CORE 2: DRIVING BIOLOGICAL PROBLEMS

核心 2：解决生物问题

• 批准号: 8382383
• 负责人: RUSS BIAGIO ALTMAN
• 金额: $ 51.32万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8382383
• 关键词: Address; Adenosine; Adverse drug effect; Adverse effects; Algorithms; Amputees; Animal Model; Antibiotics; Artificial Arm; Automobile Driving; Bacteria; Bacteriophage P22; Bacteriophage lambda; Behavior; Behavioral; Binding Proteins; Biological; Biology; Biomechanics; Brain; Caffeine; Cell Shape; Cell Wall; Cell model; Cells; Cereals; Communities; Complex; Computer Vision Systems; Computing Methodologies; Cryoelectron Microscopy; DNA; Data; Dependency; Disease; Drug Delivery Systems; Electrodes; Ensure; Environment; Family; G-Protein-Coupled Receptors; GTP-Binding Proteins; Genetic Variation; Goals; Growth; Health; Herpesviridae; Human; Implant; Individual; Infection; Intention; Investigation; Laboratories; Limb Prosthesis; Limb structure; Measures; Mediating; Membrane; Membrane Proteins; Methods; Modeling; Molecular; Monkeys; Motion; Motor; Movement; Neurosciences; Participant; Pattern; Performance; Physics; Process; Property; Prosthesis; Proteins; Quality of life; Research; Research Personnel; Route; San Francisco; Science; Signal Transduction; Staging; Structure; Synaptic Transmission; System; Testing; Therapeutic; Time; Upper Extremity; Viral; Virion; Virus; Virus Diseases; Wireless Technology; Work; base; beta-2 Adrenergic Receptors; biological systems; computational chemistry; design; disability; drug discovery; ds-DNA; electron tomography; improved; medical schools; meetings; mind control; models and simulation; molecular scale; motor control; neural prosthesis; particle; physical model; receptor; relating to nervous system; simulation; small molecule; software development; three dimensional structure; tool

CORE 2: DRIVING BIOLOGICAL PROBLEMS Three driving biological problems will focus our biocomputation research. A macroscale DBP focused on neuroprosthetic dynamics, a mesoscale DBP focused on viral and cellular dynamics, and a molecular scale DBP focused on drug target dynamics. We use seven criteria to select our Driving Biological Problems (DBPs): Canonical: The problem should be an archetype of problems in an entire field of inquiry to guarantee broad applicability of the tools we develop. ¿ Collectively cover a range of scales: It is critical that activities of the center cover scales from molecular through cellular to organismal levels. ¿ Physics-based: The DBP should present a problem that can be addressed by representing and analyzing the geometry and physics of the biological system. ¿ Data rich: Biology is dominated by experimental data. These data provide the real-world constraints that drive and validate models and simulations. ¿ World-class, engaged experimentalists: We seek close integration and deep interactions among the biological and computational participants. ¿ Have important implications for disease: We ensure that our DBPs are related to disease processes or treatments to ensure that they contribute to the advancement of human health. Our past DBPs met these criteria and enabled us to develop software that is now used by a very broad community of researchers. Our next three DBPs also satisfy these criteria and are briefly reviewed below.

核心 2：解决生物问题 三个驱动性的生物学问题将成为我们生物计算研究的重点。宏观尺度DBP专注于神经假体动力学，中尺度DBP专注于病毒和细胞动力学，分子尺度DBP专注于药物靶点动力学。 我们使用七个标准来选择驱动生物问题 (DBP)： 规范：问题应该是整个研究领域中问题的原型，以保证我们开发的工具的广泛适用性。 ?? 共同覆盖一系列规模：中心的活动覆盖从分子到细胞再到有机体水平，这一点至关重要。 基于物理：DBP 应该提出一个可以通过表示和分析生物系统的几何和物理来解决的问题。 ¿ 数据丰富：生物学以实验数据为主。这些数据提供了驱动和验证模型和模拟的现实约束。 ?? 世界一流的、积极参与的实验人员：我们寻求生物和计算参与者之间的紧密整合和深入互动。 对疾病具有重要影响：我们确保我们的 DBP 与疾病过程或治疗相关，以确保它们有助于促进人类健康。 我们过去的 DBP 符合这些标准，使我们能够开发出现在被广泛的研究人员社区使用的软件。我们接下来的三个 DBP 也满足这些标准，下面简要回顾一下。