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SYSTEMS BIOLOGY MODEL DEVELOPMENT AND APPLICATION
This project will progress along four broad levels each informing and helping develop one another. First, and already on-going, are a number of tasks using existing physiologic pharmacokinetic and pharmacodynamic models to develop and then test different hypotheses describing the adverse affect that may result from environmental exposures. This work is being, at this time, applied to humans. Models describing enzymatic changes, such as cholinesterase inhibition, are being used to show the relative impact of different exposure scenarios. Further, these models are also being developed and used to help design the most useful and cost-effective exposure measurement studies. Collaborative work is being performed to test the suitability of using in-vitro and computationally derived parameters in models such as these. This is another important aspect as given the complex nature of future models and exposure scenarios methods to rapidly estimate key physiologic, thermodynamic, and biochemical parameters will be necessary, especially for those conditions that preclude practical laboratory measurements. In addition, a number of tasks are underway or being planned that will build more complicated pharmacodynamic models for this purpose. (See project description on pharmacodynamic modeling of the prostate as an example.) Second, a task is being formulated that will begin to build a conceptual model that would used various types of data, such as pharmacokinetic data, mode of action data, “omics” data, etc. for a specific case. A mathematical model will then be built and implemented. The model will be used to show the importance of relevant data. That is, the question will be answered “how much can the risk assessment be improved and uncertainty reduced as more data demanded and become available?” Some work in this task will begin to develop the mathematical constructs necessary to incorporate “omic” data and information into quantitative models. The third task will start to describe a fairly complicated endogenous physiologic or biochemical process in detail. Organisms have many biochemical processes that help maintain the homeostasis of the system. Understanding and describing such processes may be crucial to eventually describing and predicting the adverse effects resulting from perturbation of those processes resulting from exposure to exogenous substances and factors. Although this task is still in formative stages examples include the development of kinetic model of the microsomal oxygenation system in hepatoctyes, or describing the glutathione system in physiologic detail. After model formulation, implementation, and testing the model will be expanded for use with pharmacokinetic models to describe what occurs in the endogenous system after exposure to environmental toxicants. Again, such a model can be used for hypothesis testing as well as for predictive risk assessments.
System biology models holistically describe, in a quantitative fashion, the relationships between different levels of a biologic system. Relationships between individual components of a system are delineated. System biology models describe how the components of the system interact to give rise to the physiologic function of the system. For the realm of toxicology these models will be developed to not only describe such interactions but to also describe how exposure to toxicants can perturb these interactions and the normal physiology of the system. A hallmark of these models is that they are designed to allow study of the multiple components of the system simultaneously. The resolution of such models depends upon the problem being studied. They can describe interaction between molecules, between molecules and tissue, organs, and whole systems. They can even extend to interaction between different species within ecosystems. Most populations, including humans, are simultaneously exposed to numerous potential toxicants under a myriad of conditions. Further those populations have a variety of other processes occurring during and with those exposures. Underlying disease, nutritional factors, and genetic predisposition are just a few examples of underlying factors that can greatly influence an organism’s or population’s response to environmental toxicants. System biology models offer the opportunity to describe and understand some of these mechanisms so that risk assessments can eventually be based on the most relevant biological information and not just on default assumptions for which the uncertainty is not easily identified nor quantified. The models are also excellent tools to help analyze data and test different hypotheses. These models will use and depend upon complex data such as is generated from genomics, proteomics, and metabolomics. Iteration between experimental measurements and computational modeling is necessary to understand the function of complicated biologic systems.