Digital Twins for Biomedical, Biological and Behavioral Research

ODSS is interested in advancing digital twins for biomedical, biological, and behavioral research, including digital twins intended for use in real-world healthcare and clinical settings. In addition to fit-for-purpose digital twin applications, ODSS encourages research that strengthens the broader digital twins ecosystem, including shared infrastructure, interoperable architectures, data and model standards, benchmarking frameworks, reusable software and resources, and community development. Priority areas include:

• Verification, validation, and uncertainty quantification (VVUQ)
• Methods for integrating longitudinal and multimodal data
• Practices that promote transparency, human-in-the-loop oversight, interoperability, sustainability, and workforce and community building

1. These efforts support scalable, trustworthy, and reusable digital twins across biomedical research and healthcare environments.

ICO Scientific Contact:
Fenglou Mao
[email protected]

Office of Data Science Strategy
[email protected]

ICO Scientific Contact:
NCATS – Office of Special Initiatives
[email protected]

NCCIH supports research relevant to complementary and integrative health (CIH) approaches through the development and application of digital twin technologies. Example projects include and are not limited to:

• Developing fit-for-purpose digital twins that combine multi-scale data of whole person health (e.g. physiological, behavioral, psychological, social, environmental) to optimize pain management and to predict progression from acute to chronic pain
• Approaches for verification, validation, and uncertainty quantification (VVUQ) of digital twins capturing heterogeneity in pain, resilience, and whole person health, ensuring credibility across real-world settings
• Development of interoperable, scalable, sustainable digital twin architectures and ecosystems incorporating CIH approaches 
• Identifying and computationally modeling the mechanisms of CIH health interventions to support resilience and well-being within a digital twin framework.

ICO Scientific Contact:
Emrin Horgusluoglu, PhD
[email protected]

NCI seeks research that develops and applies cancer-focused digital twins to accelerate discovery, therapeutic optimization, and improved patient outcomes across the cancer continuum. Areas of interest include:

• modeling tumor initiation and progression
• Predicting response and resistance
• Optimizing radiation, systemic, and surgical interventions
• Informing clinical trial design and enrollment
• Improving survivorship and symptom management

1. Projects should integrate multiscale and longitudinal data, incorporate rigorous verification and validation practices, address health disparities where appropriate, and demonstrate clear translational relevance to human cancer. Applicants are strongly encouraged to leverage and interoperate with NCI-supported infrastructure and resources, including the Cancer Research Data Commons, novel alternative methods, and existing NCI programs, to enhance scalability, data sharing, interoperability, and impact across research and clinical communities.

ICO Scientific Contact:
Jeffrey Buchsbaum, M.D., Ph.D.
[email protected]

Emily Greenspan, Ph.D.
[email protected]

NEI supports research to advance eye and visual system health through developing and applying digital twin (DT) technologies. NEI is particularly interested in:

• DTs for major eye disorders (e.g. glaucoma, age-related macular degeneration, diabetic retinopathy) integrating multimodal imaging, clinical, genetic, and functional data for disease prediction and clinical decision support
• Fit-for-purpose, bidirectional DTs of key ocular structures (e.g., cornea, retina, optic nerve), and visual processing in the brain to support studies of normal vision, disease progression, and treatment response
• Methods for dynamic and, where appropriate, real-time multimodal data assimilation of Optical Coherence Tomography, Optical Coherence Tomography Angiography, fundus photography, visual fields, intraocular pressure, and other physiological and functional measures 
• DT–enabled assistive technologies for individuals with visual impairment to improve mobility, object recognition, and independent living.

ICO Scientific Contact:
James (Shaojian) Gao, Ph.D.
[email protected]

NHGRI is interested in research that accelerates the development of genomics-informed digital twins with strong biological grounding, which integrate genomic information with biological and clinical data to create computational replicas of patients and their individual diseases, empowering the routine practice of genomic medicine.

ICO Scientific Contact:
NHGRI Research Funding
[email protected]

NHLBI is interested in transformative Biomedical Digital Twins (BDT) approaches to advance heart, lung, blood, and sleep research and implementation science. By integrating mechanistic models, multimodal data, and artificial intelligence (AI) across scales, BDTs enable precision diagnosis, therapy optimization, virtual clinical trials, and physical AI. Projects should address bidirectional integration between physical and virtual systems; physics-informed modeling; verification, validation, and uncertainty quantification (VVUQ); data heterogeneity; and scalable, secure data ecosystems (e.g. BioData Catalyst) that support fair, robust, and generalizable BDT design. 

NHLBI encourages modular, interoperable frameworks; standardized data models; real-time data assimilation; human-in-the-loop approaches; and applications in medical device design, patient-specific modeling, and real-world clinical workflows. BDTs as new approach methodologies and integration of responsible AI are encouraged.

