Scientists have grappled with reconciling biological evolution^1,2 with the immutable laws of the Universe defined by physics. These laws underpin life’s origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Evolutionary theory explains why some things exist and others do not through the lens of selection. To comprehend how diverse, open-ended forms can emerge from physics without an inherent design blueprint, a new approach to understanding and quantifying selection is necessary^3–5. We present assembly theory (AT) as a framework that does not alter the laws of physics, but redefines the concept of an ‘object’ on which these laws act. AT conceptualizes objects not as point particles, but as entities defined by their possible formation histories. This allows objects to show evidence of selection, within well-defined boundaries of individuals or selected units. We introduce a measure called assembly (A), capturing the degree of causation required to produce a given ensemble of objects. This approach enables us to incorporate novelty generation and selection into the physics of complex objects. It explains how these objects can be characterized through a forward dynamical process considering their assembly. By reimagining the concept of matter within assembly spaces, AT provides a powerful interface between physics and biology. It discloses a new aspect of physics emerging at the chemical scale, whereby history and causal contingency influence what exists.

The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab.

Although it has been notoriously difficult to pin down precisely what is it that makes life so distinctive and remarkable, there is general agreement that its informational aspect is one key property, perhaps the key property. The unique informational narrative of living systems suggests that life may be characterized by context-dependent causal influences, and, in particular, that top-down (or downward) causation—where higher levels influence and constrain the dynamics of lower levels in organizational hierarchies—may be a major contributor to the hierarchal structure of living systems. Here, we propose that the emergence of life may correspond to a physical transition associated with a shift in the causal structure, where information gains direct and context-dependent causal efficacy over the matter in which it is instantiated. Such a transition may be akin to more traditional physical transitions (e.g. thermodynamic phase transitions), with the crucial distinction that determining which phase (non-life or life) a given system is in requires dynamical information and therefore can only be inferred by identifying causal architecture. We discuss some novel research directions based on this hypothesis, including potential measures of such a transition that may be amenable to laboratory study, and how the proposed mechanism corresponds to the onset of the unique mode of (algorithmic) information processing characteristic of living systems.

We introduce a Bayesian method for guiding future directions for detection of life on exoplanets. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from better understanding stellar environment, planetary climate and geophysics, geochemical cycling, the universalities of physics and chemistry, the contingencies of evolutionary history, the properties of life as an emergent complex system, and the mechanisms driving the emergence of life. We provide examples for how the Bayesian formalism could guide future search strategies, including determining observations to prioritize or deciding between targeted searches or larger lower resolution surveys to generate ensemble statistics and address how a Bayesian methodology could constrain the prior probability of life with or without a positive detection. Key Words: Exoplanets—Biosignatures—Life detection—Bayesian analysis. Astrobiology 18, 779–824. 1. Introduction 2. Setting the Stage: What Is Life? What Is a Biosignature? 3. Detecting Unknown Biology on Unknown Worlds: A Bayesian Framework 3.1. Habitability in the Bayesian framework for biosignatures 4. P(data|abiotic) 4.1. Stellar environment 4.2. Climate and geophysics 4.2.1. Coupled tectonic–climate models 4.2.2. Community GCM projects for generating ensemble statistics for P(data|abiotic) and P(data|life) 4.3. Geochemical environment 4.3.1. Anticipating the unexpected: statistical approaches to characterizing atmospheres of non-Earth-like worlds 5. P(data|life) 5.1. Black-box approaches to living processes 5.1.1. Type classification of Seager et al. (2013a) 5.1.1.1. Energy capture (type I) 5.1.1.2. Biomass capture (type II) 5.1.1.3. Other uses (type III) 5.1.1.4. Products of modification of gases (type IV) 5.1.2. Alternatives for type classification 5.1.2.1. Type I, energy capture 5.1.2.2. Type II, biomass capture 5.1.2.3. Type III, “other uses” 5.1.2.4. Type IV 5.1.3. When is it appropriate to deconstruct a black box? 5.2. Life as improbable chemistry 5.3. Life as an evolutionary process 5.3.1. Life as a coevolution with its planet: Earth as an example 5.3.2. Calculating conditional probabilities in biological evolution from past biogeochemical states 5.4. Insights from universal biology 5.4.1. Network biosignatures 5.4.2. Universal scaling laws, applicable to other worlds? 6. P(life) 6.1. P(emerge): constraining the probability of the origins of life 6.2. Biological innovations and the conditional probabilities for living processes 7. A Bayesian Framework Example: Detecting Atmospheric Oxygen 8. Tuning Search Strategies Based on the Bayesian Framework 9. Conclusions Acknowledgments Author Disclosure Statement References Abbreviations Used

The hypothesis that many living systems should exhibit near-critical behavior is well motivated theoretically, and an increasing number of cases have been demonstrated empirically. However, a systematic analysis across biological networks, which would enable identification of the network properties that drive criticality, has not yet been realized. Here, we provide a first comprehensive survey of criticality across a diverse sample of biological networks, leveraging a publicly available database of 67 Boolean models of regulatory circuits. We find all 67 networks to be near critical. By comparing to ensembles of random networks with similar topological and logical properties, we show that criticality in biological networks is not predictable solely from macroscale properties such as mean degree $⟨K⟩$ and mean bias in the logic functions $⟨p⟩$, as previously emphasized in theories of random Boolean networks. Instead, the ensemble of real biological circuits is jointly constrained by the local causal structure and logic of each node. In this way, biological regulatory networks are more distinguished from random networks by their criticality than by other macroscale network properties such as degree distribution, edge density, or fraction of activating conditions.

The origins of life stands among the great open scientific questions of our time. While a number of proposals exist for possible starting points in the pathway from non-living to living matter, these have so far not achieved states of complexity that are anywhere near that of even the simplest living systems. A key challenge is identifying the properties of living matter that might distinguish living and non-living physical systems such that we might build new life in the lab. This review is geared towards covering major viewpoints on the origin of life for those new to the origin of life field, with a forward look towards considering what it might take for a physical theory that universally explains the phenomenon of life to arise from the seemingly disconnected array of ideas proposed thus far. The hope is that a theory akin to our other theories in fundamental physics might one day emerge to explain the phenomenon of life, and in turn finally permit solving its origins.