MSU Superfund Projects and Cores:

Project 1: Characterization of the Pathways Linking Ah Receptor Activation with Altered B Cell Differentiation Using an Integrated Experimental and Computational Modeling Approach

Project 2: Dissecting the Signaling Network for Ah Receptor-mediated Bcell Toxicity

Project 3: Non-Additive Ah Receptor Ligand Interactions

Project 4: Influence of Ah Receptor Ligands on Inflammatory Responses: Consequences for Tissue Injury and Gene Expression

Project 5: A Proteomic Analysis of the AHR signaling Network

Project 6: Molecular Insight into Polyaromatic Toxicant Degradation by Microbial Communities

Project 7: Geochemical Controls on the Adsorption, Bioavailability, and Long-term Environmental Fate of Dioxins, PCBs, and PAHs

Core A: Administration

Core B: Research Translation

Core C: Computational Modeling of Mammalian Biomolecular Response

Core D: Biomedical Informatics

Core E: Environmental Molecular Analysis

Return to the MSU Superfund Main Page

Link to the NIEHS SBRP site

Superfund Core C: Computational Modeling
of Mammalian Biomolecular Responses

Knowledge of the shape of the dose-response curve must extend to levels at which humans are typically exposed if we are to accurately assess the risks of adverse effects on the public health from exposures to environmental chemicals. Health effects data are usually sparse at environmental levels of exposure and computational models are being used to estimate both chemical disposition (i.e., pharmacokinetics) and tissue responses (i.e., pharmacodynamics). Current pharmacokinetic models incorporate physiological, anatomical, and biochemical information to provide accurate estimates of target tissue doses; however the pharmacodynamic relationship between a chemical at its target site and the ultimate biological effect is usually described empirically or semi-empirically. Molecular level descriptions of pharmacodynamic mechanisms would provide a better understanding of dose-response curves and would reduce uncertainty in safety and risk assessments. 

The mission of Core C is to provide the skills and resources needed to develop computational models of biochemical pathways and to thereby provide insight into the adverse health effects of TCDD and related chemicals. Since development of computational models is an iterative process, with model development and laboratory experiments proceeding hand-in-hand, the work in Core C is highly collaborative with the work in the Research Projects that the Core supports (Projects 1, 2, and 4). 

Illustrated below is a signaling map generated by Pathway Assist in support of Project 4. Treatment with LPS or TCDD affects the expression level of Serpine1 (PAI­1). Note how TCDD is linked directly to Ahr but also to other nodes such as Cyp1a1 and Mapk3 that are in fact downstream from formation of the TCDD-Ahr complex.

The overall approach to be used for development of computational models is defined by four specific aims:

1. Develop initial descriptions of biochemical pathways where the nodes of the pathway and the interactions between nodes are linked to biomedical databases.

2. Develop a directed graph by curating the pathway description obtained under SA1.

3. Develop computational models based on the network structures described by directed graphs.

4. Determine if a stochastic or Boolean model is preferable to an ODE-based model for understanding the dynamic behavior of a particular biochemical network.

Core C also trains postdoctoral fellows and other staff from the Research Projects in the use of software for development of pathway maps and for computational modeling of the pathways.

Melvin E. Andersen, Ph.D.
Core Leader
Hamner Institutes for Health Sciences

Rory B. Conolly, Sc.D.
Co-Investigator
U.S. Environmental Protection Agency

Qiang Zhang, Ph.D.
Co-Investigator
CIIT Centers for Health Research