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SUMMARY:Models and software applied to biomedical research - Dr. Neil Dalc
 hau\, Microsoft Research Cambridge
DTSTART:20120302T123000Z
DTEND:20120302T140000Z
UID:TALK36420@talks.cam.ac.uk
CONTACT:Ye Yuan
DESCRIPTION:Cells\, a fundamental unit of life on this planet\, are able t
 o process information deriving both from outside and within to make life-p
 reserving decisions. Molecular signals are passed within and between cells
  and generated in response to extracellular stimuli\, and feed into bioche
 mical networks that carry out the major functions of an organism: energy s
 torage\, protection from pathogens\, time-of-day determination\, etc. Unde
 rstanding biological information processing is fundamental for learning ho
 w to treat and prevent disease\, or even for using biochemistry to perform
  computation. \nIn this talk\, I’ll show how I am using mechanistic mode
 ls to reverse- and forward-engineer complex biochemistry\, incorporating e
 xperimental observations and exploiting modularity in the system descripti
 on. As case studies\, I will show how we are using software and modelling 
 to projects in the fields of immunology and synthetic biology.\n\n\nThe im
 mune system is a complex set of mechanisms that seek to identify and rid f
 oreign (pathogen-derived) proteins\, requiring extensive information proce
 ssing. I’ll introduce the first computational model of the MHC class I p
 athway\, which has helped to elucidate how cells present a filtered snapsh
 ot of their internal contents to T lymphocytes (which blood cells)\, the f
 irst step in the immune system recognition of viral infection. \n\n\nSynth
 etic biology offers the potential to utilise the cellular environment for 
 functions not intended by evolution alone. By inserting additional DNA\, n
 ew functions/components can be conferred to cells that can be used to synt
 hesise medicines and biofuels\, or to teach us about how biochemistry faci
 litates information processing. I will show how to engineer complex spatio
 -temporal behaviours in populations of interacting bacteria\, using interc
 ellular signalling\, inducible gene expression\, rigorous model-based desi
 gn\, and a software framework for facilitating rapid system analysis.\n
LOCATION: Cambridge University Engineering Department MIL meeting room
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