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Improving Parameter Inference from FRAP Data: an Analysis Motivated by Pattern Formation in the Drosophila Wing Disc
Lin Lin1,2, Hans G Othmer3
1Department of Biomedical Engineering, School of Mathematics, University of Minnesota, Minneapolis, MN, 55455, USA. linxx724@umn.edu.
Bulletin of Mathematical Biology
|January 20, 2017
Summary
This study introduces a novel approach to reconcile discrepancies in molecular diffusion and binding kinetics estimated using different Fluorescence Recovery After Photobleaching (FRAP) models. It highlights how simulation, observation time, and spatial data improve parameter accuracy.
Area of Science:
- Biophysics
- Cell Biology
- Quantitative Biology
Background:
- Fluorescence Recovery After Photobleaching (FRAP) provides quantitative insights into molecular diffusion and binding kinetics.
- Existing FRAP models present challenges in understanding parameter estimate connections, model assumption validity, and estimate quality.
Purpose of the Study:
- To develop a new approach for investigating discrepancies in parameter estimates derived from different FRAP models.
- To establish relationships between parameters from theoretical and recovery models and improve FRAP model formulation.
Main Methods:
- Utilized a theoretical model to simulate FRAP experiment dynamics and generate data for various recovery models.
- Investigated the impact of observation time, bleaching region size, and spatial information on parameter estimation quality.
- Employed sensitivity analysis to assess model complexity and detect over-fitting.
Main Results:
- Appropriate observation time significantly enhances estimate quality, particularly when diffusion and binding kinetics are unbalanced.
- Varying bleaching region size provides a priori knowledge crucial for model formulation.
- Spatial information in FRAP improves parameter estimation, and simplified models can suffice under certain conditions.
Conclusions:
- The proposed simulation-based approach clarifies relationships between parameters from different FRAP models.
- Sensitivity analysis is a valuable tool for optimizing FRAP model formulation and identifying over-fitting issues.
- Understanding model assumptions and data characteristics is key to accurate quantitative analysis of molecular dynamics.
