Potential integration of multi-fitting, inverse problem and mechanistic modelling approaches to applied research in animal science: a review

Modern researchers working in applied animal science systems have faced issues with modelling huge quantities of data. Modelling approaches that have previously been used to model biological systems are having problems to adapt to increased number of publications and research. So as to develop new approaches that have the potential to deal with these fast-changing complex conditions, it is relevant to review modern modelling approaches that have been used successfully in other fields. Therefore, this paper reviews the potential capacity of new integrated applied animal-science approaches to discriminate parameters, interpret data and understand biological processes. The analysis shows that the principal challenge is handling ill-conditioned complex models, but an integrated approach can obtain meaningful information from complementary data that cannot be obtained from present applied animal-science approaches. Furthermore, it is shown that parameter sloppiness and data complementarity are key concepts during system behaviour restrictions and parameter discrimination. Additionally, model evaluation and implementation of the potential integrated approach are reviewed. Finally, the objective of an integral approach is discussed. Our conclusion is that these approaches have the potential to be used to deepen the understanding of applied animal systems, and that exist enough developed resources and methodologies to deal with the huge quantities of data associated with this science.