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Immune Digital Twin
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About
team
publicatIONS
PARTNERS
Science
Immune Digital Twin
education & workforce development
Research
Careers
Contact
About
team
publicatIONS
PARTNERS
Science
Immune Digital Twin
education & workforce development
Research
Careers
Contact

Collaborations with DTIH

Collaborative computational modeling to advance immune system research and discovery.

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Collaborative computational modeling to advance immune system research and discovery

The Digital Twin Innovation Hub develops and applies multiscale computational models of the human immune system to advance fundamental discovery, hypothesis-driven research, and translational investigation.

We engage in research collaborations and sponsored projects that apply mechanistic and data-driven modeling to address foundational and applied questions in immunology and biomedical research. Our work emphasizes exploratory and proof-of-concept studies, conducted in close collaboration with academic, clinical, nonprofit, and mission-aligned partners.

These efforts are grounded in mechanistic understanding and designed to generate reusable models, interpretable insights, and publication-ready results.

Collaborative Modeling & Simulation Capabilities

DTIH collaborates with research teams to pursue modeling-driven investigations such as:

Multiscale immune model development

Co-development of mechanistic and hybrid models capturing signaling, metabolism, cell–cell interactions, and tissue/systemic immune dynamics within a unified digital twin framework.

Multiscale immune model development

Co-development of mechanistic and hybrid models capturing signaling, metabolism, cell–cell interactions, and tissue/systemic immune dynamics within a unified digital twin framework.

Multiscale immune model development

Co-development of mechanistic and hybrid models capturing signaling, metabolism, cell–cell interactions, and tissue/systemic immune dynamics within a unified digital twin framework.

Data integration and contextualization

Incorporation of experimental, clinical, and multi-omics datasets into research-grade simulations to explore system-level behavior and variability.

Data integration and contextualization

Incorporation of experimental, clinical, and multi-omics datasets into research-grade simulations to explore system-level behavior and variability.

Data integration and contextualization

Incorporation of experimental, clinical, and multi-omics datasets into research-grade simulations to explore system-level behavior and variability.

Exploratory scenario analysis

Virtual experimentation to test hypotheses, identify key mechanisms, explore counterfactuals, and assess conceptual intervention strategies.

Exploratory scenario analysis

Virtual experimentation to test hypotheses, identify key mechanisms, explore counterfactuals, and assess conceptual intervention strategies.

Exploratory scenario analysis

Virtual experimentation to test hypotheses, identify key mechanisms, explore counterfactuals, and assess conceptual intervention strategies.

Cross-scale integration

Linking subcellular pathways to organism-level immune regulation across biologically relevant spatial and temporal scales.te clinical measures, immune profiling, multi-omics datasets, and decades of literature-derived mechanistic knowledge. These data provide the foundation for curating and parameterizing an accurate representation of immune behavior.

Cross-scale integration

Linking subcellular pathways to organism-level immune regulation across biologically relevant spatial and temporal scales.te clinical measures, immune profiling, multi-omics datasets, and decades of literature-derived mechanistic knowledge. These data provide the foundation for curating and parameterizing an accurate representation of immune behavior.

Cross-scale integration

Linking subcellular pathways to organism-level immune regulation across biologically relevant spatial and temporal scales.te clinical measures, immune profiling, multi-omics datasets, and decades of literature-derived mechanistic knowledge. These data provide the foundation for curating and parameterizing an accurate representation of immune behavior.

These collaborations are intended for research teams seeking deeper mechanistic insight and computational augmentation of laboratory or clinical studies.

Open positions

Example Collaborative Project Areas

DTIH works with partners on a range of model-driven research questions. Representative examples include:

Drug Repurposing & Mechanism-Based Discovery

In silico evaluation of approved or investigational compounds across disease-relevant pathways Mechanistic identification of repurposing opportunities beyond phenotypic screening Virtual exploration of combination therapies and intervention timing

Drug Repurposing & Mechanism-Based Discovery

In silico evaluation of approved or investigational compounds across disease-relevant pathways Mechanistic identification of repurposing opportunities beyond phenotypic screening Virtual exploration of combination therapies and intervention timing

Drug Repurposing & Mechanism-Based Discovery

In silico evaluation of approved or investigational compounds across disease-relevant pathways Mechanistic identification of repurposing opportunities beyond phenotypic screening Virtual exploration of combination therapies and intervention timing

Clinical Indication Prioritization

Comparative modeling of disease mechanisms across multiple indications Identification of contexts in which a therapeutic mechanism is most likely to succeed or fail Model-informed prioritization to guide experimental or clinical investment

Clinical Indication Prioritization

Comparative modeling of disease mechanisms across multiple indications Identification of contexts in which a therapeutic mechanism is most likely to succeed or fail Model-informed prioritization to guide experimental or clinical investment

Clinical Indication Prioritization

Comparative modeling of disease mechanisms across multiple indications Identification of contexts in which a therapeutic mechanism is most likely to succeed or fail Model-informed prioritization to guide experimental or clinical investment

Quantitative Systems Pharmacology (QSP)

Development of mechanistic QSP models linking drug mechanism of action to downstream biological and clinical outcomes Integration of signaling, cellular, tissue, and systemic processes Sensitivity, uncertainty, and failure-mode analyses

Quantitative Systems Pharmacology (QSP)

