ALife is primed to address the biggest challenges in astrobiology by simulating systems which capture the most general and fundamental features of living systems. One such challenge is how to detect life outside of the solar system— especially without making strong assumptions about how life would manifest and interact with its planetary environment. Here we explore an ALife model meant to overcome this problem, by focusing on what life may do, rather than what life may be: life can spread between planetary systems (panspermia) and can modify planetary characteristics (terraformation). Our model shows that as life propagates across the galaxy, correlations emerge between planetary characteristics and location, and these correlations can function as a biosignature. This biosignature is agnostic because it is independent of strong assumptions about any particular instantiation of life or planetary characteristic. We demonstrate (and evaluate) a way to prioritize specific planets for further observation—based on their potential for containing life. We consider obstacles that must be overcome to practically implement our approach, including identifying specific ways in which better understanding astrophysical and planetary processes would improve our ability to detect life.

Biochemical reactions underlie all living processes. Like many systems, their web of interactions is difficult to fully capture and quantify with simple mathematical objects. Nonetheless, a huge volume of research has suggested many real-world systems–including biochemical systems–can be described simply as ‘scale-free’ networks, characterized by power-law degree distributions. More recently, rigorous statistical analyses upended this view, suggesting truly scalefree networks may be rare. We provide a first application of these newer methods across two distinct levels of biological organization: analyzing an ensemble of biochemical reaction networks generated from 785 ecosystem-level metagenomes and 1082 individual-level genomes (representing all domains of life). Our results confirm only a few percent of biochemical networks meet the criteria necessary to be more than super-weakly scale-free. We perform distinguishability tests across individual and ecosystem-level biochemical networks and find there is no sharp transition in the organization of biochemistry across distinct levels of the biological hierarchy–a result that holds across network projections. This suggests the existence of common organizing principles operating across different levels of biology, which can best be elucidated by analyzing all possible coarse-grained projections of biochemistry in tandem across scales.