  The realistic modeling intended to quantify precisely some biological
mechanisms is a task requiering a lot of a priori knowledge and generally
leading to heavy mathematical models. On the other hand, the structure of the
classical Machine Learning algorithms, such as Neural Networks, limits their
flexibility and the possibility to take into account the existence of complex
underlying phenomena, such as delay, saturation and accumulation. The aim of
this paper is to reach a compromise between precision, parsimony and
flexibility to design an efficient biomimetic predictive tool extracting
knowledge from livestock data. To achieve this, we build a Mathematical Model
based on Partial Differential Equations (PDE) embarking the mathematical
expression of biological determinants. We made the hypothesis that all the
physico-chemical phenomena occurring in animal body can be summarized by the
evolution of a global information. Therefore the developed PDE system describes
the evolution and the action of an information circulating in an Avatar of the
Real Animal. This Avatar outlines the dynamics of the biological reactions of
animal body in the framework of a specific problem. Each PDE contains
parameters corresponding to biological-like factors which can be learnt from
data by the developed Statistical Learning Tool.
