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SUMMARY:A Bayesian Approach to Molecular Communication - Chun Tung Chou\, 
 University of New South Wales\, Australia
DTSTART:20160413T130000Z
DTEND:20160413T140000Z
UID:TALK65566@talks.cam.ac.uk
CONTACT:Tim Hughes
DESCRIPTION:You can view a living cell simply as a soup of chemical molecu
 les\, but a living cell can sense the environment\, make decisions\, repai
 r itself\, communicate with other cells and do many other amazing things. 
 From a computing perspective\, a cell can be thought of as a computation\,
  communication and control device. Cells realise these functions by using 
 networks of chemical reactions (which are also known as molecular circuits
 ) and transport. Chemical reactions are inherently stochastic\, so a resea
 rch question in biology is to understand how cells use molecules and chemi
 cal reactions to compute and communicate. With the development of syntheti
 c biology\, there is now an opportunity to engineer novel molecular circui
 ts and bio-nano devices\, which are based on biological materials rather t
 han silicon. With this background in mind\, our research aims to develop a
  framework to understand\, analyse and engineer molecular communication. I
 n this seminar\, we examine molecular communication from a Bayesian infere
 nce point of view. Inspired by temporal coding found in living cells\, we 
 formulate an optimal decoding problem with a pair of molecular circuit bas
 ed transmitter and receiver. By modelling the end-to-end communication sys
 tem with a reaction-diffusion master equation and by solving an optimal Ba
 yesian filtering problem\, we derive the optimal decoder in the form of a 
 bank of analogue filters. An important insight from our solution is that m
 olecular communication is event-based with information communicated throug
 h binding and unbinding events. We will also discuss the problems of signa
 l and molecular circuit design.\n\nThis talk is predominantly based on the
 se two publications: (1) A Markovian approach to the optimal demodulation 
 of diffusion-based molecular communication networks. IEEE Trans. on Commun
 ications\, Oct 2015. (2) Maximum a posteriori decoding for diffusion-based
  molecular communication using analog filters. IEEE Trans on Nanotechnolog
 y\, Dec 2015.
LOCATION:Cambridge University Engineering Department\, LR6
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