Dalal, Rishad2025-02-142025-02-142022-07https://hdl.handle.net/11299/269985University of Minnesota Ph.D. dissertation. July 2022. Major: Chemistry. Advisor: Theresa Reineke. 1 computer file (PDF); xxvii, 271 pages.The field of gene therapy has grown in response of the millions of people who suffer from genetic diseases worldwide. As genetic payloads need a delivery carrier, cationic polymeric vectors have grown in promise as delivery vehicles that are more cost-effective, scalable, and stable in comparison to viral vectors. The field of polymeric gene delivery has focused on improving delivery efficiency through chemical and structural modifications. Herein, we have made steps towards understanding how architectural modifications and how structure property relationships can improve the field of gene delivery. Initially it was found that when comparing cationic homopolymers, a bottlebrush architecture outperformed a linear analog in over pDNA delivery efficacy. Follow-up studies explored how bottlebrush end-group hydrophilicity can play a role in balancing colloidal stability, gene expression, and cellular viability. In addition to architectural understanding, studies to understand how structure-property relationships within linear polymers were explored in which a combinatorial library of 36 polymers was synthesized and used to deliver pDNA and CRISPR-Cas9 RNP. Machine learning aided in optimizing and analyzing structural relationships relative to expression outputs. Overall, we were able to create guides in improving gene expression through the optimization of polymer macromolecular structure and unique chemical understanding per biological payload.enBottlebrushCRISPRDeliveryDNAPolymerSynthesisThesis or Dissertation