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Defense: Stochastic Modeling and Analysis of DNA Polymerase Kinetics Based on Observed Dwell Times

Speaker Name: 
George Labaria
Speaker Title: 
PhD Candidate (Advisor: Hongyun Wang)
Speaker Organization: 
Applied Mathematics
Start Time: 
Friday, May 24, 2019 - 11:00am
End Time: 
Friday, May 24, 2019 - 1:00pm
Engineering 2, Room 599
Hongyun Wang

Abstract:  DNA Polymerases (DNAPs) are enzymes that make DNA molecules by assembling nucleotides and are responsible for copying the genome in all cells. Fidelity in genome replication is essential for genome integrity. Replication errors could lead to mutations which lead to diseases, including cancer. DNAPs selectively bind a deoxyribonucleoside triphosphate (dNTP) that is complementary to the template nucleotide of the DNA they are copying. After the covalent incorporation of a complementary nucleotide, the DNAP moves onto the next template nucleotide in the translocation step driven by thermal fluctuations, allowing for a new round of binding. The binding and incorporation of a nucleotide, along with the translocation step, consist of a full nucleotide addition cycle. Nanopore experiments allow us to observe the DNAP translocation step with single-nucleotide spatial precision and millisecond temporal resolution. We develop methods to infer the kinetic details of the nucleotide addition cycle from dwell time data obtained from the nanopore experiments. We fully characterize the uncertainty in the inferred kinetic details, and show that the uncertainty can be controlled in experimental design. We show that a dimensionless quantity based on the randomness parameter provides a lower and upper bounds on the number of biochemical states in the polymerization (pol) step of a replication cycle. Understanding the kinetic details of the nucleotide addition cycle is essential to elucidating the mechanisms which regulate fidelity. The inference methods we developed can be applied to other single molecule experiments in which dwell time samples are observable. More importantly, the analysis results and methods for designing optimal experimental conditions will motivate more meaningful and informative single molecule measurements.