Abstract
Being able to infer the interactions between a set of species from observations of the system is of paramount importance to obtain explanatory and predictive models in ecology. We tackled this challenge by employing qualitative modelling frameworks and logic methods for the synthesis of mathematical models that can integrate both observations and expert knowledge on the system. Boolean networks is a qualitative modelling framework, which enables reasoning exhaustively on possible dynamics of the system. After devising a formal link between ecological networks and the causal structure of Boolean networks, we applied a generic model synthesis engine to infer Boolean models that are able to reproduce the observed dynamics of a protist community and of a planktonic ecosystem. Our inference method supports optimization criteria to derive the most parsimonious and most precise models. It is also able to integrate prior knowledge on the ecological network, adding constraints on impossible interactions, which is necessary to obtain realistic predictions. Such constraints may, however, prove to be too strict, in which case our method is able to conclude on the absence of a model compatible with both the observations and the input hypotheses. We demonstrated our methodology on experimental data of a protist community and of a planktonic ecosystem and showed in each case its ability to recover essential and sufficient ecological interactions to explain the observed dynamics.