2026-09-19T15:43:45Zhttps://api.mountainscholar.org/server/oai/request

oai:mountainscholar.org:10217/2427752026-01-21T15:24:02Zcom_10217_100532com_10217_100000com_10217_100357com_10217_100303col_10217_182111col_10217_100359

King, Connor, author Peccoud, Jean, advisor Munsky, Brian, committee member Khakhar, Arjun, committee member Geiss, Brian, committee member Wilusz, Jeffrey, committee member 2026-01-12T11:29:37Z 2025 King_colostate_0053A_19343.pdf https://hdl.handle.net/10217/242775 https://doi.org/10.25675/3.025667 Oncolytic virotherapy is a promising cancer treatment that uses viruses to selectively kill cancer cells. Historically, viral design has been largely qualitative, disrupting genes that help viruses evade the immune system and hijack cellular machinery and inserting pro-inflammatory genes. Here, I present a quantitative framework for tuning gene expression in common oncolytic viruses. First, I introduce a model that predicts viral replicative fitness as a function of transcription rates. I then propose a transcription mechanism in these viruses that enables more precise control to further optimize desirable phenotypes. Generating viruses from plasmids typically relies on many plasmids (3-8). Ensuring that every plasmid enters the same cell and expresses its gene is inherently inefficient. I revisit the assumption that multiple small plasmids are more efficient than a few larger ones. I show that plasmid entry rates are effectively size-independent across ~5 to 16 kb, that lipoplexes co-deliver only limited multi-plasmid cargo, and that consolidating helper genes onto single, larger plasmids increases both the probability of co-expression and the correlation between expressed genes. Finally, using a spatial agent-based tumor model, I demonstrate that maximizing infectivity in cancer cells alone is insufficient for tumor clearance in stroma-rich tissues. Granting the virus calibrated ability to infect stromal cells markedly improves penetration and overall control, revealing a therapeutic window for partial stromal targeting. Together, these results shift oncolytic virus design from trial-and-error to principled optimization. By mapping transcription to fitness, streamlining the assembly of these viruses, and accounting for tumor microenvironment barriers, this work provides design rules for future oncolytic virotherapies. born digital doctoral dissertations English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Embargo expires: 01/07/2028. stochastic modeling oncolytic virotherapy Toward model-driven development of viral therapies Text Image 2028-01-07 2028-01-07 This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). Doctor of Philosophy (Ph.D.) Doctoral Biomedical and Chemical Engineering Colorado State University embargo