ICO Scientific Contact:
NHLBI Digital Twins
[email protected]

ICO Scientific Contact:
Elizabeth Powell, Ph.D.
[email protected]

NIAID is interested in applications that use integrative, data-driven approaches to model complex biological systems and support predictive decision-making in the following areas.

• HIV/AIDS: Digital twins that model HIV transmission, reservoir dynamics, immune responses, viral control, and treatment/cure/prevention safety and efficacy, through integration of high-dimensional data, to enable patient-specific or population-level prediction and in silico evaluation of biomedical interventions across the lifespan.
• Microbiology and Infectious Diseases: Digital twins of viral, bacterial, parasitic, and fungal infections can integrate host–pathogen interactions across scales to improve understanding of disease progression, antimicrobial response, and interventions.
• Immunology, Allergy, and Transplantation: Immune digital twins to capture dynamic responses to physiological stimuli, infection, allergens, and transplants, supporting prediction of immune activation, tolerance, and outcomes.

ICO Scientific Contact:
Kristen Porter, Ph.D. (HIV/AIDS)
[email protected]

Rosemary McKaig, Ph.D. (HIV/AIDS)
[email protected]

Anupama Gururaj, Ph.D. (Allergy, immunology, and transplantation)
[email protected]

Liliana Brown, Ph.D. (Microbiology and Infectious Diseases)
[email protected]

Meghan Hartwick, Ph.D. (Data science and emerging technologies)
[email protected]

NIBIB supports developing biomedical technologies appropriately incorporate BDT design considerations inspired by the NASEM report. Example projects include and are not limited to:

• BDT frameworks that facilitate FFP problem design specifications for biomedical engineering research.
• BDT infrastructure that employs systems of systems, and plug-and-play principles.
• VVUQ methodology that integrates into all elements of the intended BDT.
• BDT technology that adapts to social and environmental changes over time.
• Physical BDT assets that incorporate appropriate, real-time sensor measures.
• Virtual BDT assets that “twin” with its physical asset through bi-directional, real-time information updates.
• Technology evaluation tools to assess BDT readiness.
• Information exchange pipelines connecting physical and virtual assets.
• BDT infrastructure that adopts to changes in human-in-the-loop involvement.
• Tools for iterative ethical considerations in all development phases of a BDT.

ICO Scientific Contact:
Grace Peng, Ph.D.
[email protected]

NIDA seeks to harness digital twin technology to address complex challenges in substance use disorder (Sud) and HIV. Priority areas include:

• Predicting risk for drug use relapse, virologic failure and HIV drug resistance, and coinfections.  
• Incorporating patient preferences, behavioral patterns, and social and systems-level factors. 
• Simulating behavioral interventions, treatment combinations, and medication dosing strategies for HIV and SUD.
• Modeling HIV treatment adherence challenges, missed doses, and impact on HIV viral suppression.  
• Forecasting care cascade gaps for SUD and HIV treatment and identifying high-leverage re-engagement points. 
• Simulating intersecting HIV and drug use trajectories to identify critical intervention windows.
• Predicting drug-drug interactions between medications for SUD and HIV and commonly misused substances.
• Improving clinical trials through simulated controls, responder identification, dropout prediction, and adverse event risk detection.

ICO Scientific Contact:
National Institute of Drug Abuse (NIDA)
[email protected]

In the context of this highlighted topic, NIDCR encourages investigator-initiated research that aligns with the objectives of the topic and advances research relevant to dental, oral, and craniofacial health and disease throughout the lifespan. Proposed Biomedical Digital Twins (DTs) should integrate mechanistic modeling, data assimilation, and predictive analytics to support applications across the continuum of care, including prevention, early detection, diagnosis, treatment planning, and monitoring.

ICO Scientific Contact:
NIDCR Division of Extramural Research
[email protected]

NIDDK is particularly interested in research that:

• Develops, validates, and implements trustworthy digital twins (DTs) for:
  • Improving understanding of disease mechanisms;
  • biomarker development and disease prediction;
  • clinical decision support;
  • personalized treatment; or
  • improved disease management.
• Advances methods for verification, validation, and uncertainty quantification (VVUQ) to enhance DT reliability and trustworthiness.
• Enables interoperable, scalable, and generalizable DT systems that perform across patient types and healthcare settings.
• Supports bidirectional integration of DTs with real-world and research data, including:
  • phenotypic;
  • genomic and other multi-omic; or
  • environmental data.
• Addresses NIDDK mission areas.

ICO Scientific Contact:
Veerasamy Ravichandran, PhD
[email protected]

Guillermo Arreaza-Rubín, M.D.
[email protected]

Daniel Gossett, Ph.D.
[email protected]

NIEHS is interested in advancing digital twins to support research to understand the impacts of environmental exposures on biological systems across the lifespan and to translate that knowledge into improved health. By integrating mechanistic models, diverse data sources, and AI, digital twins can strengthen our ability to understand how exposures affect health and support effective strategies for assessing, reducing and preventing harmful exposures.

