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:/ / www. plos. org/ wp-content/ uploads/ 2015/ 05/ Levin-4-June. jpg

All works published in PLOS Computational Biology are Open Access, which means that all content is immediately and freely available. Use this URL in your coverage to provide readers access to the paper upon publication: http://journals. plos. org/ ploscompbiol/ article?id= 10. 1371/ journal. pcbi. 1004295

Contact: Michael Levin

Address: Tufts University

Biology / Tufts Center for Regenerative and Developmental Biology

200 Boston Ave.

Suite 4600

Medford, MA 2155

UNITED STATES

Phone: 617 627 6161

Email: michael.levin@tufts.edu

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.

About PLOS Computational Biology

PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLOS Computational Biology are Open Access. All content is immediately available and subject only to the condition that the original authorship and source are properly attributed. Copyright is retained. For more information follow @PLOSCompBiol on Twitter or contact ploscompbiol@plos.org.

About PLOS