  Standard techniques for studying biological systems largely focus on their
dynamical, or, more recently, their informational properties, usually taking
either a reductionist or holistic perspective. Yet, studying only individual
system elements or the dynamics of the system as a whole disregards the
organisational structure of the system - whether there are subsets of elements
with joint causes or effects, and whether the system is strongly integrated or
composed of several loosely interacting components. Integrated information
theory (IIT), offers a theoretical framework to (1) investigate the
compositional cause-effect structure of a system, and to (2) identify causal
borders of highly integrated elements comprising local maxima of intrinsic
cause-effect power. Here we apply this comprehensive causal analysis to a
Boolean network model of the fission yeast (Schizosaccharomyces pombe)
cell-cycle. We demonstrate that this biological model features a non-trivial
causal architecture, whose discovery may provide insights about the real cell
cycle that could not be gained from holistic or reductionist approaches. We
also show how some specific properties of this underlying causal architecture
relate to the biological notion of autonomy. Ultimately, we suggest that
analysing the causal organisation of a system, including key features like
intrinsic control and stable causal borders, should prove relevant for
distinguishing life from non-life, and thus could also illuminate the origin of
life problem.
