Robotic chemical reactors, synthetic organisms, and artificial intelligence are poised to shed new light on one of science’s most engaging and vexingly murky questions — the origin of life — in a unique new research project with $2.9 million in core funding from the John Templeton Foundation.
The project got its formal start this fall at the University of Arizona under the leadership of astrobiologist Sara Walker, who has made several key contributions to the study of life’s origins, and Paul Davies, a theoretical physicist, cosmologist, and 1995 Templeton Prize laureate. The three-year series of studies and analyses aims to generate significant new data and interpretive insights on the possible pathways between “almost-life” and true biological systems. Its quantitative approach aims to help scientists understand both the ways in which life could have originated on earth and how frequently (and under what conditions) we might expect it to have arisen elsewhere in the universe.
Traditionally scientists have tackled origin-of-life research using either “bottom-up” or “top-down” approaches — working upwards from chemicals thought to have been present on primordial earth, or downwards through ever-simpler organisms to determine life’s literal minimum viable product. Walker and Davies’ project will incorporate elements of both approaches to generate usable data at unprecedented scales. Chemist Lee Cronin will lead a team at the University of Glasgow to deploy massively parallel robotic chemical reactors to explore the relationships between a huge range of environmental starting conditions and their impact on the formation of chemistries necessary for almost-life.
Another team headed by University of Minnesota biologist Kate Adamala will use cutting-edge techniques to construct synthetic cells that range from low-complexity molecular circuits to efficient, complex systems. Her goal is to study what happens as these cells become more “life-like” — and at which points in the transition cells become much more capable of responding to environmental changes.
Walker and Davies will then integrate the large amounts of data generated by Cronin and Adamala’s teams using machine learning algorithms to develop novel, empirically-backed predictions and models for life’s emergence — in essence creating a functional “meter” of life to assess the probability of life (or almost-life) emerging from a given set of starting conditions.
“In order to understand life’s origins, we need a richer understanding of that realm of complex chemistry in the transitions from abiotic to biotic systems” says Paul Wason, the John Templeton Foundation’s vice president for life sciences and genetics. “This project’s data-driven approach will help us understand more about the conditions under which life might emerge — and whether the circumstances that allowed for life’s beginnings on earth are unlikely to have been repeated elsewhere — that is, whether or not we live in a deeply bio-friendly universe.”