How can you build a model of biology we do not quite understand? What about competing hypotheses? Conflicting data?
The model is an integrated, quantitative formalization of our current understanding of the biology and data. It allows evaluation of the hypothesized biology, including competing hypotheses and data, which can be implemented via alternate structures and parameterizations. The model can be used to identify inconsistencies between hypotheses and data to support evaluation of the hypotheses and even propose new hypotheses.
|
With enough parameters you can fit an elephant. The model is underspecified and the parameters are not identifiable.
The first assertion is not true, because the model structure is not empirical but based on biological mechanism. Thus, the ability to “fit” the data is not guaranteed, regardless of the numbers of parameters. QSP models often contain many so‐called “sloppy” parameters, which do not influence behaviors of interest. Sensitivity analysis identifies influential parameters to vary in alternate parameterizations. Numerous alternate parameterizations consistent with the data are explored. Model reduction can be used to make parameters identifiable if needed.
|
|
How do we evaluate and interpret this work? To what extent should we trust the predictions?
|
• Interpretation of results is based on the following criteria (corresponding to the workflow stages):
Application to questions for which it is qualified Quantity/quality of biological knowledge and data Reasonable representation of the biology Consistency with all relevant behaviors Adequate exploration of variability and uncertainty and testing of predictive capability Articulation of important data gaps for experimental evaluation, and proposal/verification of “testable” hypotheses The robustness of the predictions depends on the extent of exploration of the above
|
The models are too complex to explain to collaborators.
Regular discussion with collaborators and advisors promotes shared understanding and ownership of the model Emphasis on the biological explanation/justification of decisions and findings fosters acceptance and adoption
|
Model predictions were wrong. Therefore the model is not useful.
Incorrect predictions can offer valuable insight by identifying inadequacies in the understanding of the biology, as formalized in the model. Proposing mechanisms that could resolve the mismatches provides novel biological hypotheses and highlights areas for further experimental exploration to advance the understanding in the field.
|