Development of mechanistic QSP models linking drug mechanism of action to downstream biological and clinical outcomes Integration of signaling, cellular, tissue, and systemic processes Sensitivity, uncertainty, and failure-mode analyses

Quantitative Systems Pharmacology (QSP)

Development of mechanistic QSP models linking drug mechanism of action to downstream biological and clinical outcomes Integration of signaling, cellular, tissue, and systemic processes Sensitivity, uncertainty, and failure-mode analyses

Physiologically Based Pharmacokinetic (PBPK) Modeling

Construction and refinement of PBPK models informed by experimental and clinical data Exploration of dose, exposure, and tissue distribution under varying physiological conditions Coupling PBPK layers with disease or QSP models

Physiologically Based Pharmacokinetic (PBPK) Modeling

Construction and refinement of PBPK models informed by experimental and clinical data Exploration of dose, exposure, and tissue distribution under varying physiological conditions Coupling PBPK layers with disease or QSP models

Physiologically Based Pharmacokinetic (PBPK) Modeling

Construction and refinement of PBPK models informed by experimental and clinical data Exploration of dose, exposure, and tissue distribution under varying physiological conditions Coupling PBPK layers with disease or QSP models

In Vitro to In Vivo Translation

Model-based interpretation of in vitro findings within in vivo physiological contexts Mechanistic bridging across experimental systems Identification of conditions under which experimental results are likely - or unlikely - to translate

In Vitro to In Vivo Translation

Model-based interpretation of in vitro findings within in vivo physiological contexts Mechanistic bridging across experimental systems Identification of conditions under which experimental results are likely - or unlikely - to translate

In Vitro to In Vivo Translation

Model-based interpretation of in vitro findings within in vivo physiological contexts Mechanistic bridging across experimental systems Identification of conditions under which experimental results are likely - or unlikely - to translate

Virtual Clinical Studies & Cohort Exploration

Simulation of virtual patient cohorts to explore heterogeneity and stratification Evaluation of alternative trial designs, dosing strategies, or intervention timi Longitudinal exploration of disease and treatment trajectories

Virtual Clinical Studies & Cohort Exploration

Simulation of virtual patient cohorts to explore heterogeneity and stratification Evaluation of alternative trial designs, dosing strategies, or intervention timi Longitudinal exploration of disease and treatment trajectories

Virtual Clinical Studies & Cohort Exploration

Simulation of virtual patient cohorts to explore heterogeneity and stratification Evaluation of alternative trial designs, dosing strategies, or intervention timi Longitudinal exploration of disease and treatment trajectories

Multi-Scale & Digital Twin Framework Development

Integration of molecular, cellular, tissue, and organism-level model Development of digital twin frameworks that evolve as new data are incorporated Validation, benchmarking, and uncertainty characterization of modeling approaches

Multi-Scale & Digital Twin Framework Development

Integration of molecular, cellular, tissue, and organism-level model Development of digital twin frameworks that evolve as new data are incorporated Validation, benchmarking, and uncertainty characterization of modeling approaches

Multi-Scale & Digital Twin Framework Development

Integration of molecular, cellular, tissue, and organism-level model Development of digital twin frameworks that evolve as new data are incorporated Validation, benchmarking, and uncertainty characterization of modeling approaches

Model-Based Education & Training

Collaborative development of interactive simulations, workshops, and curricula to train students and researchers in systems immunology and computational modeling.

Model-Based Education & Training

Collaborative development of interactive simulations, workshops, and curricula to train students and researchers in systems immunology and computational modeling.

Model-Based Education & Training

Collaborative development of interactive simulations, workshops, and curricula to train students and researchers in systems immunology and computational modeling.

Why Collaborate with the Hub

Our work combines systems immunology, multiscale modeling, AI-enhanced simulation, software engineering, and educational technology. Joining the Digital Twin Innovation Hub means contributing to a scientific foundation with real-world impact — improving how immunity is taught, studied, and applied.

Research-first foundation


Direct engagement with actively evolving, university-led immune digital twin models grounded in mechanistic detail.

Research-first foundation


Direct engagement with actively evolving, university-led immune digital twin models grounded in mechanistic detail.

Research-first foundation


Direct engagement with actively evolving, university-led immune digital twin models grounded in mechanistic detail.

Interdisciplinary expertise

Collaboration with immunologists, computational biologists, data scientists, and engineers working at the interface of theory, data, and translation.

Interdisciplinary expertise

Collaboration with immunologists, computational biologists, data scientists, and engineers working at the interface of theory, data, and translation.

Interdisciplinary expertise

Collaboration with immunologists, computational biologists, data scientists, and engineers working at the interface of theory, data, and translation.

Collaborative engagement models


Flexible structures supporting grant-funded research, sponsored studies, consortia, proof-of-concept investigations, and educational initiatives—focused on scientific advancement rather than commercial product delivery.

Collaborative engagement models


Flexible structures supporting grant-funded research, sponsored studies, consortia, proof-of-concept investigations, and educational initiatives—focused on scientific advancement rather than commercial product delivery.

Collaborative engagement models


Flexible structures supporting grant-funded research, sponsored studies, consortia, proof-of-concept investigations, and educational initiatives—focused on scientific advancement rather than commercial product delivery.

Exploring Collaboration

If you are interested in exploring a potential research collaboration - whether centered on hypothesis generation, model development, or integration of computation into your research program - we welcome an initial conversation to assess mutual alignment.

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