  This Thesis presents research at the boundary between Statistical Physics and
Biology. First, we have devised a class of Boolean constraint satisfaction
problems (CSP) whose solutions describe the feasible operational states of a
chemical reaction network. After developing statistical mechanics techniques to
generate solutions and studying the properties of the solution space for both
ensembles and individual instances of random reaction networks, we have applied
this framework to the metabolic network of the bacterium E.Coli. Results
highlight, on one hand, a complex organization of operational states into
"modules" involving different biochemically-defined pathways, and, on the
other, a high degree of cross-talk between modules. In summary, we propose that
this class of CSPs may provide novel and useful quantitative information
linking structure to function in cellular reaction networks.
