An improved framework for grey-box identification of biological processes

Developing models from observations is at the heart of empirical science. Grey-box Modeling combines the insights gained from the results obtained from first principles with observational data. When the model turns out to be unsatisfactory, the goodness of such grey-box models in terms of predictability and parameter estimates largely depends on either modifying the model structure obtained from the first principles or conducting new experiments. Unfortunately, in the context of biological models, where the model structures are usually nonlinear ODEs with a large number of states and parameters along with sparse and noisy experimental data, traditional identification protocols have to go through several iterations to identify the source of the issue. Even after multiple iterations, they may still arrive at sub-optimal solutions. In this work, we propose an improved framework with a new set of tools to resolve this issue unambiguously with a minimum round of iterations.