Alysson Muotri is professor of pediatrics and of cellular and molecular medicine at the University of California, San Diego. He also co-directs the Stem Cell Program at the university’s Moores Cancer Center.
Alysson Muotri
Associate professor
University of California, San Diego
From this contributor
With tweaks, brains in a dish may yield clear clues to autism
‘Mini-brains’ created in a dish may reveal autism’s roots and point to treatments, but they do not yet mirror some critical features of a human brain.
With tweaks, brains in a dish may yield clear clues to autism
Questions for Alysson Muotri: Applying autism tools to Zika
Mini-brains grown from stem cells in culture can reveal the effects of both autism and the Zika virus on early development.
Questions for Alysson Muotri: Applying autism tools to Zika
Explore more from The Transmitter
The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.