Program

Abstract:
As transistors near the size of molecules, computer engineers are increasingly finding themselves asking a once idle question:
How can we compute using chemistry?
In this talk, I will discuss recent progress my Brown Molecular Informatics team and I have made demonstrating how mixtures of small, unordered molecules can process information. During the first portion of this talk, I will illustrate how combinatorial chemical synthesis combined with high resolution mass spectrometry can be harnessed to store GBs of information in small molecules and metabolites.
I will then turn to describing how basic principles of chemistry, such as mixing, acid/base complementarity, and autocatalysis, can be exploited to realize fully molecular neural networks for machine learning and image processing.
I will end with a discussion of the challenges molecular computation faces that may be resolved with clever doses of synthetic and theoretical chemistry, and the connections molecular computation has to the field of systems chemistry.

Abstract:
The rapid growth of digital data generation requires a massive amount of storage media for much longer periods of time. For the supply of storage to keep pace with the burgeoning demand, the industry needs a new storage medium that is denser, more durable, sustainable, and cost effective to cope with the expected future growth.
DNA data storage is an attractive option among the new media because of its density and longevity. The advances in synthetic biology, DNA sequencing technology, and software and workflow solutions for data manipulation will soon make DNA data storage cost competitive with conventional media. It also has the potential to enable fundamentally new ways of using storage and in situ compute within a DNA archive.
In this presentation, a member of the DNA Data Storage Alliance will discuss opportunities and challenges for DNA data storage solutions to be commercially deployed as a part of the storage ecosystem. We will also analyze a few vertical markets such as Digital Preservation, Government, and Advanced Driver Assistance Systems and a set of user parameters, including capacity, Total Cost of Ownership (TCO), access frequency, and performance.

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.