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The impact of temporal sampling resolution on parameter inference for biological transport models
Jonathan U Harrison1, Ruth E Baker1
1Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom.
Plos Computational Biology
|June 26, 2018
Summary
Understanding biological transport requires accurate parameter estimation from imaging data. This study reveals how temporal sampling resolution and measurement noise impact model parameter inference, offering experimental guidelines for motile behavior characterization.
Area of Science:
- * Biophysics and quantitative biology.
- * Computational modeling and data analysis.
Background:
- * Imaging data is crucial for studying biological processes like bacterial motility and mRNA transport.
- * Quantitative characterization of biological behavior requires mathematical models and parameter estimation.
- * Inferring model parameters from imaging data is essential for comparing biological species or mutants.
Purpose of the Study:
- * To investigate the impact of temporal sampling resolution on parameter inference for biological transport models.
- * To develop a methodology for estimating parameters of velocity jump process models from noisy imaging data.
- * To provide experimental guidelines for optimizing data collection in motile behavior studies.
Main Methods:
- * Employed exact inference within a Bayesian framework for simple velocity jump process models.
- * Utilized a hidden states framework to mitigate errors from discrete observations.
- * Analyzed the sensitivity of parameter estimates to sampling resolution and measurement noise.
Main Results:
- * Demonstrated that temporal sampling resolution significantly affects parameter estimates (reorientation rate, noise amplitude).
- * Quantified the trade-off between temporal sampling resolution and observation noise in parameter estimation.
- * Showcased the robustness of the inference methodology to model misspecification.
Conclusions:
- * The study provides crucial insights into optimizing data collection strategies for biological transport studies.
- * Developed a robust inference framework applicable to real-world biological imaging datasets.
- * Offers practical experimental guidelines for researchers characterizing motile behavior using velocity jump models.
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