Math Modeling Seminar: Blood Digital Twins Developed Using Dynamic State Modeling of Single-Cell RNA-seq Data
Math Modeling Seminar
Blood Digital Twins Developed Using Dynamic State Modeling of Single-Cell RNA-seq Data
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Dr. Pancy Thein Lwin
Postdoctoral Researcher
University of Rochester Medical Center
Abstract:
The development of blood digital twins represents a critical step toward personalized medicine and in-silico clinical trials. Here, we present a dynamic modeling framework that leverages single-cell RNA-seq data to construct immune digital twins through Boolean network-based State Transition Graphs (STGs). By modeling intracellular signaling dynamics, we identify dominant attractors that represent stable cellular states and track pseudotime progression across disease-specific trajectories. Cells are clustered based on attractor similarity, spatial proximity in reduced dimensions, and alignment with clinical phenotypes of atherosclerosis (e.g., AS+ vs AS−) in people living with HIV. Integrating dynamic signaling models with pseudotime inference enables estimation of transition probabilities and identification of key driver genes shaping disease fate. In silico perturbation experiments reveal gene targets that significantly shift disease trajectories, offering insights into therapeutic vulnerabilities. This personalized modeling approach builds upon the scBONITA (single-cell Boolean Omics Network Invariant-Time Analysis) framework from Palshikar et al., to generate patient-specific profiles that summarize attractor landscapes, driver genes, and immune dynamics. Looking forward, expanding this framework to encompass broader system-level processes will bring us closer to implementing virtual clinical trials, transforming the future of drug development and precision immunotherapy. This talk will also discuss the trending concept of digital twins in biomedical research and their growing relevance for mathematical and computational biology modelers.
Bio: Dr. Pancy Lwin earned her PhD in Mathematical Modeling from the Rochester Institute of Technology, where she was advised by Dr. Moumita Das. She is currently a postdoctoral researcher in Dr. Juilee Thakar’s lab at the University of Rochester Medical Center, working at the intersection of immunology, computational biology, and bioinformatics. Her journey into computational biology began during her graduate school years, where she discovered her passion for applying mathematics to tackle complex biological problems from the perspectives of biophysics, pharmacology, and immunology. Emerging from her diverse modeling interests are topics such as model-informed drug development and digital twins. Outside of science, she enjoys running half-marathons at a leisurely pace, hiking, paddling, and cooking Burmese cuisine.
Intended Audience: Beginners, undergraduates, graduates. Those with interest in the topic.
Interpreters have been requested.
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This is an RIT Only Event
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