Computational Modeling of Complex Processes Across Biological Scales is sponsored by National Institutes of Health (NIH) - National Library of Medicine (NLM). This topic encourages innovative research in computational modeling of complex processes across biological scales, focusing on developing multiscale models.

Computational Modeling of Complex Processes Across Biological Scales | Grants & Funding U.S. Department of Health and Human Services National Institutes of Health Computational Modeling of Complex Processes Across Biological Scales When beginning your next investigator-initiated application, consider the following NIH highlighted topic.

The area of science described below is of interest to the listed NIH Institutes, Centers, and Offices (ICOs). This is not a notice of funding opportunity (NOFO). Apply through an appropriate NIH Parent Funding Announcement or another broad NIH opportunity available on Grants.

gov . Learn how to interpret and use Highlighted Topics . Post Date: April 17, 2026 Expiration Date: April 17, 2027 This topic encourages innovative research in computational modeling of complex processes across biological scales (i.e., to develop multiscale models).

The topic seeks to build a collaborative community of researchers to improve the replicability and reproducibility of computational multiscale models, promoting their advancement and reuse. Multiscale computational models that integrate processes across different spatial and temporal levels, from molecular to organismal, to epidemiologic and from microseconds to years.

They provide a comprehensive understanding of complex systems and offer an exciting opportunity to advance biomedical research. This approach helps reveal how interactions at molecular and cellular scales influence larger, population-, geographical-, or global-scale phenomena, offering insights into complex biological processes, and may help develop better and more precise biomedical interventions.

By integrating processes from molecular to epidemiologic levels, multiscale computational models provide a comprehensive understanding of complex systems.

This topic encourages innovative research and collaborative approaches that integrate technologies and informatic practices to develop, improve, and disseminate multiscale computational models for human health and diseases, and their associated technologies, across the research community.

The topic also supports leveraging computational multiscale models as an important component of Novel Alternative Methods (NAMs) to investigate the mechanism and safety of a medical intervention in pre-clinical, translational, and clinical development.

Central Scientific Contact: National Institute of Allergy and Infectious Diseases (NIAID) 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. Liliana Brown, Ph. D.

(Microbiology and infectious diseases) Jason Hataye, MD, Ph. D. (HIV/AIDS) Anupama Gururaj, Ph.

D. (Allergy, immunology, and transplantation) Meghan Hartwick, Ph. D.

(Data science and emerging technologies) National Center for Complementary and Integrative Health (NCCIH) 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). IC may give special consideration to support meritorious applications in this topic area.

National Cancer Institute (NCI) 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. Jeffrey C.

Buchsbaum, MD, PhD; Division of Cancer Treatment and Diagnosis, NCI Emily Greenspan, PhD; Center for Biomedical Informatics and Information Technology (CBIIT), NCI National Heart, Lung, and Blood Institute (NHLBI) 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.

National Institute on Aging (NIA) 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 National Institute on Alcohol Abuse and Alcoholism (NIAAA) 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.

National Institute of Biomedical Imaging and Bioengineering (NIBIB) 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.

National Institute on Drug Abuse (NIDA) 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.

IC may dedicate funds available to support applications in this Topic area depending upon the availability of funds, the number of meritorious applications, and competing ICO priorities.

National Institute of Dental and Craniofacial Research (NIDCR) 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. IC may give special consideration to support meritorious applications in this topic area.

National Institute of Mental Health (NIMH) 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.

Mauricio Rangel-Gomez, PhD National Library of Medicine (NLM) 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. Office of Data Science Strategy (ODSS) 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. This office does not award grants. Applications must be relevant to the objectives of at least one of the participating Institutes or Centers listed in this topic.

Office of Research on Women's Health (ORWH) 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. This office does not award grants.

Applications must be relevant to the objectives of at least one of the participating Institutes or Centers listed in this topic. Elena Gorodetsky, M. D.

, Ph. D. Victoria Shanmugam, MBBS, MRCP, FACR, CCD For technical issues E-mail OER Webmaster