Research Thrust 3

Predictive Microbiome MResearch Thrust 3 (RT3): Predictive Modeling of the Built Environment Microbiome
onitoring

RT3 focuses on predicting how microbial communities in buildings change over time and how those changes affect human health.

Researchers in RT3 develop data driven models that integrate microbial measurements, environmental conditions, and building characteristics to forecast microbial dynamics and health risks in indoor spaces. These models move beyond retrospective analysis to enable forward looking, decision focused insights.

RT3 translates complex microbiome data into actionable predictions, such as when pathogen risks are likely to increase or when interventions are most effective. By linking microbial behavior to factors like occupancy, ventilation, water use, and environmental conditions, RT3 helps identify the drivers of unhealthy microbial states.

RT3 works closely with measurement and intervention efforts across PreMiEr. Data from RT1 provide the real world inputs needed to train and validate models, while insights from RT3 guide targeted interventions developed in RT2. This integration enables adaptive, evidence based microbiome management.

Together, RT3 delivers the predictive capability needed to move from reactive responses to proactive control, supporting smarter buildings that anticipate and protect human health.

Currently Funded Projects

  • Research Team: Barbara Turpin (UNCCH, lead), Glenn Morrison (UNCCH), Rachel Noble (UNCCH), Nicole Rockey (DUKE), Claudia Gunsch (DUKE), Boyuan Chen (DUKE), Joe Brown (UNCCH), Joshua Granek (DUKE), David Singleton (DUKE), Denene Blackwood (UNCCH), Sherlynette Pérez Castro (UNCCH), Gavin Duffy (DUKE), John Zhou (DUKE), Clara Eichler (UNCCH), Evelyn Liu (DUKE), Emily Nortmann (UNCCH), Qing Dai (DUKE).

    Description: We propose an integrated system to support healthy homes through precision microbial engineering interventions for targeted control of fungal pathogens in built environments. The system links microbial cocktail formulation, optimized aerosolization, and engineered dispersion strategies to ensure effective coverage and persistence of non-pathogenic microorganisms. Using the PreMiEr Home@Duke testbed and UNC Environmental Chambers, we will (1) characterize fungal hazards to develop and validate biotic inoculants against target fungi, (2) model and optimize bioaerosol dispersion, transport, and deposition under diverse operational scenarios, and (3) implement a feedback loop using sensors to assess conditions post-intervention and trigger further treatment. This research advances microbial engineering for the built environment by uniting detection, targeted biocontrol, and adaptive environmental management into a field-tested platform, with potential to reduce disease transmission, improve indoor microbiome resilience, and transform pathogen mitigation strategies.

  • Research Team: Megan Lott (UNCCH, lead), Joe Brown (UNCCH), Glenn Morrison (UNCCH), Deverick Anderson (DUKE), Linden Neal (UNCCH).

    Description: Within PreMiEr, we are constructing quantitative microbial risk assessment (QMRA) models to guide, evaluate, and improve microbiome engineering interventions that improve human health. Specifically, we are building risk assessment models to quantify risks and protective effects attributed to microbial exposure from the built environment within the healthcare setting. We are utilizing the data generated by the Measurement Research Thrust (RT1) to estimate patients’ plausible exposures to pathogens of concern within the built environment. We are leveraging existing dose-response models from previous epidemiological and experimental studies to relate the simulated exposure dose to health outcomes. These efforts will establish a base model upon which we iterate using the knowledge learned from the Center’s spatiotemporal and generative models. Ultimately, we will utilize these models to simulate the outcomes of our microbiome engineering interventions, compare their relative effectiveness, and evaluate their utility in reducing infection.

Previously Funded Projects

  • Status: Part of PreMiEr core project “Predictive modeling of healthcare built environments” effective September 1, 2024

    Faculty: Yi-Hui Zhou (NCSU, lead), Benjamin Callahan (NCSU), Nathan Crook (NCSU), Lawrence David (Duke)

    Description: This project aims to establish a more robust and interpretable pipeline for predicting disease and dynamic host traits using microbiome data subject to environmental perturbations. By exploring the impact of sample characteristics and studying the temporal dynamics of microbiome growth under different environmental conditions, this project aims to establish a reliable and interpretable pipeline for predicting host traits using microbiome data.

  • Status: Part of PreMiEr core project “Predictive modeling of healthcare built environments” effective September 1, 2024

    Faculty: Li Ma (Duke, lead), Zhicheng Ji (Duke), Glenn Morrison (UNCCH), Barbara Turpin (UNCCH), Yi-Hui Zhou (NCSU)

    Description: Compared to the human microbiome, the microbiome of the built environment has received little attention and there remains a need for model development. This project seeks to understand the heterogeneity of the indoor microbiome and test the applicability of gut microbiome models as a basis for indoor microbiome models. Understanding data variability and structure will aid design of future perturbation or intervention field studies.

  • Status: Sunset effective September 1, 2025

    Faculty: Li Ma (DUKE, lead), Anthony Fodor (CHAR), Zhicheng Ji (DUKE), Glenn Morrison (UNCCH), Barbara Turpin (UNCCH)

    Description: Compared to the human microbiome, the microbiome of the built environment has received little attention and there remains a need for model development. Different approaches in this project seek to develop statistical models and tools that are capable of accurately characterizing built-environment microbiome compositions and link them to health outcomes, and to use advanced modeling techniques to predict and control pathogen transmission. This project also hopes to develop an understanding of the heterogeneity of the indoor microbiome and test the applicability of existing microbiome models as a basis for indoor microbiome models. Understanding such data variability and structure will aid design of future perturbation or intervention field studies.

  • Status: Sunset effective September 1, 2024

    Faculty: Daniel Reker (Duke, lead), Nathan Crook (NCSU)

    Description: Current methods for studying the built-environment microbiome and its metabolites are limited and expensive, making it difficult to fully understand the health effects, mechanisms, and involved microbes. This project’s goal is to design novel machine learning tools that can rapidly identify the ways in which the built-environment metabolome may impact human health.