Insights on Serology, CRISPR Diagnostics, and Machine Learning Architectures for Biological Sequences

Fueled by technological breakthroughs, advancements in our understanding of infectious agents offer unprecedented potential for their early detection, intervention, and ultimately, eradication. This dissertation focuses on combining cutting-edge immunological, diagnostic, and computational approaches to confront infectious diseases more effectively, with a particular emphasis on SARS-CoV-2. The first two chapters delve into the immunological aspects of SARS-CoV-2, exploring the dynamics of antibody responses during primary infection and reinfection. First, we explore the dynamics of antibody responses during primary infection, revealing a “switch-like” relationship between antibody titer and function. Next, we investigate the humoral immune response following reinfection, identifying specific biomarkers that differentiate between primary infection and reinfection, offering potential tools for monitoring disease spread and understanding immunity. The subsequent chapter shifts focus towards technological innovation in diagnostics, presenting a novel bead-based method for CRISPR diagnostics that leverages a split-luciferase reporter system for enhanced sensitivity and a highly deployable bead-based platform for multiplexed pathogen detection. This work represents a significant advancement in rapid, scalable, and portable diagnostic tools. Finally, the dissertation culminates with a leap into computational biology, introducing ’Janus,’ a subquadratic state space model designed to efficiently handle large biological sequences. Janus demonstrates superior performance in genomics and proteomics tasks, outperforming existing models with significantly fewer parameters, thus paving the way for more efficient and accurate modeling of protein behavior and other biological processes. Collectively, these works contribute to the broader field of infectious disease research with new immunological insights paired with advances in technological and computational solutions.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Computational and Systems Biology Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Siddiqui, Sameed Muneeb

Advisors dc:contributor.advisor

• Sabeti, Pardis
• Collins, Jim

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/157162

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/157162