AUSTIN, Texas, March 23, 2017 — Texas Advanced Computing Center (TACC) announced today that its Stampede supercomputer has helped researchers from Tufts, University of Maryland, Baltimore County create tadpoles with pigmentation never before seen in nature.
The flow of information between cells in our bodies is exceedingly complex: sensing, signaling, and influencing each other in a constant flow of microscopic engagements. These interactions are critical for life, and when they go awry can lead to the illness and injury.
Scientists have isolated thousands of individual cellular interactions, but to chart the network of reactions that leads cells to self-organize into organs or form melanomas has been an extreme challenge.
“We, as a community are drowning in quantitative data coming from functional experiments,” says Michael Levin, professor of biology at Tufts University and director of the Allen Discovery Center there. “Extracting a deep understanding of what’s going on in the system from the data in order to do something biomedically helpful is getting harder and harder.”
Working with Maria Lobikin, a Ph.D. student in his lab, and Daniel Lobo, a former post-doc and now assistant professor of biology and computer science at the University of Maryland, Baltimore County (UMBC), Levin is using machine learning to uncover the cellular control networks that determine how organisms develop, and to design methods to disrupt them. The work paves the way for computationally-designed cancer treatments and regenerative medicine.
“In the end, the value of machine learning platforms is in whether they can get us to new capabilities, whether for regenerative medicine or other therapeutic approaches,” Levin says.
Writing in Scientific Reports in January 2016, the team reported the results of a study where they created a tadpole with a form of mixed pigmentation never before seen in nature. The partial conversion of normal pigment cells to a melanoma-like phenotype — accomplished through a combination of two drugs and a messenger RNA — was predicted by their machine learning code and then verified in the lab.
Read the full report from TACC at: https://www.tacc.utexas.edu/-/machine-learning-lets-scientists-reverse-engineer-cellular-control-networks
Source: TACC