MS03 - MFBM-17

Immune Digital Twins: Mathematical and Computational Foundations (Part 1)

Tuesday, July 15 at 10:20am

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Note: this minisymposia has multiple sessions. The other sessions are: Part-2, and Part-3.

Tomas Helikar (University of Nebraska - Lincoln), Juilee Thakar (Juilee_Thakar@URMC.Rochester.edu) - University of Rochester Medical Center James Glazier (jaglazier@gmail.com) - Indiana University

Description:

Immune Digital Twins (IDTs), virtual models of the human immune system, coupled with periodic real-world data, are a growing focus of precision medicine. Designing control interventions to regulate the immune system requires real-time evaluation of high-dimensional parameter spaces of possible interventions in real-time and the ability to continuously recalibrate models of a changing patient. Because of the sparsity and lack of accuracy of the available experimental data feeds, the lack of first-principles models, the intrinsic stochasticity of the underlying biology, and population heterogeneity building full IDTs presents numerous challenges in model design, parameter identification (in complex agent-based models), uncertainty quantification, forecast deployment and data assimilation for model refinement. Another critical aspect of the development of IDTs is the integration of AI/ML methods with mechanistic models in a variety of roles. Due to the rapidly developing field, we would like to propose and request time for a 5-hour mini-symposium that will focus on modeling and mathematical challenges and achievements related to IDTs. We have secured 10 speakers across disciplines and career stages to cover a wide range of topics, including the use of IDTs in the control of sepsis, respiratory infections, and cancer immunotherapies and approaches to multiscale model construction and parameterization, addressing the aforementioned challenges.

Elsje Pienaar

Purdue University

"Patient-specific Immuno-profiles in Mechanistic Models: CD8+ T cell Exhaustion in children with perinatal HIV"

We and others have reported evidence of T cell exhaustion in children with perinatal HIV with increased expression of inhibitory receptors PD-1, CD160, and TIM-3, but there is limited data on the virologic functional consequences of this immune exhaustion. We address this by using an immune database from Kenyan children with perinatal HIV and unexposed controls. We computationally integrate T cell profiles of differentiation, activation and exhaustion in an agent-based model (ABM) to predict how T cell exhaustion impacts viral control following HIV exposure in vitro. Our ABM includes macrophages, CD4 and CD8 T cells, cytokines, and HIV. Model mechanisms include viral dynamics, macrophage activation, T cell activation and proliferation, cytotoxic T cell killing, and cytokine/HIV diffusion and degradation. Participants are grouped by HIV plasma viremia and by age, less than 5 years or 5-18 years. Our findings indicate that cells from virally active participants, who have the highest levels of exhaustion, have lower predicted viral concentrations and infected cells compared to other participant groups during new infection. However, this coincides with higher cell death, suggesting that short-term viral control is associated with excessive inflammation, which could be detrimental long-term. Cells from virally suppressed participants older than 5 years can maintain lower viral concentrations while limiting cell death, reflecting a more sustainable short-term immune response. In virally suppressed children younger than 5 years, immune response patterns strongly resemble the age-matched healthy control group, suggesting early viral suppression may preserve antiviral immune responses. Our model predicts unique patterns of cell death for each participant group, with CD8 T cell death being dominant in virally active groups and CD4 T cell and macrophage death being dominant in healthy and virally suppressed groups. Finally, exhausted CD8 T cells are predicted to contribute significantly to CD8 T cell killing, proliferation, and activation in the virally active group, indicating partially functional CD8 T cells can still contribute to short-term viral control. Our analysis functionally integrates participant-specific immunophenotypic data to allow quantification of the extent, mechanisms, and impact of immune dysfunction in perinatal HIV and could inform pediatric HIV remission and cure strategies.

James A. Glazier

Indiana University, Bloomington

"Medical Digital Twins: Addressing Simulation Equivalence Challenges in Virtual-Tissue Models"

Developing closed-loop Medical Digital Twins requires multiple tools—both physical (sensors/actuators) and computational—to support the cycle of measurement, forecasting, divergence assessment, anomaly detection, data assimilation, and action selection. While significant progress has been made in predictive modeling and data assimilation, comparing simulation states presents unique challenges, particularly for agent-based spatial Virtual-Tissue models. When working with scalar quantities like blood oxygenation, comparing measured and forecast values is straightforward. However, for Virtual-Tissue models, determining whether two simulation states derive from the same underlying model becomes complex. Implementation differences across software frameworks create substantial numerical variations (inter-simulation variability), while stochasticity within single implementations produces multiple potential phenotypes (intra-simulation variability). To address these reproducibility and interoperability challenges, I present three methodologies for determining simulation state equivalence despite phenotypic differences: 1) A neural-network image classifier that learns features of equivalent model configurations robust to both intra- and inter-simulation variability. This classifier also supports developing generative AI surrogates of mechanistic agent-based models for Medical Digital Twin applications. 2) AI/ML approaches to cluster and classify synthetic images generated by agent-based models of cell sorting and angiogenesis. 3) Leveraging the classification techniques to solve the inverse problem of inferring model parameters from images, enabling parameter identification in complex systems. The presentation concludes with proposed next steps for advancing these techniques in the Digital Twin ecosystem.

Hana Dobrovolny

Texas Christian University

"Incorporating the immune response into models of oncolytic virus treatment of cancer"

Oncolytic viruses present a promising path for cancer treatment due to their selectivity in infecting and lysing tumor cells and their ability to stimulate the immune response. While the immune response can help eliminate the tumor, it also acts to clear the virus and often limits the effectiveness of oncolytic virus therapy. Using experimental data, we test models of oncolytic virus infections incorporating various immune components in order to determine the most suitable immune models. We use the models to investigate the role of the immune response in oncolytic virus treatment, finding that a moderate immune response can prolong the oncolytic virus infection, allowing the virus to infect and kill more tumor cells than either a weak or strong immune response.

Jason E. Shoemaker

University of Pittsburgh

"Network representation of sex-specific immunity: A steppingstone to digital twins?"

In a world of immense and growing computational power, the eventual rise of Digital Twins will enable a degree of personal health optimization that is currently unimaginable. There are important questions on how society gets there, the ethics of owning one’s digital twin, and many more important questions to address as we progress towards the Digital Twin world. One small but important question in the short term is how we can use currently available tools to design personal treatments today or guide drug discovery. In our lab, we have leaned heavily on using molecular interaction networks as baseline models of human gene regulation. We have both independently and with colleagues developed new algorithms that can integrate interaction data and gene expression data to predict either drug mechanisms of action or pathways for suppressing respiratory virus replication. Now, we are using these tools to explore for antiviral drug targets that are sex-specific, meaning proteins that, when targeted, may help regulate virus replication specially in male or females. And we are extending these studies to determine what roles hormones may play as well. Here, we will discuss our early results wherein we have analyzed primary human nasal cells from male and female donors. Our early results show that network-based representations of gene regulation better isolate hormone regulated pathways, including inflammation pathways important to respiratory infection. With sufficient data, network-based approaches combined with machine learning may be a promising approach developing early digital twins that are relevant to respiratory infection.

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Annual Meeting for the Society for Mathematical Biology, 2025.