Efficient Design of Biological Experiments for Dose-Response Modeling in Nanomaterial Toxicology Studies

提案编号：1065931 PI姓名：Feng Yang评估纳米材料风险的最基本步骤之一是基于生物学实验了解并正确表征其剂量-反应关系。由于成本、道德或其他资源或时间的限制，样本量通常受到限制，有效利用可用资源至关重要。因此，实验的设计，即，实验剂量的选择和动物的分配对剂量-反应研究的成功起着重要的作用。智力上的优点：由于毒性数据的特殊性，有效的剂量-反应模型设计特别具有挑战性。本研究的目的是开发一种实验设计程序，该程序可适应剂量-反应曲线的非线性性质和毒性数据的方差异质性，以指导生物实验中的剂量选择和动物分配，从而有效地生成剂量-反应关系。建议的设计程序将建立在一个两阶段的贝叶斯范式，它提供了一个统计上有效的机制，利用先验信息的设计未来的实验。将确定最合适的剂量-反应以及方差模型来描述毒性数据。Bootstrapping是一种计算密集型的重新设计方法，与传统的统计推断方法相反，将用于从后续实验设计所需的初步数据中获得重要信息。为了实现实际有用的设计，将同时考虑多个设计标准，并采用多目标元分析法来搜索一组帕累托最优设计，以便在实际实验设置中评估各种权衡。这项研究的结果将产生一个有效的实验设计程序，能够通过高质量的剂量来表征物质，响应曲线使用较少的实验。从拟议的工作中获得的方法将大大减少毒理学研究的实验成本和时间，减轻对动物伦理的日益关注，并加速量化环境和职业暴露于纳米材料的风险，安全和健康影响的进展。该项目的实施将促进不同学科的融合以及大学与联邦实验室之间的合作伙伴关系。广泛的数据传播将直接影响人类健康和环境的风险和安全评估。这项工作的教育目标是培训研究生和本科生，招募代表性不足的学生（妇女、少数民族和低收入）参加拟议的研究。此外，这项工作将为课堂案例研究提供宝贵的资源，并吸引年轻人才进入工业工程接口的多学科领域（例如，计算机模拟、统计等）生物学和纳米技术。

Proposal No: 1065931PI Name: Feng YangOne of the most fundamental steps in assessing the risk of a nanomaterial is to understand and properly characterize its dose-response relationship based on biological experiments. Because of costs, ethics, or other limitations on resources or time, sample sizes are usually restricted and efficient use of available resources is critical. Thus, the design of experiments, i.e., the selection of experimental doses and the allocation of animals, plays an importantrole in the success of dose-response studies.Intellectual Merits: Efficient design for dose-response modeling is particularly challenging due to the special features of toxicity data. The objective of the proposed research is to develop an experimental design procedure, which accommodates the nonlinear nature of dose-response curves and variance heterogeneity of toxicity data, to guide the dose selection and animal allocation in biological experiments for the efficient generation of dose-response relationships. The proposed design procedure will be built in a two-stage Bayesian paradigm,which provides a statistically valid mechanism to utilize prior information for the design of future experiments. Most suitable dose-response as well as variance models will be identified to describe the toxicity data. Bootstrapping, a computationally intensive resampling method as opposed to conventional statistical inference methods, will be used to derive important information from the preliminary data required by the subsequent experimental design. To achieve practically useful designs, multiple design criteria will be considered simultaneously, and multi-objective metaheuristics will be adapted to search for a set of Pareto optimum designs, which allow for the evaluation of various trade-offs in practical experimental settings.Broader Impacts: The outcome of this research will result in an efficient experimental design procedure which is able to characterize substances by high-quality dose-response profiles using less experiments. The methods obtained from the proposed work will substantially reduce the experimental cost and time in toxicology studies, alleviate the rising concerns for animal ethics, and accelerate the progress toward quantifying the risk, safety and health effects of environmental and occupational exposure to nanomaterials. The implementationof the project will promote the fusion of different disciplines and the collaborative partnership between universities and federal labs. The broad data dissemination will directly impact the risk and safety assessment for human health and the environment. The educational objectives of this effort are to train graduate and undergraduate students, to recruit underrepresented (women, minority and low-income) students to participate in the proposed research. In addition, this work will provide a valuable resource for classroom case studies, and attract young talents into the multidisciplinary field interfacing industrial engineering (e.g., computer simulation, statistics, etc.) with biology and nanotechnology.