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SUMMARY:Multi-objective optimisation algorithms to predict gene expression
  in biological models - Claudio Angione (University of Cambridge)
DTSTART:20140515T130000Z
DTEND:20140515T140000Z
UID:TALK52335@talks.cam.ac.uk
CONTACT:Advait Sarkar
DESCRIPTION:Metabolic engineering is a promising biotechnology approach wi
 th an increasing demand for mathematical models for accurate design purpos
 es. One of the main goals of metabolic engineering is to predict the best 
 environmental condition in which a bacterium has to grow in order to reach
  specific optimal output values from a range of objective functions chosen
  by the researcher. To this end\, I will show a multi-objective optimisati
 on algorithm I have developed to search for the gene expression values tha
 t optimise multiple cellular functions in biological models. \n \nLarge bi
 ological models usually involve steady-state analyses that make it possibl
 e to predict or investigate the behaviour of the biological entity being m
 odelled. These models are based on linear constraints and therefore are ab
 le to quickly simulate the behaviour of the organism. Flux Balance Analysi
 s (FBA) is a common constraint-based approach for simulating biochemical n
 etworks. It is able to estimate the flow of metabolites through the networ
 k and also the growth rate of the organism.\n\nThe multi-objective optimis
 ation is performed using a parallel optimisation algorithm based on a gene
 tic algorithm\, i.e. a robust technique inspired to the principles of evol
 ution and natural selection. This allows to maximise two or more objective
 s simultaneously. The algorithm also allows to fine tune the codon usage o
 f the bacterium in a multi-objective fashion. Coupled with gene-editing me
 thods\, this would allow to modify in vitro the bacterial genome such that
  the final gene expression is the one that optimises the objectives chosen
 .
LOCATION:LT2\, Computer Laboratory\, William Gates Building
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