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Created by: Alex Browning

Issue 244: Models are now routine in the interpretation of biological data, however are often limited by parameter non-identifiabilities. Indeed, simple goodness-of-fit metrics including the likelihood and residual error give only limited information about where a model does and doesn’t fit. In the paper accompanying the artwork, we study non-identifiability of complex models of tumour growth using simple surrogate models, that lie in between a model of interest and the data. The artwork shows a set of visual results: a low-dimensional line of constant goodness-of-fit lies at the intersection of two higher dimensional surfaces, each representing features in the data (in this case, the maximum size and initial growth rate of the tumour). One can move along this intersection in parameter space and achieve only a minimal change to the model predictions; hence, parameter non-identifiability.