To curb the spread of misinformation in science and medicine, the scientific community should devote more resources to technological approaches that identify false and retracted findings.
Image courtesy of Dave Cutler (artist).
New Research In
Physical Sciences
Featured Portals
Articles by Topic
Biological Sciences
Featured Portals
Articles by Topic
Edited by Robert H. Austin, Princeton University, Princeton, NJ, and approved December 4, 2017 (received for review June 19, 2017)
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.
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.
Log in using your username and password
Purchase access
You may purchase access to this article. This will require you to create an account if you don't already have one.
Artificial intelligence exploration of protocells
More Articles of This Classification
Physical Sciences
Chemistry
Related Content
Cited by...
- No citing articles found.
Similar Articles
You May Also be Interested in
In the midst of a microscopy boom, stem cell researchers are coming up with inventive techniques for capturing live images of these enigmatic cells in their native habitats.
Image courtesy of Panteleimon Rompolas (University of Pennsylvania, Philadelphia).
Sixty-six million years ago, an asteroid wiped out a huge swath of life on planet Earth. But could this and similar impacts have helped kick-start life itself?
Image courtesy of Detlev van Ravenswaay/ScienceSource.
Ralph Pudritz and Ben Pearce describe a model of how RNA-based life could have originated on the early Earth.
PNAS QnAs with Joanne Chory, winner of the Breakthrough Prize in life sciences.
