MS03 - MFBM-17 Part 1 of 3

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

Tuesday, July 15 at 10:20am

SMB2025 Follow

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.

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.

Yi Jiang

Georgia State University

"Immunogenic Cell Death: The Key to Unlocking the Potential for Combined Radiation and Immunotherapy"

Immunogenic cell death (ICD) enhances anti-tumor immunity by releasing tumor-associated antigens and activating the anti-tumor immune system response. Here, we develop a mathe- matical model to quantify the role of ICD in optimizing the efficacy of combined radiotherapy (RT) and macrophage-based immunotherapy. Using preclinical murine data targeting the SIRPα-CD47 checkpoint, we show that RT alone induces minimal ICD, whereas disrupting the SIRPα-CD47 axis significantly enhances both phagocytosis and systemic immune activation. Our model predicts an optimal RT dose (6–8 Gy) for maximizing ICD, a dose-dependent abscopal effect, and a hierarchy of treatment efficacy, with SIRPα-knockout macrophages exhibiting the strongest tumoricidal activity. These findings provide a quantitative framework for designing more effective combination therapies, leveraging ICD to enhance immune checkpoint inhibition and radiotherapy synergy.

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.

#SMB2025 Follow

Annual Meeting for the Society for Mathematical Biology, 2025.