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.

Quantum thermodynamics deals with thermodynamic effects and thermodynamic constraints (e.g. the 2nd law) that emerge in out-of-equilibrium microscopic open quantum systems, and in microscopic heat machines. Presently, the technology developed for quantum computing is sufficient for exploring quantum thermodynamic experimentally (new experimental results will be shown). On top of the second law, thermodynamic resource theory predicts additional mathematical constraints on thermal transformation of microscopic systems. Unlike the second law, these constraints cannot be related to thermodynamic observables. Consequently, they are useful for some theoretical purposes, but not for making concrete predictions on realistic scenarios. In this talk I will present a new formalism that yields additional “seconds laws” that follow the logic and structure of the standard 2nd law. While the 2nd law deals with the first moment of the energy (average heat, average work), the observables in the new laws are higher moments of the energy. I will show several scenarios where these laws provide concrete answers to “blind spots” that are not addressed by the standard 2nd law. In other cases tighter bounds are obtained compared to the standard 2nd law. Potentially, this formalism can significantly extend the thermodynamic framework, and put additional practical bounds on thermal transformations and microscopic heat machines. Finally, I will discuss the connection to quantum coherence measures and list several research directions.

Williams syndrome (WS) is a neurodevelopmental disorder caused by a heterozygous microdeletion of about 26 genes from chromosomal region 7q11.23, characterized by hypersociability and unique neurocognitive abnormalities. Of those deleted, general transcription factor II-i (Gtf2i), has been shown to affect hypersociability in WS, although the cell type and neural circuitry critical for the hypersociability are poorly understood. To dissect neural circuitry related to hypersociability in WS and to characterize the neuron-autonomous role of Gtf2i we conditionally knockedout Gtf2i in forebrain excitatory neurons and found this recapitulate WS features, including increased sociability and anxiety and neuroanatomical defects. Unexpectedly, we found that in the mutant mouse cortex 70% of the significantly downregulated genes were involved in myelination, together with a reduction in mature oligodendrocyte cells number, disrupted myelin ultrastructure and fine motor deficits. Analyzing the transcriptome in human frontal cortex, we found similar downregulation of myelination-related genes, suggesting a novel pathophysiological mechanism in WS, based on neuron-oligodendrocytes signaling deficits. Overall, our data detail the cellular processes that may lead to the WS typical phenotype and developmental abnormalities, and suggest new paths to explore and treat WS, as well as social and cognitive abnormalities.