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SynBio Forum: Genetics, Vision and Machine Learning in Biological Systems

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Join us for our termly SynBio Forum! We’ll be exploring microscopy-image analysis and machine learning approaches in biology with Ricardo Henriques (UCL) and Brenda Andrews (University of Toronto). The talks will be followed by a dinner buffet and drinks reception. Reserve your spot today!

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SCHEDULE

5:30-6:15pm: Ricardo Henriques (UCL) + Q&A

6:15-7:00pm: Brenda Andrews (Univ. of Toronto) + Q&A

7:00pm onwards: Dinner buffet + drinks reception

There will also be a showcase of projects created through the Biomaker Winter Challenge – a computing challenge at the intersection of biology, engineering and computer science. (https://www.biomaker.org/)

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DEMOCRATISING LIVE -CELL HIGH -SPEED SUPER RESOLUTION MICROSCOPY

Dr. Ricardo Henriques (UCL)

Abstract: Dr. Ricardo Henriques established his lab at the MRC Laboratory for Molecular Cell Biology, UCL to undertake research combining cell biology, optical physics and biochemistry. His group focuses on biological problems that cannot be addressed with current imaging technology, and thus aims to develop analytical, optical and biochemical approaches to address these questions.

In cell biology they aim to understand how viruses enter cells by probing and remodelling 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 Super-Resolution (SR) methods and computational modelling approaches that, although designed to answer questions of interest in the lab, will have broad applications in cell biology research.

Ricardo and his lab have developed robust fluidics approaches to automate complex sequences of treatment, labelling and imaging of live and fixed cells. Their open-source NanoJ-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 reproducibility.

MACHINE LEARNING AND COMPUTER VISION APPROACHES FOR PHENOTYPIC PROFILING IN YEAST

Dr. Brenda Andrews (University of Toronto)

Abstract: A powerful method to study the genotype-to-phenotype relationship is the systematic assessment of mutant phenotypes using high-content screening and automated image analysis. We have developed a combined experimental-computational pipeline for analysis of the effect of genetic perturbations on subcellular compartments in yeast. Our approach involves using Synthetic Genetic Array (SGA) analysis, which automates yeast genetics, to introduce markers of various subcellular compartments into yeast mutant arrays, in order to identify comprehensive lists of genes involved in subcellular morphology. Quantitative analysis of these large image datasets requires computational approaches such as image recognition, feature extraction and machine learning.

We have developed a general computational pipeline for single cell image analysis to quantify penetrance of perturbations affecting the sub-cellular morphology of 18 sub-cellular compartments. To develop the pipeline, we first focused on surveying the yeast genome for genes required for proper formation and maintenance of the early, intermediate and late endocytic compartments. This analysis revealed that mutation of 13% of the screened genes caused a morphological phenotype with a penetrance of 50% or greater for at least one of the four screened markers. Mutation of hundreds more genes, mostly connected to more distant bioprocesses, caused moderate but still significant defects in at least one of the major compartments involved in endocytosis. This analysis will allow for the identification of connections between biological processes, the prediction of novel gene function, and the generation of a clearer understanding of basic eukaryotic cell biology.

This talk is part of the Synthetic Biology Strategic Research Initiative series.

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