How causal analysis can reveal autonomy in models of biological systems

TL;DR

This paper applies integrated information theory to analyze the causal architecture of a biological system model, revealing insights into autonomy and organization that could distinguish living systems from non-living ones.

Contribution

It demonstrates how causal analysis using IIT can uncover the organizational structure and autonomy in biological models, offering new insights beyond traditional approaches.

Findings

The biological model exhibits a complex causal architecture.

Causal borders relate to biological notions of autonomy.

Analysis may help distinguish life from non-life.

Abstract

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.