Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge

Being able to infer the interactions between a set of species from observations of the system is of paramount importance to obtain explaining and predictive models in ecology. We tackled this challenge by employing qualitative modeling frameworks and logic methods for the synthesis of mathematical models that can integrate both observations and expert knowledge on 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. Our inference method supports optimization criteria to derive 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 system, and showed its ability to recover essential and sufficient ecological interactions to explain the observed dynamics.