MS07 - MFBM-17

Immune Digital Twins: Mathematical and Computational Foundations (Part 3)

Thursday, July 17 at 4:00pm

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Note: this minisymposia has multiple sessions. The other sessions are: Part-1, and Part-2.

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.

Juilee Thakar

University of Rochester

"Monocyte digital twin and HIV associated vascular disease"

People living with HIV (PLWH) continue to show a heightened risk for atherosclerosis (AS) even under effective antiretroviral therapy (ART). Monocytes are key drivers of AS pathogenesis. They can directly contribute to lesion formation by differentiating into lipid-laden macrophages (foam cells) in the arterial intima. Indirectly, their persistent immune activation and secretion of inflammatory cytokines support chronic inflammation, a hallmark of HIV-associated vascular disease. Because monocytes continuously replenish the macrophage pool in the vessel wall, they represent an important early predictor of AS progression in HIV. To investigate this, we performed single-cell transcriptomic profiling of 138,487 circulating monocytes from four well-matched participant groups—HIV-AS−, HIV-AS+, HIV+AS−, and HIV+AS+—stratified by age, sex, and Reynolds cardiovascular risk score. We identified eight transcriptionally distinct monocyte subsets, including canonical CD14+ cells and a previously undescribed population characterized by platelet interaction, referred to as platelet-monocyte complexes (PMCs). We used Boolean Omics Network Invariant Time Analysis (BONITA) developed in our group to identify pathway specific stable cellular states and their basin of attraction. Using these cellular states we have defined monocyte digital twins that predict the AS pathogenesis.

Esteban Hernandez Vargas

University of Idaho

"Adaptive Observers in Digital Twins for Drug Resistance Mitigation in HIV"

High mutation rates in HIV pose a significant challenge for long-term therapy, as the virus can quickly develop resistance to specific antiretroviral drugs. Despite extensive research, there remains no clear consensus on how to schedule treatments to maintain viral suppression and mitigate resistance optimally. In this talk, I present a digital twin framework for modeling HIV mutation dynamics, employing an adaptive observer to approximate a surrogate of a higher-order nonlinear mutation model. This approach enables us to monitor and anticipate the emergence of drug-resistant strains in silico, providing a foundation for exploring adaptive treatment strategies. Preliminary simulation results indicate that this computational framework can outperform standard clinical scheduling recommendations, offering a more individualized and responsive alternative to conventional therapy. This work represents a step toward leveraging digital twins to support clinical decision-making in the treatment of complex, mutating viral infections. Funding: This research was supported by the National Science Foundation grant DMS -2315862.

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.

Miriam Rafailovich

"Molecular level modeling of the immune response and thrombosis following viral infection"

Abstract to be determined. Please check back later.

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Annual Meeting for the Society for Mathematical Biology, 2025.