervised foundation models; discuss protein structures, target identification and generative molecule design; and explain why experimental validation and generalisation beyond training data remain difficult. The conversation also considers interpretable cellular representations and the potential role of AI in clinical-trial recruitment and prediction. The publisher provides an accompanying transcript.

Hosted by Vijay Pande

Michael Hines discusses the development of NEURON and its use in simulating biophysically detailed cells and networks.

Avrama Blackwell discusses computational models of chemical signalling inside neurons and their relationship to cellular information processing.