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Developing a Dynamic Model of the DNA Lab Process - Archival
This webinar detailed the development of a model that synthesizes, in silico, allele, stutter and noise signal generated from stochastic variation induced by the forensic DNA laboratory process.
This webinar detailed the development of a model that synthesizes, in silico, allele, stutter and noise signal generated from stochastic variation induced by the forensic DNA laboratory process.  The model described herein allows rapid in silico simulation of electropherograms from multi-contributor samples which enables detailed investigations of involved scenarios. An implementation of the model in the StellaTM environment that is parameterized by extensive laboratory data is freely and publicly available. To illustrate its utility, we presented data demonstrating the effects of sample dilutions, of injection time and cycle number on peak height, and the nature of stutter ratios at low template. We verified the model’s findings by comparison with experimentally generated data, illustrating its utility.

This webinar was recorded in its entirety at the time of the live event in order to capture the one on one interaction with the presenter.

Speaker

Catherine M. Grgicak, Ph.D.
Assistant Professor, Biomedical Forensic Sciences, Boston University School of Medicine
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