Department of Systems & Computational Biology

Seminars

RECRUITMENT SEMINAR 

  

"Free energy, optimal control, and optimal response in microscopic non-equilibrium systems 

  

David Sivak, Ph.D. 

Systems Biology Fellow, University of California, San Francisco    

 

Wednesday, January 29, 2014 

Price Auditorium 

10:30am    

  

Abstract:  Molecular machines are protein complexes that convert between different forms of energy, and they feature prominently in essentially any major cell biological process. A plausible hypothesis holds that evolution has sculpted these machines to efficiently transmit energy and information in their natural contexts, where energetic fluctuations are large and non-equilibrium driving forces are strong. Toward a systematic picture of efficient, stochastic, non-equilibrium energy and information transmission, I present theoretical developments in three distinct yet related areas of non-equilibrium statistical mechanics: How can we measure how far from equilibrium a driven system is? How do we find efficient methods to push a system rapidly from one state to another? And finally, what are generic properties of systems that efficiently harness the energy and information present in environmental fluctuations? For further details: http://davidsivak.com/  

  

RECRUITMENT SEMINAR 

 

 

"Cell type-specific genomics of Drosophila neurons 

  

Fred Davis, Ph.D. 

Postdoctoral Associate, HHMI Janelia Farm      

 

Wednesday, February 19, 2014


Price Auditorium

10:30am 

  

Abstract:  The diversity of gene expression across cell types is particularly striking in the myriad cell types of the nervous system. While most genomic methods are typically applied to whole tissues, new technologies have started to make these methods applicable to individual cell types, including neurons in the brain. I will begin by describing the first cell type-specific gene expression and histone modification profiles measured from distinct neuronal subpopulations in the Drosophila brain. In addition to recovering known gene expression differences, these profiles indicate significant cell type–specific chromatin modifications. In particular, a small subset of differentially expressed genes exhibits a striking anti-correlation between repressive (H3K27me3) and activating (H3K27ac) histone modifications. These genes are enriched for transcription factors, recovering known and predicting new regulators of neuronal identity. I illustrate the utility of this chromatin pattern by demonstrating that it can be used as a genome-wide screen in mammalian systems to significantly enrich for transcription factors that can convert adult cell identity. I will close by describing the utility of cell type-specific genomic profiling in the context of neural circuits, presenting expression profiles measured from the first neuropil of the Drosophila visual circuit. Our results suggest that cell type-specific profiling of neuronal populations can illuminate how these neurons develop and function in the adult brain.  

  

RECRUITMENT SEMINAR 

 

 

"Using regulatory genomics to decipher disease genetics and evolutionary dynamics 

  

Lucas Ward, Ph.D. 

Postdoctoral Associate, Computer Science and Artificial Intelligence Laboratory, MIT   

    

Wednesday, February 26, 2014 


Price Auditorium

10:30am 

 

  

Abstract:  Association and linkage studies provide genome-wide information about the genetic basis of complex disease, but medical research has focused primarily on protein-coding variants, owing to the difficulty of interpreting noncoding mutations. This picture has changed with advances in the systematic annotation of functional noncoding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs and molecular quantitative trait loci all provide complementary information about the regulatory function of noncoding sequences. I will first discuss problems in deciphering the transcriptional regulatory code, and work I have done in model organisms to studying the interplay between chromatin, regulatory motifs, and evolutionary turnover. I will then discuss regulatory genomics modeling in in human using large compendia of epigenomic maps, which has allowed us to generate hypotheses about which variants on disease haplotypes are causal, to perform systems-level analyses which reveal regulatory pathways underlying complex phenotypes, and to detect lineage-specific purifying selection through aggregated patterns of human diversity. Finally, I will discuss how these models pave the way to interpret mutations found through clinical whole-genome sequencing and to perform rare-variant association studies, and how they will let us better understand our evolutionary history.  

 

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