Research Seminar - Identifiability and Inference in Cell Biology

Explore cutting-edge research in mathematical biology and cell biology through this 51-minute research seminar by Oxford mathematician Ruth Baker. Delve into the challenges of developing biologically realistic models to interpret the vast amounts of quantitative data generated by new experimental technologies. Learn how quantitative comparisons between models and data can reveal subtle details of biological mechanisms. Discover steps taken to tackle mathematical challenges in developing identifiable models that can be efficiently calibrated to quantitative data. Examine case studies on Drosophila egg chambers and RNA complex assembly, exploring compartment-based models, parameter inference, and model comparison techniques. Gain insights into identifiability analysis, MCMC results, and profile likelihood analysis. Understand the importance of model building, robustness, and the potential for multiple mechanisms in biological systems. This seminar provides a comprehensive overview of current research in mathematical biology, offering valuable insights for researchers and students in the field.

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