PARABIOTIC STEM CELL

共生干细胞

• 批准号: 7723347
• 负责人: RICHARD H. SIDERITS
• 金额: $ 0.05万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723347
• 关键词: Algorithms; Architecture; Cell Cycle; Cell Cycle Kinetics; Cell Differentiation process; Class; Code; Computer Retrieval of Information on Scientific Projects Database; Data; Daughter; Development; Environment; Feeds; Funding; Grant; Human; Institution; Internet; Modeling; Monitor; Notification; Object Class; 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; 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利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 这项研究将在自组织的共生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是平台无关的。在自组织结构中对动态可区分过程进行建模将需要一个超级计算环境。该模型基于面向对象的多线程编程体系结构。一旦被实例化，“Stem_Cell”类的对象就遵循已知的人类细胞周期动力学原理，然后可以实例化这个类的其他子对象。实例化后，每个对象都会生成类线程。该对象的线程1)侦听远程数据馈送；2)通过异生细胞周期监测进展；3)识别和分类相关对象的其他实例；4)基于实时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(Borg)算法用过程变量同时和实时地更新。这一结果将是一个世代组织的潜在进化矩阵(“GOEM”)，可以通过基于互联网的副生物系统控制台访问。应用包括干细胞分化的建模；自组织和启发式系统研究；用于决策和系统控制接口的渐进式自适应神经矩阵开发。