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SUMMARY:SynBio Forum: Genetics\, Vision and Machine Learning in Biological
  Systems - Brenda Andrews (University of Toronto)\, Ricardo Henriques (UCL
 )
DTSTART:20190212T173000Z
DTEND:20190212T193000Z
UID:TALK119365@talks.cam.ac.uk
CONTACT:Alexandra Ting
DESCRIPTION:(FREE) REGISTRATION & MORE INFORMATION: http://bit.ly/LentForu
 m\n\nJoin us for our termly SynBio Forum! We'll be exploring microscopy-im
 age analysis and machine learning approaches in biology with Ricardo Henri
 ques (UCL) and Brenda Andrews (University of Toronto). The talks will be f
 ollowed by a dinner buffet and drinks reception. Reserve your spot today!\
 n\n---\n\nSCHEDULE\n\n5:30-6:15pm: Ricardo Henriques (UCL) + Q&A\n\n6:15-7
 :00pm: Brenda Andrews (Univ. of Toronto) + Q&A\n\n7:00pm onwards: Dinner b
 uffet + drinks reception\n\nThere will also be a showcase of projects crea
 ted through the Biomaker Winter Challenge - a computing challenge at the i
 ntersection of biology\, engineering and computer science. (https://www.bi
 omaker.org/)\n\n----\n\nDEMOCRATISING LIVE-CELL HIGH-SPEED SUPER RESOLUTIO
 N MICROSCOPY\n\nDr. Ricardo Henriques (UCL)\n\nAbstract: Dr. Ricardo Henri
 ques established his lab at the MRC Laboratory for Molecular Cell Biology\
 , UCL to undertake research combining cell biology\, optical physics and b
 iochemistry. His group focuses on biological problems that cannot be addre
 ssed with current imaging technology\, and thus aims to develop analytical
 \, optical and biochemical approaches to address these questions.\n\nIn ce
 ll biology they aim to understand how viruses enter cells by probing and r
 emodelling membranes\, and what are the structural changes viruses undergo
  during cell-entry\, uncoating and morphogenesis. To do so\, the group is 
 developing new classes of fluorescent probes\, high-speed cell friendly Su
 per-Resolution (SR) methods and computational modelling approaches that\, 
 although designed to answer questions of interest in the lab\, will have b
 road applications in cell biology research.\n\nRicardo and his lab have de
 veloped robust fluidics approaches to automate complex sequences of treatm
 ent\, labelling and imaging of live and fixed cells. Their open-source Nan
 oJ-Fluidics system is based on low-cost LEGO hardware controlled by ImageJ
 -based software and can be directly adapted to any microscope\, providing 
 easy-to-implement high-content\, multimodal imaging with high reproducibil
 ity.\n\nMACHINE LEARNING AND COMPUTER VISION APPROACHES FOR PHENOTYPIC PRO
 FILING IN YEAST\n\nDr. Brenda Andrews (University of Toronto)\n\nAbstract:
  A powerful method to study the genotype-to-phenotype relationship is the 
 systematic assessment of mutant phenotypes using high-content screening an
 d automated image analysis. We have developed a combined experimental-comp
 utational pipeline for analysis of the effect of genetic perturbations on 
 subcellular compartments in yeast. Our approach involves using Synthetic G
 enetic Array (SGA) analysis\, which automates yeast genetics\, to introduc
 e markers of various subcellular compartments into yeast mutant arrays\, i
 n order to identify comprehensive lists of genes involved in subcellular m
 orphology. Quantitative analysis of these large image datasets requires co
 mputational approaches such as image recognition\, feature extraction and 
 machine learning. \n\nWe have developed a general computational pipeline f
 or single cell image analysis to quantify penetrance of perturbations affe
 cting the sub-cellular morphology of 18 sub-cellular compartments. To deve
 lop the pipeline\, we first focused on surveying the yeast genome for gene
 s required for proper formation and maintenance of the early\, intermediat
 e and late endocytic compartments. This analysis revealed that mutation of
  13% of the screened genes caused a morphological phenotype with a penetra
 nce of 50% or greater for at least one of the four screened markers. Mutat
 ion of hundreds more genes\, mostly connected to more distant bioprocesses
 \, caused moderate but still significant defects in at least one of the ma
 jor compartments involved in endocytosis. This analysis will allow for the
  identification of connections between biological processes\, the predicti
 on of novel gene function\, and the generation of a clearer understanding 
 of basic eukaryotic cell biology.
LOCATION:Old Divinity School\, St Johns Street\, Cambridge
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