title: Computational systems biology and dose-response modeling in relation to new directions in toxicity testing.
abstract: The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of "network motifs"--the basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.
authors.affiliation: Division of Computational Biology, The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Zhang
authors.forename: Sudin
authors.initials: S
authors.lastname: Bhattacharya
authors.forename: Melvin E
authors.initials: ME
authors.lastname: Andersen
authors.forename: Rory B
authors.initials: RB
authors.lastname: Conolly
mesh_terms: D000818:Animals; D002470:Cell Survival / Q000187:drug effects; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D004785:Environmental Pollutants / Q000032:analysis* / Q000633:toxicity; D006801:Humans; D008954:Models, Biological; D049490:Systems Biology / Q000379:methods*; D018675:Toxicity Tests / Q000379:methods*
chemical_list: D004785:Environmental Pollutants