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Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior

Laurie J. Points, James Ward Taylor, Jonathan Grizou, Kevin Donkers and Leroy Cronin
PNAS January 16, 2018. 201711089; published ahead of print January 16, 2018. https://doi.org/10.1073/pnas.1711089115
Laurie J. Points
aWestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom
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James Ward Taylor
aWestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom
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Jonathan Grizou
aWestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom
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Kevin Donkers
aWestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom
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Leroy Cronin
aWestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom
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  • ORCID record for Leroy Cronin
  • For correspondence: lee.cronin@glasgow.ac.uk
  1. Edited by Robert H. Austin, Princeton University, Princeton, NJ, and approved December 4, 2017 (received for review June 19, 2017)

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Significance

Exploring and understanding the emergence of complex behaviors is difficult even in “simple” chemical systems since the dynamics can rest on a knife edge between stability and instability. Herein, we study the complex dynamics of a simple protocell system, comprising four-component oil droplets in an aqueous environment using an automated platform equipped with artificial intelligence. The system autonomously selects and performs oil-in-water droplet experiments, and then records and classifies the behavior of the droplets using image recognition. The data acquired are then used to build predictive models of the system. Physical properties such as viscosity, surface tension, and density are related to behaviors, as well as to droplet behavioral niches, such as collective swarming.

Abstract

Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors. Utilizing >7,000 experiments collected using an autonomous robotic platform, we illustrate how smart automation cannot only help with exploration, optimization, and discovery of new behaviors, but can also be core to developing fundamental understanding of such systems. Using this process, we were able to relate droplet formulation to behavior via predicted physical properties, and to identify and predict more occurrences of a rare collective droplet behavior, droplet swarming. Proton NMR spectroscopic and qualitative pH methods enabled us to better understand oil dissolution, chemical change, phase transitions, and droplet and aqueous phase flows, illustrating the utility of the combination of smart-automation and traditional analytical chemistry techniques. We further extended our study for the simultaneous exploration of both the oil and aqueous phases using a robotic platform. Overall, this work shows that the combination of chemistry, robotics, and artificial intelligence enables discovery, prediction, and mechanistic understanding in ways that no one approach could achieve alone.

  • artificial intelligence
  • protocell models
  • complex chemical systems
  • emergence
  • machine learning

Footnotes

  • ↵1To whom correspondence should be addressed. Email: lee.cronin{at}glasgow.ac.uk.
  • Author contributions: J.G. and L.C. designed research; L.J.P., J.W.T., J.G., and K.D. performed research; L.J.P., J.W.T., and K.D. contributed new reagents/analytic tools; L.J.P., J.W.T., and J.G. analyzed data; and L.J.P., J.G., and L.C. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1711089115/-/DCSupplemental.

Published under the PNAS license.

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Artificial intelligence exploration of protocells
Laurie J. Points, James Ward Taylor, Jonathan Grizou, Kevin Donkers, Leroy Cronin
Proceedings of the National Academy of Sciences Jan 2018, 201711089; DOI: 10.1073/pnas.1711089115

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Artificial intelligence exploration of protocells
Laurie J. Points, James Ward Taylor, Jonathan Grizou, Kevin Donkers, Leroy Cronin
Proceedings of the National Academy of Sciences Jan 2018, 201711089; DOI: 10.1073/pnas.1711089115
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