Bulletin of the American Physical Society
APS March Meeting 2018
Volume 63, Number 1
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session F50: Morphogenesis II
11:15 AM–2:15 PM,
Tuesday, March 6, 2018
LACC Room: 511B
Sponsoring Units: DBIO GSOFT GSNP
Chair: Zi Chen, Dartmouth Coll
Abstract ID: BAPS.2018.MAR.F50.4
Abstract: F50.00004 : Revealing Developmental Logic with Deep Neural Networks
12:15 PM–12:27 PM
Here we demonstrate deep neural network to be a powerful tool tackling this problem. With the cellular rule to be determined being represented by fully-connected network, the developing embryo become a recurrent-convolutional network, and is trained with back propagation.
Though DNNs are well-known black boxes themselves, we still wonder, particularly, whether this approach can help revealing real biological mechanism given phenomena or some measured data. This idea was tested on Turing pattern and Drosophila gap-gene system, and could result in 1) a similar-looking regulatory network compared with existing knowledge, and 2) predictions on mutant behavior which bare notable similarities with experiment data.
We believe in the near future, this technique can help settle various unresolved issues in embryonic development.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.MAR.F50.4
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