The IRIS model reconstructs from gene activity which signals a cell received during development; a prediction for lung cells was confirmed in experiments
Prepared by Sintelligent with the help of SI. Published in SI: . Updated: .
SI is learning to "read" a cell's signaling history from gene activity, which could help grow needed tissues, but so far only for six pathways.
According to phys.org, researchers at the Massachusetts Institute of Technology and the Whitehead Institute (led by Pulin Li, with first author Nicholas Hutchins) built a neural-network model, IRIS, that uses "fingerprints" of gene activity to reconstruct which chemical signals a cell received during development. The model was trained on a large dataset of how thousands of human embryonic stem cells responded to dozens of combinations of six major signaling pathways. It predicted when and where pathways activate in mouse embryo cells that go on to form heart, gut, muscle and spinal cord, and works on cell types it has not seen before; the signals predicted for lung cells were confirmed by experiments. The work is published in Nature Methods; the limitation is that only six pathways are covered, and applicability to the full range of cell signaling remains to be shown.
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