Αγαπητά μέλη της πανεπιστημιακής κοινότητας,
Τη Δευτέρα, 12 Απριλίου, ώρα 7 μμ, θα πραγματοποιηθεί τηλεδιάλεξη με θέμα:
“DNA Computing for DNA Data Storage and Disease Diagnosis”
Προσκεκλημένος ομιλητής: Georg Seelig, Professor in Electrical & Computer Engineering and the Paul G. Allen School for Computer Science & Engineering at the University of Washington.
Η τηλεδιάλεξη πραγματοποιείται στο πλαίσιο του μεταπτυχιακού προγράμματος ‘Αναλυτική Χημεία & Νανοτεχνολογία’, καλύπτει διεπιστημονική ερευνητική δραστηριότητα και απευθύνεται σε ευρύ ακροατήριο.
Σύνδεσμος:
https://upatras-gr.zoom.us/j/96020984114?pwd=ZTJuaFRtdTRGOW9aaC9FbmJralZTUT09
Meeting ID: 960 2098 4114
Passcode: 254846
Ακολουθεί περίληψη της διάλεξης και σύντομο βιογραφικό του ομιλητή.
Abstract: In this talk, I will briefly introduce the field of DNA computing starting with work by Adleman. I will then highlight two different applications for DNA computing. First, I will introduce a molecular computation for disease diagnosis. Our workflow begins by training a computational classifier on labelled gene expression data. This in silico classifier is then realized at the molecular level to enable expression analysis and classification of previously uncharacterized RNA samples. Classification occurs through a series of molecular interactions between RNA inputs and engineered DNA probes designed to differentially weigh each input according to its importance. Second, I will introduce an approach for performing computation in the context of DNA data storage. Synthetic DNA has the potential to store the world's continuously growing amount of data in an extremely dense and durable medium. Current proposals for DNA-based digital storage systems include the ability to retrieve individual files by their unique identifier, but not by their content. Here, we demonstrate content-based retrieval from a DNA database by learning a mapping from images to DNA sequences such that an encoded query image will retrieve visually similar images from the database via DNA hybridization. We encoded and synthesized a database of 1.6 million images and queried it with a variety of images, showing that each query retrieves a sample of the database containing visually similar images are retrieved at a rate much greater than chance.
Short CV: Georg Seelig is a professor in Electrical & Computer Engineering and the Paul G. Allen School for Computer Science & Engineering at the University of Washington. He is an adjunct professor in Bioengineering. Seelig holds a PhD in physics from the University of Geneva in Switzerland and did postdoctoral work in synthetic biology and DNA nanotechnology at Caltech. He received a Burroughs Wellcome Foundation Career Award at the Scientific Interface in 2008, an NSF Career Award in 2010, a Sloan Research Fellowship in 2011, and a DARPA Young Faculty Award in 2012, an ONR Young Investigator Award in 2014 and a Microsoft Research Outstanding Collaborator Award in 2016.
Seelig is interested in understanding how biological organisms process information using complex biochemical networks and how such networks can be engineered to program cellular behavior. His approach combines forward engineering of synthetic RNA-based regulatory circuits with the quantitative characterization of existing RNA-based gene regulatory pathways. His group is applying engineered circuits to problems in disease diagnostics and therapy.
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