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SUMMARY:Computational models of morphogenetic decision-making - Prof. Mich
 ael Levin &amp\; Dr. Santosh Manicka (Tufts University)
DTSTART:20210520T160000Z
DTEND:20210520T170000Z
UID:TALK160651@talks.cam.ac.uk
CONTACT:Dr. Adrien Hallou
DESCRIPTION:The cognitive capacities of nervous systems have their evoluti
 onary origin in ancient\, pre-neural systems for computation. Long before 
 an organism must make decisions to guide behavior in space\, its embryonic
  tissues must navigate morphospace to create a complex anatomy. Indeed\, d
 uring regeneration\, cellular collective must coordinate their activity to
  rebuild a specific organ or appendage and then stop when the correct patt
 ern is complete. Our lab has shown that one important component of morphol
 ogical computation is bioelectrical: cell groups drive spatio temporal pat
 terns of resting potentials that process morphogenetic information and reg
 ulate gene expression and cell behavior. In the first half of this talk\, 
 we will describe developmental bioelectricity and show examples in which w
 e re-write the bioelectric pattern memories that guide growth and form. Un
 derstanding the origin and computational capacities of these non-neural ci
 rcuits is critical for evolutionary developmental biology and the design o
 f new biomedical therapies. Therefore\, we have begun efforts to model the
  biophysics and information processing in bioelectrical networks. In the s
 econd half of the talk\, we examine and analyze a minimal bioelectric netw
 ork model (BEN). We show that BEN networks can be trained to function as l
 ogic gates (the fundamental units of a digital computer)\, pattern detecto
 rs\, and pattern regulators. We show examples of trained BEN networks in e
 ach case and phenomenologically map them to biological examples. We furthe
 r show how decisions are made in these networks using dynamical systems an
 d information theory analysis tools. The results motivate the understandin
 g of non-neuronal phenomena like morphogenesis and embryogenesis from an i
 nformation-processing point of view\, and thus help design novel biologica
 l experiments and regenerative medicine strategies.
LOCATION:Webinar
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