Tootfinder

A nonparametric approach to practical identifiability of nonlinear mixed effects models
Tyler Cassidy, Stuart T. Johnston, Michael Plank, Imke Botha, Jennifer A. Flegg, Ryan J. Murphy, Sara Hamis
https://arxiv.org/abs/2507.20288

A nonparametric approach to practical identifiability of nonlinear mixed effects models
Mathematical modelling is a widely used approach to understand and interpret clinical trial data. This modelling typically involves fitting mechanistic mathematical models to data from individual trial participants. Despite the widespread adoption of this individual-based fitting, it is becoming increasingly common to take a hierarchical approach to parameter estimation, where modellers characterize the population parameter distributions, rather than considering each individual independently. T…

Initial VLT/MUSE spectroscopy of the interstellar object 3I/ATLAS
Cyrielle Opitom, Colin Snodgrass, Emmanuel Jehin, Michele T. Bannister, Erica Bufanda, Sophie E. Deam, Rosemary Dorsey, Marin Ferrais, Said Hmiddouch, Matthew M. Knight, Rosita Kokotanekova, Brayden Leicester, Micha\"el Marsset, Brian Murphy, Vincent Okoth, Ryan Ridden-Harper, Mathieu Vander Donckt, L\'ea Ferellec, Damien Hutsemekers, Manuela Lippi, Jean Manfroid, Zouhair Benkhaldoun

Initial VLT/MUSE spectroscopy of the interstellar object 3I/ATLAS
The interstellar comet 3I/ATLAS is only the third interstellar object to be discovered. Pre-perihelion measurements provide a unique opportunity to study its activity and composition, which may alter as it is heated in the coming months. We provide an initial baseline from optical spectroscopic observations obtained only two days after discovery, using the MUSE instrument on the VLT on 2025 July 3, while 3I was at 4.47 au from the Sun and 3.46 au from the Earth. These observations confirm the c…

A cautionary tale of model misspecification and identifiability
Alexander P Browning, Jennifer A Flegg, Ryan J Murphy
https://arxiv.org/abs/2507.04894 http…

A cautionary tale of model misspecification and identifiability
Mathematical models are routinely applied to interpret biological data, with common goals that include both prediction and parameter estimation. A challenge in mathematical biology, in particular, is that models are often complex and non-identifiable, while data are limited. Rectifying identifiability through simplification can seemingly yield more precise parameter estimates, albeit, as we explore in this perspective, at the potentially catastrophic cost of introducing model misspecification a…