MS07 - MFBM-17

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

Thursday, July 17 at 3:50pm

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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.

Heber L. Rocha

Indiana University

"Multiscale Modeling of Immune Surveillance for Cancer Patient Digital Twins"

Tumors are complex ecosystems characterized by heterogeneous cellular behaviors, intercellular interactions, and stochastic processes, which collectively challenge the development of personalized cancer therapies due to unpredictable therapeutic responses. Digital twins—computational representations of individual patients—offer a transformative approach to simulate and predict treatment outcomes, enabling precision oncology. This presentation describes an multiscale agent-based model, developed using the PhysiCell framework, to investigate immune surveillance in micrometastases, early metastatic clusters critical to cancer progression. Through high-throughput simulations of over 100,000 virtual patient trajectories, our model revealed a spectrum of outcomes, ranging from tumor proliferation to immune-mediated eradication. These analyses identified critical parameters, such as immune cell functionality and tumor immunogenicity, that govern these divergent dynamics. These findings provide a robust foundation for constructing cancer patient digital twins to optimize therapeutic strategies. To enhance model reliability, our ongoing efforts focus on uncertainty quantification, employing sensitivity analysis and parameter calibration to address inherent biological variability and epistemic uncertainties, thereby advancing the development of clinically actionable digital twins.

Gary An

University of Vermont

"NASEM-compliant Critical Illness Digital Twins to cure sepsis"

To date there are no pharmacological agents that can substantively and reliably affect the underlying host pathophysiology of sepsis. The effective control of sepsis requires personalized precision medicine, which requires the capabilities provided by digital twins compliant with industrial standards and consistent with the definition put forth in the National Academies of Science, Engineering and Medicine (NASEM) report entitled 'Foundational Research Gaps and Future Directions for Digital Twins' that provides an operational definition for a digital twin and lists specific challenges moving forward for the development of this technology. NASEM defines a digital twin thusly: 'The key elements that comprise a digital twin include (1) modeling and simulation to create a virtual representation of a physical counterpart, and (2) a bidirectional interaction between the virtual and the physical. This bidirectional interaction forms a feedback loop that comprises dynamic data-driven model updating (e.g., sensor fusion, inversion, data assimilation) and optimal decision-making (e.g., control, sensor steering).' Notably, this definition is not met by the vast majority of currently described biomedical “digital twins,” and this insufficiency limits the applicability of non-NASEM compliant digital twins to provide the true personalized precision medicine required to treat complex immune diseases such as sepsis. We present a prototype Critical Illness Digital Twin developed with a workflow that utilizes mechanistic models with machine learning and artificial intelligence for clinically relevant parameter space identification, trajectory personalization, discovery of novel multimodal/adaptive therapeutic control and guidance for sensor/actuator development. The CIDT is based on a previously validated agent-based model of systemic inflammation, and constructed to conform to a mathematical object terms the Model Rule Matrix (MRM). The MRM employs the Maximal Entropy Principle to account for the latent space of 'what is left out' (e.g. Epistemic Uncertainty) in the rule structure of the CIDT. Operating on the CIDT with a workflow that includes genetic algorithms and active learning we identified non-falsifiable configurations of the MRM with respect to two distinct clinical cytokine time-series datasets, one for burns, one for trauma. We further applied deep reinforcement learning to train an artificial intelligence that can cure sepsis arising from a novel pathogen by modulating host cytokines using only currently FDA-approved biologics. Additional future work must include testing with a sufficiently complex large animal model that can recapitulate the heterogeneity seen in clinical sepsis.

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Annual Meeting for the Society for Mathematical Biology, 2025.