PARABIOTIC STEM CELL

共生干细胞

• 批准号: 7956208
• 负责人: RICHARD H. SIDERITS
• 金额: $ 0.1万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956208
• 关键词: Algorithms; Architecture; Biomedical Research; Cell Cycle; Cell Cycle Kinetics; Code; Computer Retrieval of Information on Scientific Projects Database; Data; Daughter; Development; Environment; Feeds; Funding; Grant; High Performance Computing; Human; Institution; Internet; Modeling; Monitor; Notification; Process; Research; Research Personnel; Resources; Source; Stem cells; System; Time; United States National Institutes of Health; Update; base; digital models; heuristics; platform-independent; programs; remote sensing; stem cell differentiation; systems research; time use

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This research will model digital biodynamic principles of stem cell differentiation within a self organizing parabiotic construct. The algorithm development and code optimization are platform independent. Modeling dynamically differentiable processes within a self organizing construct will require a super computing environment. The model is based on an object oriented and multithreaded programming architecture. Once instantiated an object of class "Stem_Cell" follows known human cell cycle kinetic principles and may then instantiate other daughter objects of this class. Once instantiated each object generates class Threads. The object's threads 1) sense remote data feeds; 2) monitor progression through the parabiotic cell cycle; 3) identify and categorize other instances of related objects; 4) differentiate functionally based on real time parameters. All instances of a given class of differentiating objects may be updated with process variables simultaneously and in real time using a class-notification ("Borg") algorithm. This result will be a Generationally Organized Latent Evolutional Matrix ("Golem") accessible through an internet based Parabiotic system console. Applications include modeling of Stem cell differentiation; self organizing and heuristic systems research, progressively adaptive neuromic matrix development for decision and system control interfaces.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 这项研究将在一个自组织的联体结构中模拟干细胞分化的数字生物动力学原理。算法开发和代码优化与平台无关。在一个自组织结构中对动态微分过程进行建模需要一个超级计算环境。该模型是基于面向对象和多线程的编程架构。一旦实例化，类“Stem_Cell”的对象遵循已知的人类细胞周期动力学原理，然后可以实例化该类的其他子对象。一旦实例化，每个对象生成类Threads。对象的线程1）感测远程数据馈送; 2）监测通过共生细胞周期的进展; 3）识别和分类相关对象的其他实例; 4）基于真实的时间参数在功能上进行区分。给定类别的区分对象的所有实例可以使用类别通知（“博格”）算法用过程变量同时且真实的更新。这一结果将是一个世代组织的潜在进化矩阵（“Golem”），可通过基于互联网的共生系统控制台访问。应用包括干细胞分化的建模;自组织和启发式系统研究，逐步适应决策和系统控制界面的神经元矩阵开发。