An artificial intelligence system has for the first time reverse-engineered the regeneration mechanism of planaria--the small worms whose extraordinary power to regrow body parts has made them a research model in human regenerative medicine.
The discovery by Tufts University biologists presents the first model of regeneration discovered by a non-human intelligence and the first comprehensive model of planarian regeneration, which had eluded human scientists for over 100 years. The work, published in PLOS Computational Biology, demonstrates how "robot science" can help human scientists in the future.
To mine the fast-growing mountain of published experimental data in regeneration and developmental biology Lobo and Levin developed an algorithm that would use evolutionary computation to produce regulatory networks able to "evolve" to accurately predict the results of published laboratory experiments that the researchers entered into a database.
"Our goal was to identify a regulatory network that could be executed in every cell in a virtual worm so that the head-tail patterning outcomes of simulated experiments would match the published data," Lobo said.
The paper represents a successful application of the growing field of "robot science" - which Levin says can help human researchers by doing much more than crunch enormous datasets quickly.
"While the artificial intelligence in this project did have to do a whole lot of computations, the outcome is a theory of what the worm is doing, and coming up with theories of what's going on in nature is pretty much the most creative, intuitive aspect of the scientist's job," Levin said. "One of the most remarkable aspects of the project was that the model it found was not a hopelessly-tangled network that no human could actually understand, but a reasonably simple model that people can readily comprehend. All this suggests to me that artificial intelligence can help with every aspect of science, not only data mining but also inference of meaning of the data."
Image Caption: Planaria Algorithmic
Image Credit: Levin et al.
Image Link: https:/
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Contact: Michael Levin
Address: Tufts University
Biology / Tufts Center for Regenerative and Developmental Biology
200 Boston Ave.
Medford, MA 2155
Phone: 617 627 6161
Citation: Lobo D, Levin M (2015) Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration. PLoS Comput Biol 11(6): e1004295. doi:10.1371/journal.pcbi.1004295
Funding: This work was supported by NSF grant EF-1124651, NIH grant GM078484, USAMRMC grant W81XWH-10-2-0058, and the Mathers Foundation. Computation used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by NSF grant OCI-1053575, and a cluster computer awarded by Silicon Mechanics. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
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