Computational Modeling of Complex Processes Across Biological Scales

NIAID supports research in multiscale computational models of allergic, immunologic and infectious disease and to foster a collaborative modeling community. Computational models can:

• elucidate biological mechanisms of infection and/or transmission of pathogens and inform the development of improved countermeasures,
• define immune system regulation and responses to triggers (commensal and pathogenic organisms, preventative strategies, environment, autoantigens, transplant),
• characterize trajectories of allergic or immune-mediated diseases from initiation through progression and/or resolution, including microbiome effects, and
• help to meet emerging needs in response to pathogenic threats. 

This topic helps define infection mechanisms, guide medical intervention development, characterize immune responses and disease trajectories, and support the NIAID modeling community across biological scales.

ICO Scientific Contact:
Liliana Brown, Ph.D. (Microbiology and infectious diseases)
[email protected]

Jason Hataye, MD, Ph.D. (HIV/AIDS)
[email protected]

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

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

NCCIH supports efforts to advance multiscale computational modeling to generate mechanistic insights and improve clinical outcome prediction for complementary and integrative health approaches within a whole person research framework. 

Whole person research emphasizes multiorgan system integration and dynamic interactions across molecular, cellular, tissue, organ, psychological, behavioral, social, and environmental domains, from individuals to populations and across the lifespan. 

Health outcomes of interest include whole person health, restoration, emotional well-being, resilience, pain, sleep, chronic disease prevention, and symptom management.

Complementary health approaches typically include natural products (e.g., diets, supplements, herbs, pre/probiotics) or mind-body interventions (e.g., meditation, hypnosis, music, massage, chiropractic manipulation, light-based therapies, yoga, tai chi, art therapies).

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

NCI supports computational modeling and data integration to advance cancer research across the NCI mission, with demonstrated impact on cancer biology, therapeutic discovery, and clinical decision-making. It provides a scalable, reusable capability supporting basic biology, genetics, prevention, translational research, and clinical investigation, interoperating with existing NCI data infrastructure.

Across NCI programs, integrated computational models spanning molecular, genetic, imaging, clinical, and population data increasingly inform research and clinical insight, enabling hypothesis generation, biomarker discovery, and patient stratification. This creates a shared need for transparent, reproducible, and cloud-compatible modeling frameworks.

This component establishes a flexible modeling capability to reduce time to insight, limit duplication, and accelerate cancer discovery, prevention, translation, and clinical impact.

ICO Scientific Contact:
Jeffrey C. Buchsbaum, MD, PhD; Division of Cancer Treatment and Diagnosis, NCI
[email protected]

Emily Greenspan, PhD; Center for Biomedical Informatics and Information Technology (CBIIT), NCI
[email protected]

NHLBI supports multiscale computational modeling efforts that advance prevention, prediction, and treatment strategies related to heart, lung, blood, and sleep (HLBS) diseases. HLBS conditions involve complex, multi-system interactions spanning molecular, cellular, tissue, organ, and population scales that change across life stages, and thus, models should focus on integrating effects of sex differences, and heterogeneous data (clinical, epidemiological, omics, imaging, environmental) to simulate health and disease trajectories across a variety of spatial and temporal scales. NHLBI supports efforts that leverage AI and advanced data science strategies to analyze complex HLBS datasets available in BioData Catalyst. Priority areas include cardiovascular processes, pulmonary pathophysiology, hemodynamic/hematologic processes, and sleep/circadian regulation. NHLBI also values implementation science to design, test, and scale evidence-based interventions in clinical and community settings.

ICO Scientific Contact:
NHLBI
[email protected]

The National Institute on Aging (NIA) is interested in supporting projects to better understand aging mechanisms and relevant biology, particularly dynamic changes associated with aging across scales.

Examples of appropriate topics may include, but are not limited to, the development of a multiscale framework modeling aging and AD/ADRD based on the following:  

• Aging clocks and biomarkers, identifying aging signatures and predicting outcomes  
• Interactions among the hallmarks of aging  
• Heterogeneity of health and aging trajectories 
• Oscillations in energetics and metabolism with aging
• Age-related changes in biology across scales 
• Dynamic patterns of interventions for healthy aging  
• Image-based analysis of aging function, biology and disease for multi-scale models of aging 
• Computational models of age-related decision processes
• Decision alignment and misalignment between goals and actions
• Simulated consequences of decisions in real-world contexts

ICO Scientific Contact:
Leonid Tsap, Ph.D.
[email protected]

Computational modeling of complex processes across biological scales is highly relevant to alcohol research because alcohol-related conditions—such as Alcohol Use Disorder (AUD), fetal alcohol spectrum disorders (FASD), and alcohol-induced organ damage—arise from interactions spanning molecular, cellular, organ, behavioral, and population levels. Multiscale models enable researchers to integrate these layers, revealing mechanistic pathways that link molecular changes to systemic effects and behavioral outcomes. This approach supports NIAAA’s mission by advancing precision medicine strategies, improving prediction of treatment responses, and reducing reliance on animal studies through Novel Alternative Methods (NAMs). By fostering reproducibility and collaboration, these models accelerate discovery and translation, ultimately informing interventions that prevent and treat alcohol-related problems.

