Topic: mechanistic models

This symposium connects academic and industrial work on automated chemistry, from robotic solid/liquid handling and flow chemistry to reaction monitoring, purification, algorithms and experiment design. Scientific sessions examine automated chemistry data workflows, agentic high-throughput synthesis, kinetic measurements and mechanistic models, data-driven purification and AI-assisted R&D. It serves researchers, students and industrial chemists using automation in organic, medicinal and process chemistry. The 24 November programme has registration at 10:00, opening remarks at 10:30, scient

Build and evaluate short-term ecological forecasts by repeatedly linking observations, models and predictions. A biodiversity challenge uses shrub, rabbit and dung-beetle data from Doñana with remote-sensing measurements. Participants process time series and satellite indices, compare autoregressive, hierarchical, correlational and mechanistic models, propagate uncertainty and assess forecasts against benchmarks. Reproducible R workflows lead to a submitted forecast and discussion of how predictions support conservation and adaptive management. Live sessions: 25–29 January, 14:00–18:30 daily,

Modern experimental techniques now reveal the structure and function of neural circuits at unprecedented scale and resolution. How can we use this wealth of data to understand how cells and circuits implement computations underlying behaviour? Achieving this goal requires models that are consistent with biophysical mechanisms and circuit dynamics, yet flexible enough to capture behaviourally relevant computations. We develop simulation-based machine learning methods that address this challenge. I will show how these approaches—in combination with connectomic measurements—make it possible to bu

I’ll give an overview of internal - or motivational - states in larval zebrafish. Specifically we will focus on the role of the Oxytocin system in regulating the detection of, and behavioral responses to, conspecifics. The appeal here is that Oxytocin has likely conserved roles across all vertebrates, including humans, and that the larval zebrafish allows us to study some of the general principles across the brain but nonetheless at cellular resolution. This allows us to propose mechanistic models of emotional states.

The SARS-CoV-2 pandemic is awash in data, including daily, spatially-resolved COVID case data, virus sequence data, patients `omics data, and mobility data. Journals are now also awash in studies that make use of quantitative modeling approaches to gain insight into the geographic spread of SARS-CoV-2 and its temporal dynamics, as well as studies that predict the impact of control strategies on SARS-CoV-2 circulation. Some, but by no means all, of these studies are informed by the massive amounts of available data. Some, but by no means all, of these studies have been useful — in that their pr

Experimental techniques now make it possible to measure the structure and function of neural circuits at an unprecedented scale and resolution. How can we leverage this wealth of data to understand how neural circuits perform computations underlying behaviour? A mechanistic understanding will require models that align with experimental measurements and biophysical mechanisms, while also being capable of performing behaviorally relevant computations. Building such models has remained a central challenge. I will present our work on addressing this challenge. We have developed machine learning me

Neural population dynamics are jointly shaped by numerous factors at the cellular, synaptic, and network levels. Mechanistic models, especially spiking neural networks, are crucial for understanding how these interacting variables influence neural activity. However, the process of adjusting model parameters to match experimental recordings, i.e., inverse modeling, is challenging, and often requires a trade-off between model complexity and tractability. Consequently, models often use fixed or manually tuned parameters, limiting their dynamic range and explanatory power while making cross-study