This course focuses on identifiability and inference in cell biology research seminars. The learning outcomes include understanding the challenges of interpreting quantitative data, developing biologically realistic models, and calibrating models to data efficiently. The course teaches skills such as parameter inference, model building, identifiability analysis, and model comparison. The teaching method involves showcasing how quantitative comparisons between models and data can reveal biological mechanisms. The intended audience includes researchers, mathematicians, biologists, and anyone interested in cell biology and mathematical modeling.
Overview
Syllabus
Intro
Research focus
Challenges of quantitative data
The inverse problem
Drosophila egg chamber
Compartment-based model
Typical behaviour of the solution
Parameter inference
RNA complex assembly
Quasi-steady distribution
Transport strongly biased
Dynamic regime
Over-expression mutant
Model predictions
Robustness of accumulation
Other mechanisms at play? . Could be a range of mechanisms at play that engender robustness to the system
Model comparison
Experimental data
Model building and identifiability
Mathematical models
Identifiability analysis Approach
MCMC results - Model 1
Profile likelihood analysis
Take-home message
Summary
Acknowledgements
Taught by
Oxford Mathematics