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

In the context of this topic, NIBIB supports:

• Model-driven medical technologies: Computational models that are used to drive the ethical design, development and use of biomedical imaging and bioengineering technologies that improve human health and medical care.
• Models to improve research rigor: Novel mathematical, statistical and computational methods to improve the use of computational models for biomedical, biological and behavioral research, including methods for dynamic quantification of uncertainty throughout the modeling pipeline are of high priority.

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

NIDA is interested in research using multiscale computational models to understand brain and body systems involved in the development, maintenance and treatment of substance use disorder (SUD) and HIV. Computational modeling can provide mechanistic insights on SUD and HIV-related cognitive and neural processes, related symptoms, and their influence on the propensity to develop these conditions. 

Multi-scale computational models could link scales, such as changes in cognitive processes and behavior, brain and body interactions, or by integrating detailed neuron and microcircuit models with brain network models. Multi-scale computational models of SUD and HIV could elucidate how environmental influences, underlying predispositions and brain and body systems contribute to the development, maintenance, and treatment of these conditions. Brain-body computational models could include connections of brain areas and/or circuits with peripheral organ systems.

ICO Scientific Contact:
Jessica Mollick
[email protected]

NIDCR seeks to support innovative, rigorous, and reusable multiscale computational modeling and to foster a collaborative modeling community focused on dental, oral, and craniofacial (DOC) health and disease. These models will link molecular-to-population processes across basic discovery, translational, and clinical research, integrating biological, behavioral, clinical, and real-world data to improve mechanistic understanding, risk stratification, and intervention design. Priority areas include immune, inflammatory, allergic, microbial, and host-environment dynamics in periodontal disease, peri-implantitis, oral mucositis, Sjögren’s disease, oral/oropharyngeal cancers, craniofacial developmental disorders, and oral–systemic interactions. When validated and fit for purpose, computational multiscale models may serve as NAMs. NIDCR encourages community standards, benchmarking, uncertainty quantification, and interoperable dissemination to enable reproducibility, reuse, and translation.

ICO Scientific Contact:
Preethi Chander, PhD
[email protected]

NIMH is interested in rigorous multiscale computational models that mechanistically link genetic, molecular, cellular, and circuit processes underlying brain function and behavior across the lifespan. Models should define causal/probabilistic links across levels of analysis and couple computation with experimental validation. Priorities include modeling circuit function, molecular and circuit-level mediating vulnerability and resilience, heterogeneity in functional trajectories, critical neurodevelopmental windows, and how biological or environmental perturbations cascade through neural systems to change cognition, affect, behavior and real‑world outcomes. Particularly models that:

• advance digital phenotypes
• include diagnostics, preventive or therapeutic interventions
• include Novel Alternative Methods to strengthen translational validity research 

Projects that show rigor, transparency, reproducibility, and FAIR data/model practices to enable precision mental health are encouraged.

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

The National Library of Medicine (NLM) is interested in supporting the development of multiscale and reproducible computational models that integrate processes across molecular, cellular, tissue, organismal, and population levels to advance systems biology and biomedical research. These models provide a comprehensive view of complex biological systems, enabling researchers to gain insights to fundamental life science questions, uncover disease mechanisms and accelerate the development of medical therapeutics. NLM is particularly interested in innovative and disease agnostic computational approaches that use integrated data, Artificial Intelligence (AI) technologies and simulations to enable prediction of complex phenomena across scales of the biological system. Such modeling leads to better understanding of disease progression and holds promise for improving disease prevention, precision medicine and health interventions through deeper, data-driven insights into biological complexity.

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

The Office of Data Science Strategy (ODSS) supports research that advances FAIR, reproducible, and interoperable practices for multiscale computational modeling. ODSS is interested in efforts that establish common standards, shared resources, and transparent computational workflows to improve model rigor, traceability, and reusability. Approaches that integrate modern data science methodologies, AI/ML tools, and scalable computing infrastructure are encouraged to enhance model evaluation and enable efficient integration across biological scales. These activities align with ODSS’s mission to strengthen the biomedical data ecosystem and promote widespread, responsible use of computational models.

ICO Scientific Contact:
Fenglou Mao, PhD
[email protected]

The Office of Research on Women’s Health (ORWH) is interested in research projects that enhance the reproducibility of computational models for allergic, immunologic, and infectious diseases, with a focus on sex-specific effects and conditions predominantly affecting women across the lifespan, including female-specific diseases.

The Office of Autoimmune Disease Research in the Office of Research on Women’s Health (OADR-ORWH) is interested in research focusing on: 

• Projects to develop and support data science and computational tools to characterize autoimmune disease from inception to progression.
• Projects to utilize computational models and Novel Alternative Methods (NAMs) to study autoimmunity across the lifespan.

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

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