UC Berkeley

Malaria, dengue, Zika, and other mosquito-borne diseases continue to pose a major global health burden through much of the world, despite decades of work combating the mosquito vector and the virus or parasite responsible for each disease. The advent of CRISPR/Cas9-based gene editing greatly simplifies design and implementation of novel genetic-based approaches for vector control. This has reignited interest in gene drive technologies, requiring an understanding of the population and spatial dynamics of the wide range of novel gene drive architectures in development.

This thesis traverses the path from design, to understanding, to prediction of the dynamics for novel gene drive constructs. Chapter 1 develops and extends simulation frameworks for exploring the dynamics of gene drives. The first half enhances the field's genetic and geo-spatial simulation capabilities, while the second half builds upon that to utilize time-varying properties applied with a continuous-time framework. The outcome of this chapter is two expansive and computationally-efficient software packages freely available in the R ecosystem.

Chapter 2 acknowledges our limited knowledge of factors that impact the success of different gene drive constructs, and in conjunction with the biological design and testing of novel constructs, develops a robust inference methodology to understand and quantify the mechanisms underpinning gene drive behavior. Chapter 2 begins with a standard MCR design targeting a non-essential gene. We then expand our approach for treatment of split-gene designs that target essential genes with different (pre or post-zygotic) fertility impacts. All of this work is done in a manner consistent with the simulation frameworks from chapter 1.

Finally, chapter 3 applies the models developed in chapter 1, while integrating knowledge gained in chapter 2, to forecast the dynamics of innovative gene drive constructs in realistic settings. The chapter begins with a large-scale exploration of two classic designs, now generated using a CRISPR-based approach, to query the chance of ``success", any dangers of ``success", and repercussions if ``success" was not the correct goal. The chapter finishes with a recent design that seeks to bridge the space between linked and split-gene drive designs, seeking to combine the benefits of both ideas. It is a fitting conclusion to a project that spans math and biology to generate a mechanistic and predictive understanding of modern gene drives.