We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model.Ĭitation: White A, Tolman M, Thames HD, Withers HR, Mason KA, Transtrum MK (2016) The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems. We introduce the concept of a sloppy system–a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We use a simple hyper-model of model error to quantify a model’s discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. Furthermore, which mechanisms are relevant/irrelevant vary among experiments.
As a consequence, models are often overly complex, with many practically unidentifiable parameters.
In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors.
We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models.
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