Published: November 21, 2017 Author: Nina Welding
From the earliest of days, researchers have been recording their observations, analyzing what they see to interpret and apply the facts before them. Today, however, imaging especially in biomedical communities requires more than the human eye or even incredibly accurate “cameras.” In cases such as the joint project between the University of Notre Dame and Pennsylvania State University (PSU), it requires close collaboration between biologists and computer scientists using deep-learning methods for artificial intelligence to speed up and improve the process.
The joint project, titled “From Cells to Societies: Mechanisms by which Microbial Parasites Control Host Phenotypes,” studies the collective social behaviors of fungal cells inside host ants. Called “zombie ants,” the insects’ bodies are basically hijacked by a fungus, which compels them to act in a certain way in order to spread fungal spores.
Entomologist David Hughes at PSU had been studying the phenomenon for years, searching for clues as to how the fungus gains control over an ant’s body without infecting its brain. Hughes and his team have dissected colonies of infected ants, studying each slice to identify ant cells versus fungal cells. However, a single ant image would take months to identify and analyze.
Enter Danny Z. Chen, professor of computer science and engineering (CSE) at the University of Notre Dame, and Yizhe Zhang, a CSE Ph.D. student at Notre Dame. Hughes and Chen met at the National Academies Keck Futures Initiatives Conference on Collective Behaviors in 2014. Within a year not only were they working together to find ways to better image and analyze fungal cells within ants, but they had also been awarded a 5-year National Institutes of Health Research Grant to continue their efforts. One of their first papers from this project, titled “3D Visualization and a Deep Learning Model Reveal Complex Fungal Parasite Networks in Behaviorally Manipulated Ants,” was published in the Early Edition of the Proceedings of the National Academy of Sciences of the United States of America, November 7, 2017.
Originally Published: http://conductorshare.nd.edu/news/close-collaboration-sheds-light-on-collective-behaviors/