Toward model-driven development of viral therapies

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.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Biomedical and Chemical Engineering

Grantor dc:publisher

Colorado State University. Libraries

Year dc:date.issued

2025

Authors dc:creator

• King, Connor, author
• Peccoud, Jean, advisor
• Munsky, Brian, committee member
• Khakhar, Arjun, committee member
• Geiss, Brian, committee member
• Wilusz, Jeffrey, committee member

• stochastic modeling
• oncolytic virotherapy

Statement dc:rights

• 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.

Language dc:language.iso

eng, English

Identifier URI

https://doi.org/10.25675/3.025667

OAI record oai:identifier

https://api.mountainscholar.org/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Amountainscholar.org%3A10217%2F242775