Relevant projects could include:

• Examination of real-life exposure scenarios (e.g., chemical mixtures, multiple exposure routes) and the interplay of individual factors (e.g., genetics, life stage) and how this affects health
• Agent-based modeling to predict individual exposures from geospatial exposure data sets
• Validating or reproducing molecular epidemiological findings from human studies 
• Enabling virtual testing and design optimization in developing strategies to reduce, mitigate or prevent the harmful effects of chemicals.

Digital twin (DT) technology is the development of dynamic, person-specific computational models that integrate pharmacokinetics, pharmacodynamics, genomics, multi-omics data, and real-world clinical measurements to simulate individualized drug responses and disease progression. By combining mechanistic modeling, physiologically-based pharmacokinetic frameworks, quantitative systems pharmacology, and artificial intelligence. 

NIGMS’ areas of interest are:

• Use of DT technology in clinical research in the areas of anesthesia, injury, critical illness, wound healing, and sepsis.
• Research on inter-individual variability in drug metabolism and response. 
• Predictive modeling of dose-response relationships and adverse events in normal physiology and disease states.
• Development of DT technology to predict complexities of polypharmacy, drug-drug interactions, and to support mechanistic models to predict toxicity.

ICO Scientific Contact:
Chris Chao
[email protected]

NIMH supports research on digital twins that use harmonized data to predict, simulate, and optimize mental-health trajectories across biological, behavioral, clinical, and social scales.
Priority areas include:

• Multiscale mechanisms: molecular, cellular, circuit, behavior/symptoms/response, context
• Preclinical translation: cell/organoid and animal twins for targets, dose, circuits, species validity
• Clinical forecasting: risk for onset, relapse, recovery, safety, care needs
• Closed-loop care: update treatment/prevention with sensors, tasks, Electronic Health Records (EHR), clinician input
• Virtual clinical trials in psychiatry: baseline data to predict disease progression, data for estimating endpoints, sample sizes, stratification
• Product development: performance, usability, manufacturing, implementation risk
• HIV/mental health continua: simulate prevention, adherence, service use, intervention effects

ICO Scientific Contact:
Mauricio Rangel-Gomez, PhD
[email protected]

NIMHD promotes fit-for-purpose, bidirectional digital twin research consistent with the 2023 NASEM definition.  NIMHD brings a unique and essential perspective for context-aware AI and precision public health and seeks fit-for-purpose digital twin models that account for differences across population groups to ensure that digital twins are valid and generalizable. Importantly, digital twins should not function solely as a biomedical model. It must integrate real-world experiences and contextual factors to accurately reflect how biological, behavioral, and environmental factors interact for an individual and ultimately for public health.  If constructed using large, representative datasets, digital twins can help identify population-specific risk pathways, enhance the precision of intervention efforts, and broaden applicability across U.S. populations – increasing the generalizability of findings generated from these tools.

ICO Scientific Contact:
Deborah Duran, Ph.D.
[email protected]

Digital twin (DT) research in health requires foundational methodological advances aligned with NLM’s mission in biomedical informatics and data science. Key needs include standardized definitions distinguishing digital models, digital shadows, and bidirectional digital twins, along with shared terminology for synchronization, fidelity, and validation. NLM is particularly interested in research promoting DT interoperability through ontologies, common data models, and metadata standards for integration of EHRs, wearables, genomics, imaging, and other health related data across time scales. Additional priorities include AI and computational methods to harmonize and model multimodal, longitudinal data for studying disease progression, elucidating biological mechanisms, and supporting in silico experimentation. Methodological focus areas include dynamic data assimilation, bias and representativeness frameworks, hybrid mechanistic–AI models, and rigorous uncertainty quantification.

ICO Scientific Contact:
Yanli Wang, PhD
[email protected]

ORWH is interested in fit-for-purpose, bidirectional digital twins that advance women’s health by improving understanding of sex-specific biology across the life course.

Areas of interest include:

• Hormone signaling and homeostasis
• Life stages such as puberty, menstrual cycling, pregnancy, postpartum, and menopause
• Sex-specific efficacy, dosing, treatment response, clinical prediction, and toxicity of drugs and biologics
• Mechanistic and/or data-driven predictive modeling at multi-organ, multi-system, or whole-person levels using multimodal and/or longitudinal data 

The Office of Autoimmune Disease Research in ORWH (OADR-ORWH) is interested in supporting the following research:

• Development and use of bidirectional digital twins in autoimmune disease research
• Validation and verification of digital twins for autoimmune disease research
• Development of data science and computational tools to leverage digital twin technologies in autoimmune disease research.

ICO Scientific Contact:
Elena Gorodetsky, M.D., Ph.D.
[email protected]

Victoria Shanmugam, MBBS, FRCP, FACR, CCD
[email protected]