At a glance
- ATSDR's Simulation Science Section supports public health research projects and initiatives by using computational modeling.
- Computational modeling helps researchers figure out what might happen in a chemical incident or outbreak.
- ATSDR uses four main types of computational modeling to help researchers gain insight into public health issues.
Purpose
Computational modeling is a process that uses computers to simulate and study complex systems using math, physics, and computer science.
ATSDR's Simulation Science Section uses computational modeling to support a variety of public health research projects and initiatives. These activities are focused on protecting the health of people and communities.
The Simulation Science Section collaborates with:
- Public health professionals.
- Health assessors at CDC and state health departments.
- Emergency response teams.
- Academic researchers at colleges and universities.
Researchers can use several data streams and apply mathematical methods with computational modeling. This data can help to figure out what might happen in a chemical incident or outbreak.
Why it matters
ATSDR researchers use computational modeling to do the following:
- Estimate chemical levels in the body
- Understand how chemicals transport and change in the environment
- Link chemicals with potential human health effects
- Provide missing data on potential harmful effects of chemicals
Computational modeling helps researchers gain insight into public health issues and efficiently find solutions. With computational modeling, researchers can evaluate thousands of hazardous substances to identify potential health risks quickly and inexpensively.
Computational modeling also allows researchers to generate reliable data without using human subjects or lab animals in experiments. This is helpful for assessing potential health risks from new situations or circumstances that are hard to recreate in a lab.
The bottom line: Computational modeling saves time and resources by maximizing efficiency and filling in data gaps.
Types
ATSDR's Simulation Science Section uses four main types of computational modeling.
Physiologically based toxicokinetic (PBTK)
PBTK models describe what happens when chemicals enter a person's body. These models help researchers connect environmental exposures to internal body burden levels.
Benchmark dose
Benchmark dose models help researchers determine whether a specific chemical dose or concentration produces a certain effect in a person's body. These effects include weight loss or the growth of a tumor.
Structure-activity relationship (SAR)
SAR models help researchers learn about the potential health effects of unknown or poorly understood substances. They compare these substances to well-studied substances with similar chemical structures.
Fate and transport
Fate and transport models predict how chemicals move in and transfer between air, soil, and water.
Water models are a subtype of the fate and transport model. The fate and transport model is used to identify people who have been exposed to harmful levels of contaminated drinking water.
For more details and examples of computational modeling, check out this video:
Opportunities
To collaborate with ATSDR's Simulation Science Section or use computational modeling in your research, contact Chief Patricia Ruiz at doz8@cdc.gov.
Publications
Modeling chemical exposure and toxicokinetics
- Development of a human Physiologically Based Pharmacokinetic (PBPK) Toolkit for environmental pollutants
- Assessing the Application of Physiologically Based Pharmacokinetic Models in Acute Chemical Incidents
- Prevalence of Exposure to Environmental Metal Mixtures Among Pregnant Women in the United States National Health and Nutrition Examination Survey (NHANES) 1999–2018
- Global Serum Per- and Polyfluoroalkyl Substances Exposures and Their Correlation with Lipids: A Systematic Review and Meta-Analysis
- Investigating the Quantitative Toxicological Relationship between PFAS Alkyl Fluorine Structure and Exposure Levels Leading to Changes in Blood-Based Clinical Markers in Rats
Predicting chemical toxicity and health effects
- Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology
- Meta-analysis of animal studies applied to short-term inhalation exposure levels of hazardous chemicals
- Development, validation and integration of in silico models to identify androgen active chemicals
- Machine Learning Prediction of Transthyretin Binding for Thyroid Hormone Transport Disruption for Chemical Risk Assessment
- In Silico Hazard Assessment of Ototoxicants Through Machine Learning and Computational Systems Biology
- Screening Level Estimation of Chemical Mixtures Toxicity Using In Silico Models: A JP-5 Case Study for Environmental Incidents
- Workflow for Predictive Risk Assessments of UVCBs: Cheminformatics Library Design, QSAR, and Read-across Approaches Applied to Complex Mixtures of Metal Naphthenates
Chemical metabolism
Simulation science methods and applications
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