How causal analysis can reveal autonomy in models of biological systems

Abstract

SummaryStandard 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.

Re-conceptualizing the origins of life

“…Marshall et al [40] instead focus on the intrinsic complexity of living processes. Building on earlier work by Kim et al [42] (see also [43]) and leveraging the quantitative framework of integrated information theory [44], they demonstrate how causal analysis can reveal where the boundaries of a living system might lie, and importantly how, within those boundaries, the system generates its own future states.…”

“…how much information it generates, processes, etc.). This can be approached extrinsically or intrinsically, as approached by Cronin and co-workers [39] and Marshall et al [40], respectively.…”

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

“…Integrated information theory has been used to study the structure of the fission yeast cell cycle network, and the network as a whole has been found to maintain integration through the sequence of states corresponding to the phases of the cell cycle [465]. A subgraph of the network in figure 3(A), known as a 'backbone motif', was also analyzed [466].…”

When is an action caused from within? Quantifying the causal chain leading to actions in simulated agents

“…In all, our results suggested that the "intrinsicality" of the direct causes and the causal chain preceding an agent's actions may serve as a useful indicator of its intrinsic complexity and degree of causal autonomy (see also Marshall et al (2017);Bertschinger et al (2008)), while the number of nodes constituting the cause purviews, as well as the complexity and duration of the causal chains may reflect its context-sensitivity.…”

“…First, the AC analysis specifically takes an animat's mechanistic, counterfactual structure into account (see also Shalizi et al (2005)). Therefore, it may describe aspects of the system that cannot be captured by purely structural, or dynamical, informational, or correlational measures based on observed data only (Marshall et al, 2017). For example, we hardly found significant differences between task conditions regarding the inputs to the motor nodes ( Figure 2F,G).…”