  We present schema redescription as a methodology to characterize canalization
in automata networks used to model biochemical regulation and signalling. In
our formulation, canalization becomes synonymous with redundancy present in the
logic of automata. This results in straightforward measures to quantify
canalization in an automaton (micro-level), which is in turn integrated into a
highly scalable framework to characterize the collective dynamics of
large-scale automata networks (macro-level). This way, our approach provides a
method to link micro- to macro-level dynamics -- a crux of complexity. Several
new results ensue from this methodology: uncovering of dynamical modularity
(modules in the dynamics rather than in the structure of networks),
identification of minimal conditions and critical nodes to control the
convergence to attractors, simulation of dynamical behaviour from incomplete
information about initial conditions, and measures of macro-level canalization
and robustness to perturbations. We exemplify our methodology with a well-known
model of the intra- and inter cellular genetic regulation of body segmentation
in Drosophila melanogaster. We use this model to show that our analysis does
not contradict any previous findings. But we also obtain new knowledge about
its behaviour: a better understanding of the size of its wild-type attractor
basin (larger than previously thought), the identification of novel minimal
conditions and critical nodes that control wild-type behaviour, and the
resilience of these to stochastic interventions. Our methodology is applicable
to any complex network that can be modelled using automata, but we focus on
biochemical regulation and signalling, towards a better understanding of the
(decentralized) control that orchestrates cellular activity -- with the
ultimate goal of explaining how do cells and tissues 'compute'.
