Day 1
| Time | Topic | Speaker | Category |
|---|---|---|---|
| 09:00 – 10:00 | Course Overview and Breakfast Meet & Greet (icebreaker activity) | Alejandro Maass, Luis Zapata | Welcome |
| 10:00 – 11:00 | Intro to mathematical modelling and data science in cancer evolution, covering cancer as an evolutionary process, motivation for studying cancer evolution, and examples such as adaptive therapy and driver gene identification. | Luis Zapata | Lecture |
| 11:00 – 12:30 | Data science in cancer evolution, including large-scale multi-omics analysis and mathematical principles for predictive modelling. | Luis Zapata | Lecture |
| 12:30 – 13:30 | Lunch | Break | |
| 13:30 – 15:00 | Introduction to mathematical modelling of cancer evolution using ordinary differential equations (ODEs), including tumour growth models, phenotype transitions, and tumour–immune interactions. | Chloe Colson | Lecture |
| 15:00 – 15:30 | Coffee break | Break | |
| 15:30 – 17:00 | Practical session on simulating ODE models in R/Python, focusing on qualitative behaviour and steady-state analysis. | Chloe Colson | Practical |
| 17:00 – 18:00 | Group project and presentation assignment, including research paper discussion and participant research presentations. | Luis Zapata | Practical |
Day 2
| Time | Topic | Speaker | Category |
|---|---|---|---|
| 09:00 – 10:30 | Discrete stochastic models of cancer evolution, including branching/birth–death processes for genetic evolution (mutations and selection) and stochastic models for phenotypic evolution. | Chloe Colson | Lecture |
| 10:30 – 11:00 | Coffee break | Break | |
| 11:00 – 12:30 | Practical on stochastic genetic evolution models: simulation under neutrality vs driver mutations, VAF spectrum visualisation, and mutation-rate estimation from VAF distributions. | Chloe Colson | Practical |
| 12:30 – 13:30 | Lunch | Break | |
| 13:30 – 15:00 | Statistical inference of parameters, covering maximum likelihood estimation and Bayesian inference (likelihood- and simulation-based approaches). | Giulio Caravagna | Lecture |
| 15:00 – 15:30 | Coffee break | Break | |
| 15:30 – 17:00 | Practical: parameter inference case studies, including estimating mutation and growth rates from single timepoint snapshots and from longitudinal data. | Giulio Caravagna | Practical |
| 17:00 – 18:00 | Keynote talk I | Alejandro Maass / Steffen |
Day 3
| Time | Topic | Speaker | Category |
|---|---|---|---|
| 09:00 – 10:30 | Measuring selection in cancer genomes, including genomic signals of positive/negative selection and Bayesian/frequentist approaches for quantifying selection. | Luis Zapata | Lecture |
| 10:30 – 11:00 | Coffee break | Break | |
| 11:00 – 12:30 | Practical: measuring selection in cancer genomes, including driver/essential gene identification using functional impact, recurrence, and dN/dS. | Luis Zapata | Practical |
| 12:30 – 13:30 | Lunch | Break | |
| 13:30 – 15:00 | Subclonal deconvolution of tumours: estimation of allele frequencies, purity, and copy number as inputs for subclonal reconstruction. | Giulio Caravagna | Lecture |
| 15:30 – 16:00 | Coffee break | Break | |
| 15:30 – 17:00 | Practical: subclonal deconvolution tools and workflows, including approaches for subclonal evolution and neutrality testing (e.g., MOBSTER, Battenberg, PyClone). | Giulio Caravagna | Practical |
Day 4
| Time | Topic | Speaker | Category |
|---|---|---|---|
| 09:00 – 10:30 | Machine learning for tumour profiling: unsupervised analysis to uncover subtypes, multi-omic integration with MOFA to expose latent axes, and phenotype prediction using PARETO. | Alex Di Genova | Lecture |
| 10:30 – 11:00 | Coffee break | Break | |
| 11:00 – 12:30 | Practical: machine learning for tumour profiling, including hands-on applications (Google Colab). | Alex Di Genova | Practical |
| 12:30 – 13:30 | Lunch | Break | |
| 13:30 – 15:00 | Introduction to mathematical modelling of cancer evolution using partial differential equations (PDEs), including tumour cell growth/movement and phenotype evolution along continuous state spaces. | Chloé Colson | Lecture |
| 15:00 – 15:30 | Coffee break | Break | |
| 15:30 – 17:00 | Practical: visualising PDE model simulations. | Chloé Colson | Practical |
| 17:00 – 18:00 | Keynote talk III | ||
| 18:00 – late | Free evening |
Day 5
| Time | Topic | Speaker | Category |
|---|---|---|---|
| 09:00 – 10:30 | Continue: applications of data science and genomics for cancer treatment. | Luis Zapata | Lecture |
| 10:30 – 11:00 | Coffee break | Break | |
| 11:00 – 12:30 | Continue: machine learning in genomics. | Alex Di Genova | Practical |
| 12:30 – 13:30 | Lunch | Break | |
| 13:30 – 15:00 | Flash talks: participants’ own research. | Practical | |
| 15:00 – 15:30 | Coffee break | Break | |
| 15:30 – 17:00 | Flash talks: research papers. | Practical | |
| 17:00 – 18:00 | Wrap-up discussion and course debrief. |