  In this paper we suggest that, under suitable conditions, supervised learning
can provide the basis to formulate at the microscopic level quantitative
questions on the phenotype structure of multicellular organisms. The problem of
explaining the robustness of the phenotype structure is rephrased as a real
geometrical problem on a fixed domain. We further suggest a generalization of
path integrals that reduces the problem of deciding whether a given molecular
network can generate specific phenotypes to a numerical property of a
robustness function with complex output, for which we give heuristic
justification. Finally, we use our formalism to interpret a pointedly
quantitative developmental biology problem on the allowed number of pairs of
legs in centipedes.
