Project properties
Title Understanding the effect of different types of experimental noise
Group Systems and Synthetic Biology
Project type thesis
Credits 36
Supervisor(s) Edoardo Saccenti
Examiner(s) Edoardo Saccenti, Rob Smith
Contact info robert1.smith@wur.nl
Begin date 2024/05/15
End date
Description Experimental errors significantly impact parameter estimation for dynamic systems, introducing uncertainties that can compromise the accuracy and reliability of the model. These errors arise from various sources, such as measurement inaccuracies, environmental disturbances, and limitations in the experimental setup. In dynamic systems, where parameters often exhibit time-varying behavior, even minor errors can propagate and magnify, leading to substantial deviations in the estimated parameters.
This can result in models that do not accurately capture the system's true dynamics, potentially causing erroneous predictions and suboptimal interpretation.
Experimental errors exhibit a large variety of structures (additive, multiplicative, correlated and uncorrelated): while additive noise if relatively well understood, it is not clear how and to what extent more complex error models (and which characteristics) affect parameter estimation in dynamic models where concentration (for instance) are measured in presence of errors.
Scope of this project is to explore how different type of error impact on parameter estimation and identifiability using well characterized dynamic models as template.

References

1. Saccenti, E., Hendriks, M.H., and Smilde, A.K. (2020). Corruption of the Pearson correlation coefficient by measurement error and its estimation, bias, and correction under different error models. Scientific Reports 10, 1-19.
2. Gábor, A., and Banga, J.R. (2015). Robust and efficient parameter estimation in dynamic models of biological systems. BMC systems biology 9, 1-25.







Used skills Advanced multivariate statistical tools, Models and characteristics of experimental errors. Parameter estimation for dynamic models. Advanced programming skills in R/Matlab and Python.
Requirements Good understanding of basic concepts of multivariate statistics (covariance and correlation matrices, statistical distributions, data generation). Familiarity with dynamics modelling (ordinary differential equations) and parameter estimation at the level of Advance Systems Biology (SSB-31806). Programming in Matlab or R (or alternatively Python).