Human Quantitative Dynamics

人类数量动力学

• 批准号: 8985919
• 负责人: Srinivas Ravi V Iyengar
• 金额: $ 2.5万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8985919
• 关键词: Biochemical; Bioinformatics; Cataloging; Catalogs; Cell physiology; Cells; Cellular biology; Characteristics; Chemicals; Communities; Costs and Benefits; Data; Databases; Diagnosis; Disease; Educational workshop; Engineering; Ensure; Future; Gene Expression Profile; Genomics; Health; Human; Human Genome; Human body; Immunologist; Kinetics; Knowledge; Lead; Mechanics; Metadata; Modeling; Molecular; Organ; Physiological; Pilot Projects; Proteins; Reproducibility; Research Personnel; Resources; Technology; Tissues; United States National Institutes of Health; cell type; epigenomics; gastrointestinal; meetings; network models; public health relevance; research study; response

 DESCRIPTION (provided by applicant): As we use the vast genomic and epigenomic knowledge obtained over the past fifteen years to understand health and disease, there is a growing realization that we need to develop a quantitative understanding of the components of various cell types and tissues in terms of their concentrations and interactions. This knowledge is necessary to build predictive dynamical models to connect the genomic and epigenomic characteristics to biochemical and physiological functions. To obtain this knowledge it will be necessary to experimentally identify and determine concentrations all proteins (and other cellular components) along with the kinetic parameters that govern their interactions in all cell types from major organs of the human body. This quantitative and dynamic experimental cataloging of proteins and their interactions will be anchored in the human genome and transcriptome at one end, and human cell physiological responses at the other. The quantitative data will be most useful when organized in a database that enable building of dynamical ODE and PDE models as well as network models. To ensure reproducibility and wide usage of these kinetic parameters the metadata describing experiments should be both deep and broad. To develop a bottom-up community driven plan for a Human Quantitative Dynamics (HQD) Project we are seeking partial support for a workshop for which bring together researchers from different communities who would generally not meet with one another. This would include molecular neurobiologists, cellular immunologists, gastrointestinal biologists, chemical and mechanical engineers, bioinformatics researchers and dynamical modelers. The workshop will be held at the NIH in Bethesda and will be open and free. At this workshop these researchers will analyze the strengths, weaknesses and cost /benefit ratios of recently completed large scale projects; evaluate the current level of knowledge, identify the contours of a plan for a pilot project and for future workshops that can develop and assess more in-depth plans of how we can come together to develop resources and technologies that can enable quantitative and predictive understanding of human cell biology and physiology.

 描述（由应用提供）：由于我们使用了过去十五年来获得的庞大基因组和表观基因组知识来了解健康和疾病，因此我们需要在其浓度和相互作用方面对各种细胞类型和组织的组成部分进行定量了解。这些知识对于建立预测动态模型是必要的，以将基因组和表观基因组特征与生化和生理功能联系起来。为了获得这些知识，有必要在实验中识别和确定所有蛋白质（和其他细胞成分）以及控制所有细胞类型的相互作用的动力学参数。蛋白质及其相互作用的这种定量和动态的实验分解将锚定在人类基因组和转录组中，另一端将锚定在人类细胞生理反应中。当在数据库中组织起来，该数据库能够构建动态ODE和PDE模型以及网络模型，将最有用。为了确保这些动力学参数的可重复性和广泛的用法，描述实验的元数据应既深又宽。为了制定人类定量动态（HQD）项目的自下而上的社区驱动计划，我们正在寻求对研讨会的部分支持，该研讨会将来自不同社区的研究人员聚集在一起，这些研究人员通常不会彼此相遇。这将包括分子神经生物学家，细胞免疫学家，胃肠道生物学家，化学和机械工程师，生物信息学研究人员和动态建模者。研讨会将在贝塞斯达的NIH举行，将开放和自由。在这个研讨会上，这些研究人员将分析最近完成的大规模项目的优势，劣势和成本 /收益率；评估当前知识水平，确定试点项目计划的轮廓以及将来可以开发和评估我们如何共同开发资源和技术的未来研讨会，这些计划可以对人类细胞生物学和生理学进行定量和预测的理解。