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SUMMARY:Understanding the energetic costs of biological computation - Benj
 amin Machta\, Yale University
DTSTART:20241015T120000Z
DTEND:20241015T130000Z
UID:TALK222388@talks.cam.ac.uk
CONTACT:Tal Agranov
DESCRIPTION:Biological information processing is energetically costly\; ou
 r brain consumes more energy per gram than our muscles. But while thermody
 namic tools roughly capture the energy requirements of the mechanical work
  done by muscles\, we have no equivalent framework to understand the energ
 y requirements and efficiency of the information processing done by our br
 ains. Fundamental bounds applied without reference to physical details typ
 ically predict costs of order the thermal energy\, KBT per bit\, at least 
 six orders of magnitude smaller than measured costs of transmission betwee
 n neurons. Here I will argue that costs can still be understood as arising
  from the need to beat intrinsic thermal fluctuations\, but that practical
  costs can be much larger than KBT due to geometric constraints and the ph
 ysical properties of the communication medium.\n\n\nI will focus on the en
 ergy cost of communication between molecular components. Living systems us
 e physically distinct media for communication\; electrical currents mediat
 ed by ion channels are carried through the cytoplasm and are sensed by dis
 tant voltage sensitive ion channels. And second messenger molecules are pr
 oduced locally and diffuse to be sensed by distant molecular receptors. Ea
 ch of these processes are inherently dissipative and inherently noisy\, co
 rrupted by thermal fluctuations intrinsic to the medium. I will show that 
 sending signals that are reliable over these fluctuations implies costs wh
 ich are often orders of magnitude larger than the thermal energy\, plausib
 ly accounting for the high energy cost of running ion channels and other s
 ignal transduction machinery.
LOCATION:Center for Mathematical Sciences\, Lecture room MR4
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