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A laboratory robot helped researchers search for oil-and-water droplet mixtures that showed selected behaviors, using video analysis and an evolutionary algorithm to guide each round of experiments. The droplets were nonliving models—not cells—and the system’s “AI brain” was a computer-controlled laboratory workflow, not a biological or general-purpose intelligence.
What the robot actually did
A team associated with University of Glasgow chemist Lee Cronin built a chemorobotic system to prepare and test oil-in-water droplets. A custom 3D-printed fluidic device and pumps made the droplets; a camera recorded them; image-processing software identified activity; and an evolutionary algorithm used those measurements to choose formulations for later rounds.
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The primary study used four oil components—1-octanol, diethyl phthalate (DEP), 1-pentanol, and octanoic acid—in water containing the cationic surfactant TTAB at pH 13. The setup automated a loop of formulation, observation, measurement, and selection. In this context, “AI” refers to image analysis and algorithmic search within that loop, not to a robot independently understanding chemistry.
How the droplets were “evolved”
The researchers represented the proportions of the four oils in a recipe as a digital “genome.” The droplet’s measured behavior was its “phenotype.” Those terms describe an analogy: the droplets had no DNA or RNA sequence carrying hereditary information. The algorithm changed recipes and selected among them according to a chosen behavioral measure.
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The primary study’s activity-selection run
In the 2017 Nature Communications study, the authors searched a lattice of 282 oil formulations. In a separate genetic-algorithm run, they tested 20 recipe “genomes” per generation for 10 generations, selecting for the number of active droplets after one minute. By that paper’s defined activity measure, the count nearly doubled over the run. This was an increase in a selected activity score—not a doubling of cells, lifespan, or biological evolutionary fitness.
The faster run reported in 2018
James Urquhart’s 2018 Chemistry World report described nearly 400 random mixtures explored in a little under two days and a speed-optimization result reported as 14 times faster than predecessors after 30 generations. That account concerns a speed-selected run; its generations and metric should not be combined with the primary paper’s 10-generation activity-selection result.
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Why the environment mattered
The droplets displayed behaviors including movement and division, and their behavior changed when the physical environment changed. The 2017 paper’s contribution was not only to search for recipes, but also to test how environmental conditions and obstacles could alter what happened and which formulations the algorithm favored. The authors wrote that “the environment not only acts as an active selector over the genotypes, but also enhances the capacity for individual genotypes to undergo adaptation in response to environmental pressures.”
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallHere, “division” describes a reported droplet behavior, not biological reproduction. The environment could influence the measured outcome and therefore the algorithm’s selection, but the recipe remained a chemical formulation rather than a sequence-based genome.
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What this says—and does not say—about protocells
These oil droplets were simplified protocell models: compartments useful for studying how chemical systems can display organized behavior. They were not living cells, and the experiment did not create life or recreate life’s origin. Its value is as a way to investigate how chemical mixtures and their surroundings can produce behaviors that researchers can measure and select—questions relevant to origins-of-life research.
Other work uses different kinds of protocell models and reaches different endpoints. For example, a 2024 Nature Communications study reported liposomes supporting DNA replication and adaptive evolution through compartmentalized in-vitro transcription, translation, and replication. That is distinct from the 2017 oil-droplet experiment: the compartment type, role of genetic material, selection process, and demonstrated outcome differ. A separate 2018 PNAS project reported more than 7,000 experiments exploring unstable protocells; that figure belongs to that project, not the oil-droplet study.
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Sources
- James Urquhart, Chemistry World, 30 January 2018
- Parrilla-Gutierrez et al., Nature Communications 8, article 1144 (2017)
- Nature Communications (2024), “Darwinian Evolution of Self-Replicating DNA in a Synthetic Protocell”
- PNAS / PubMed (2018), “Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior”
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