Foreword
Keating, J Oxford's Sedleian Professors of Natural Philosophy: The First 400 Years v-vi (14 Dec 2023)
Error Bounds for Flow Matching Methods
Benton, J Deligiannidis, G Doucet, A Transactions on Machine Learning Research volume 2024 (01 Jan 2024)
Generalisation under gradient descent via deterministic PAC-Bayes
Clerico, E Farghly, T Deligiannidis, G Guedj, B Doucet, A Proceedings of Machine Learning Research volume 272 349-389 (01 Jan 2025)
Bubble racing in a Hele-Shaw cell
Booth, D Wu, K Griffiths, I Howell, P Nunes, J Stone, H Journal of Fluid Mechanics volume 1010 (16 May 2025)
Carrollian amplitudes from holographic correlators
Alday, L Nocchi, M Ruzziconi, R Yelleshpur Srikant, A Journal of High Energy Physics volume 2025 issue 3 (20 Mar 2025)
Fri, 23 May 2025

11:00 - 12:00
L2

Modelling infectious diseases within-host

Dr Ruth Bowness
(Dept. Maths Science, University of Bath)
Abstract

During the talk I will describe my research on host-pathogen interactions during lung infections. Various modelling approaches have been used, including a hybrid multiscale individual-based model that we have developed, which simulates pulmonary infection spread, immune response and treatment within in a section of human lung. The model contains discrete agents which model the spatio-temporal interactions (migration, binding, killing etc.) of the pathogen and immune cells. Cytokine and oxygen dynamics are also included, as well as Pharmacokinetic/Pharmacodynamic models, which are incorporated via PDEs. I will also describe ongoing work to develop a continuum model, comparing the spatial dynamics resulting from these different modelling approaches.  I will focus in the most part on two infectious diseases: Tuberculosis and COVID-19.

ALE Spaces and Nodal Curves
Hitchin, N The Quarterly Journal of Mathematics volume 76 issue 1 337-347 (17 Feb 2025)
Fri, 16 May 2025

11:00 - 12:00
L4

Round the clock: circadian gene expression, growth and division in cyanobacteria

Dr Bruno Martins
(School of Life Sciences, University of Warwick)
Abstract

Circadian clocks generate autonomous daily rhythms of gene expression in anticipation of daily sunlight and temperature cycles in a variety of organisms. The simples and best characterised of all circadian clocks in nature is the cyanobacterial clock, the core of which consists of just 3 proteins - KaiA, KaiB and KaiC - locked in a 24-h phosphorylation-dephosphorylation loop. Substantial progress has been made in understanding how cells generate and sustain this rhythm, but important questions remain: how does the clock maintain resilience in the face of internal and external fluctuations, how is the clock coupled to other cellular processes and what dynamics arise from this coupling? We address these questions using an interdisciplinary approach combining time-lapse microscopy and modelling. In this talk, I will first characterise the clock's free-running robustness and explore how the clock buffers environmental noise and genetic mutations. Our stochastic model predicts how the clock filters out such noise, including fast light fluctuations, to keep time while remaining responsive to environmental shifts, revealing also that the wild-type operates at a noise optimum. Next, I will focus on how the clock interacts with the other major cellular cycle, the cell division cycle. Our single-cell data shows that the clock couples to the division rate and expression of cell cycle-dependent factors using both frequency modulation and amplitude modulation strategies, with implications for cell growth and cell size control. Our findings illustrate how simple systems can exhibit complex dynamics, advancing our understanding of the interdependency between gene circuits and cellular physiology.  
 

Fri, 09 May 2025

11:00 - 12:00
L4

5 years after COVID: what did modellers get right and wrong?

Professor Matt Keeling
(Dept of Mathematics University of Warwick)
Abstract
The COVID-19 pandemic represented a major challenge to many sectors of society. It also provided the opportunity for epidemiological modellers to prove their worth. Much of the modelling was performed to extremely tight deadlines and was underpinned by noisy and often biased data. 
5 years on, and with the benefit of hindsight, I’ll present a personal perspective of what went well, what went badly and lessons for next time. I’ll cover many aspects, but pay particular attention to vaccination, roadmaps, Omicron and building collaborative networks. 


 

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