Transitions: Rational design of viral vectors

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The ability to use genetic data to accurately predict the attributes of living organisms would have a profound impact on many fields of biology, including genetics, molecular biology, synthetic biology and biomedical engineering. One goal of this project is to develop approaches for genotype-phenotype predictions using a simple viral system (Vesicular Stomatitis Virus). Additional project goals are to allow the PI, who is a well-respected computational biologist, to gain hands-on experience using several crucial experimental techniques and to establish a research program that has experience in both experimentation and modeling. This project will also provide research opportunities for many undergraduate and graduate students, including students from underrepresented groups. The overall goal of the project is to build a multiscale model showing how fluctuations of discrete events at the molecular level determine the quantitative virulence of a virus population. This model will be developed as a genotype to phenotype map that captures the relationships between genotype and phenotype observed in a library of 9,600 engineered vesicular stomatitis virus variants. This model will establish how the genetic control of the transcription and translation of the vesicular stomatitis virus five genes can determine viral propagation in cell culture without introducing mutations that alter the function of the proteins coded by the viral genome. The model derived from experimental data will guide the design of optimized vectors that combine a low-virulence and a high-degree of reproducibility. The project will proceed through iterations of the Design-Build-Test-Learn cycle that will allow the characterization of 96 virus variants per month by the end of the performance period. Libraries of virus variants will be designed and assembled before being transfected in cell cultures. They will be tested by flow cytometry. Experimental data will be used to refine the mathematical model of viral replication by adding new data to that collected in previous iterations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.