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Modeling Environmental Exposures and Disease (MEED)

The Modeling Environmental Exposures and Disease (MEED) Facility Core provides computational support in quantifying exposures and biological processes associated with xenobiotics at the systems level, accounting for different system components, interactions, and functional states. In so doing, the Core facilitates the study of molecular mechanisms by which exposures contribute to the initiation and promotion of disease processes. The Core employs informatics methods and computational simulation tools that integrate mechanistic knowledge and data-derived information across several environmental and microenvironmental scales, as well as across multiple scales of biological organization (e.g., molecular, cellular, organ, organism, microbiome).

Schematic Depiction of the Source to Exposure to Dose to Response Continuum

Core Directors

Panos Georgopoulos, PhD

Professor Rutgers-SPHDepartment of Environmental Health and Justice

Hao Zhu, PhD

Professor Tulane UniversityDivision of Biomedical Informatics and Genomics

MEED Advisory Group

Hari Iyer

Assistant Professor Rutgers Cancer Institute of New JerseyCancer Epidemiology and Health Outcomes

Nanostructures

Over 100 geodatabases linked with a multi-model analysis framework allow comparative visualization and pattern analyses of environmental and socio-economic disparities at census block group level across the 565 municipalities.

Core Mission

Achieving CEED’s goals requires computational support, informatics expertise, and the integrative analysis of large, heterogeneous data sets from multiple sources, using emerging science, engineering, and technology. These data include health outcomes and exposure-relevant information from CEED researchers as well as from public and proprietary sources.

To better assess and evaluate health outcomes, CEED investigators may need to model specific mechanisms involved in the sequence of processes connecting “stressor to exposure to dose to effect” across many environmental and biological scales. The Modeling Environmental Exposures and Disease Facility Core (MEED) fills these needs by providing access to: (a) high-level data integration and analytics; and (b) mechanistic systems modeling tools that can estimate exposure and dose, and identify or predict early biological responses and indicators, for the purpose of designing intervention and prevention strategies to modify disease risk.

Exposure-Wide Association Studies of COVID-19 outcomes

MEED Core Services

The goal of this Core is to provide support in quantifying exposures and biological processes associated with xenobiotics at the systems level, accounting for different system components, interactions, and functional states.

MEED utilizes the resources of the EOHSI CCL, a scientific computing facility focused on data analytics and multiscale modeling of environmental and biological systems and of their interactions, with applications ranging from simulating impacts of climate change on human exposures to physiologically-based pharmacokinetic and pharmacodynamics modeling.

Predicted Warm Season Ozone Changes

Data Management and Sharing

MEED follows procedures consistent with the overall CEED data management and sharing plan; data used or derived by the Facility Core (including model inputs and outputs) are organized in databases residing on backed-up storage arrays that are either local (on the CCL servers housed in EOHSI) or housed at OARC

Exposure Modeling

Microenvironmental modeling
Residential, occupational, public, vehicular, etc. microenvironments
Cumulative and aggregate exposure modeling for individuals and populations ú Inhalation, ingestion, dermal absorption
Modeling ecological impact of chemicals
Sentinel organisms; food web modeling
Life-Cycle Analysis of exposure to chemicals

Computational Toxicology

Biologically based dosimetry modeling – inhalation, dermal absorption, ingestion
Physiologically based toxicokinetic modeling (e.g., Metals, VOCs, Pesticides)
Mechanistic toxicodynamic modeling

Training and Education

MEED provides training and support to the research teams of CEED members (including graduate students and post-doctoral fellows) through either one-to-one or group-oriented sessions and workshops involving hands-on practicing in using software packages and databases maintained by the Core. Seminar-style presentations and demonstrations, either in-class or online, are also organized by the Core to inform CEED members and their teams on new computational modeling and data analytics capabilities that are available to them through MEED.

Recent Core Publications

Integrating concentration-dependent toxicity data and toxicokinetics to inform hepatotoxicity response pathways

Russo D P, Aleksunes L M, Goyak K, Qian H, Zhu H* Integrating concentration-dependent toxicity data and toxicokinetics to inform hepatotoxicity response pathways. Environ. Sci. Technol., 2023, Accepted

Advancing Computational Toxicology by Interpretable Machine Learning

Jia X, Wang T, Zhu H* Advancing Computational Toxicology by Interpretable Machine Learning. Environ. Sci. Technol., 2023, In press.

Mechanism-driven Modeling of Chemical Hepatotoxicity Using Structural Alerts and an In Vitro Screening Assay

Jia X, Wen X, Russo D, Aleksunes L M, Zhu H* Mechanism-driven Modeling of Chemical Hepatotoxicity Using Structural Alerts and an In Vitro Screening Assay. J. Hazard. Mater., 2022, (436) 129193. (NIEHS Extramural Paper of the Month; Society of Toxicology AACT SAFE Best Publication Award)

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data

Ciallella H, Russo D, Sharma S, Li Y, Sloter E, Sweet L, Zhu H* Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data. Environ. Sci. Technol., 2022, (56) 5984–5998.

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach

Ciallella H, Russo D, Aleksunes L M, Grimm F, Zhu H* Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach. Environ. Sci. Technol., 2021, (55) 10875-10887. (Society of Toxicology Computational Toxicology Best Paper Award)