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The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems
Andrew White1, Malachi Tolman1, Howard D Thames2,3
1Department of Physics & Astronomy, Brigham Young University, Provo, Utah, United States of America.
Plos Computational Biology
|December 7, 2016
Summary
Sloppy models present challenges for experimental design and parameter estimation. Complementary experiments can increase systematic error, reducing model predictability despite accurate parameter fitting.
Area of Science:
- Systems Biology
- Computational Biology
- Mathematical Modeling
Background:
- Mathematical models of complex biological processes often contain numerous parameters, many of which are practically unidentifiable.
- The relevance of specific physical mechanisms within these models can vary significantly between different experimental conditions.
Purpose of the Study:
- To investigate the interplay between experimental design, parameter estimation, and systematic error in the context of sloppy models.
- To assess the impact of model discrepancy on predictive power, even when parameters are accurately estimated.
Main Methods:
- Utilized a hyper-model to quantify model discrepancy.
- Applied the methodology to two complex biological models: EGFR signaling and DNA repair, using optimally selected experiments.
Main Results:
- Complementary experimental design can inadvertently introduce systematic error by highlighting omitted mechanisms, leading to poor data fit.
- Despite accurate parameter estimation, model discrepancy significantly reduced predictive capabilities compared to the sloppy regime with minimal systematic error.
Conclusions:
- Identified 'sloppy systems' as sequences of models of increasing complexity that exhibit sloppiness at microscopic accuracy.
- Advocated for using a hierarchy of models with varying detail to understand system behavior, rather than relying solely on parameter estimation in a single model.
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