Integrated Calendar

Abstract:
In the last decade, the human population has produced zettabytes (10^21) of digital data. This creates immense opportunities and challenges for biology research. In this talk, I will present two research directions of my groups on the intersection between genetics and data, which we dub “genetic media”.

First, I will speak about crowd sourcing massive genetic data using social media. We collected over 80 million profiles from the largest social-media website driven by genealogy and constructed a single family tree of 13 million people. Using this data, we analyzed the genetic architecture of longevity. I will also speak about our on-going efforts to crowd source genomes and social media phenotypes to this massive pedigree.

In the second part of my talk, I will present using synthetic DNA as a medium for long-term data storage. Previous studies in leading journal have presented this concept but failed to show reliable data retrieval. Here, we report a storage strategy, called DNA Fountain, that is highly robust and approaches the Shannon limit. The success of our strategy relies on careful adaptation of coding theory to the domain-specific constraints of DNA molecules. To demonstrate its power, we stored a full computer operating system, movie, and other files in DNA oligos and perfectly retrieved the information. We explored the limit of our architecture in terms of bytes per molecules and obtained a perfect retrieval from a density of 215Petabyte/gram of DNA, orders of magnitudes higher than previous techniques.

Abundance of recently obtained datasets on brain structure (connectomics) and function (neuronal population activity) calls for a normative theory of neural computation. In the conventional, so-called, reconstruction approach to neural computation, population activity is thought to represent the stimulus. Instead, we propose that the similarity of population activity matches the similarity of the stimuli under certain constraints. From this similarity matching principle, we derive online algorithms that can account for both structural and functional observations.

Bio: Dmitri "Mitya" Chklovskii is Group Leader for Neuroscience at the Simons Foundation's new Flatiron Institute in New York City. He received a PhD in Theoretical Physics from MIT and was a Junior Fellow at the Harvard Society of Fellows. He switched from physics to neuroscience at the Salk Institute and founded the first theoretical neuroscience group at Cold Spring Harbor Laboratory in 1999, where he was an Assistant and then Associate Professor. From 2007 to 2014 he was a Group Leader at Janelia Farm where he led a team that assembled the largest-ever connectome. His group develops software for experimental data analysis and constructs normative theories of neural computation.