TISSUE KINETICS MODEL FOR ARTIFICIAL CELL/TISSUE ARRAY CONSTRUCTS

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。 描述创建四维模型所需的基本参数， 自主生长的虚拟组织阵列结构。 这些准则和 变量将模拟生物过程和结构， 灵活和可扩展到广泛的潜在研究 应用. 该结构将模拟组织的四维生长动力学 而不是在细胞水平上利用循环的基本原理 动力学 该模型应符合我们的基本概念， 异常生长动力学，包括存在或不存在 生物生理调节剂 该动力学模型可以模拟肿瘤和非肿瘤组织， 例如导管内和浸润性乳腺癌以及免疫 对癌症的反应。 编程语言的选择将是JAVA和/或PYTHON， 都是可扩展的面向对象的平台独立的高级 编程语言，可以支持最终的发展， 互联网可访问的用户界面的交互式模型。 数据集将动态写入启用PYTHON的渲染 程序（trueSapce V6.6），它可以创建一个三维模型， 结构的演变。 程序流程概述如下： 单元类的实例将通过对象实例化 面向语言 第一个像元将被指定为空间坐标 0,0,0. 实例化的时间将被记录为实例变量。 在给定的时间段后（根据计算机时钟进行检查），细胞 有机会进入细胞周期的增殖期。 这一过程的相对时间将模拟实际的细胞周期 医学文献中引用的动力学数据。 例如，如果 细胞进入细胞周期的下一个阶段， 它在整个循环中的渡越时间，那么第二个细胞物体将是 实例化并分配给相邻的未占用坐标。 的 坐标系可以被预先配置（以模拟无生长区域或 组织结构），并且也可以使用极坐标系。 进入的可能性、过境的持续时间和最终离开 从单个细胞周期阶段将被引导的存在或 缺少实例变量。 例如，雌激素或 乳腺癌细胞上的孕酮受体可以用 具有0.0-1.0之间的真实的数的变量，其中较高的值表示 受体密度增加。 不仅可以设定生物参数 但这需要细胞周期特异性功能来调节转运 基于标记物的存在或不存在， 根据细胞周期的各个阶段来管理单个细胞对象， 代表阳性和阴性细胞周期的变量的平衡 修饰语 可以实例化cell对象的子类来表示正常的 细胞可能模拟对细胞亚类的免疫应答， 代表肿瘤细胞。 通过这种方式， 细胞与虚拟化疗药物的相互作用（正常和异常） 化合物（具有已知的细胞周期效应）、放射疗法或甚至 微重力环境 同样的方法可以应用于细胞分化， 依赖变量或实例变量将触发对单元格的调用 分化功能。

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. Describe basic parameters needed to create a quadri-dimmensional model for autonomously growing virtual tissue array constructs. These guidelines and variables would simulate biological process and structure while remaining flexible and extensible to a broad range of potential research applications. The constructs would model quadri-dimensional growth kinetics at the tissue rather than cellular level taking advantage of basic principles of cycle kinetics. The model should conform to our basic concept of both normal and abnormal growth kinetics including the presence or absence of biophyisologic modifiers. This kinetic model could model neoplastic and non-neoplastic tissue for example Intraductal and Invasive breast cancer as well as the immune response to cancer. The choice of programming language would be either JAVA and/or PYTHON which are both extensible object oriented platform independent high level programming languages which could support the eventual development of an internet accessible user interface to the interactive model. The dataset would be dynamically written to a PYTHON enabled rendering program (trueSapce V6.6) which could create a three dimensional model of the construct as it evolves. An outline of the program flow follows: An instance of a cell class would be instantiated through an object oriented language. The first cell would be assigned to spatial coordinates 0,0,0. The time of instantiation would be logged as an instance variable. After a given period of time (checked against the computer clock) the cell is given an opportunity to enter the proliferative phase of the cell cycle. The relative timing of this process would simulate actual cell cycle kinetic data referenced in the medical literature. If, for example, the cell does enter the next phase of the cell cycle and ultimately completes its transit time through the cycle then a second cell object would be instantiated and assigned to an adjacent unoccupied coordinate. The coordinate system could be pre-configured (to simulate no growth areas or tissue structures), and could also use a polar coordinate system. The likelihood of entering, the duration of transit and the ultimate exit from the individual cell cycle phases would be guided by the presence or absence of instance variables. For example the presence of estrogen or progesterone receptor on a breast cancer cell could be represented by a variable with a real number from 0.0-1.0 with higher values representing increased density of receptor. Not only could biological parameters be set in this way but calls to cell cycle specific functions to regulate transit through the cell cycle based on the presence or absence of markers could govern individual cell-objects though the cell cycle phases based on a balance of variables representing both positive and negative cell cycle modifiers. Subclasses of the cell object could be instantiated to represent normal cells possibly modeling an immune response to cell subclasses which represent tumorous cells. In this way it might be possible to simulate cell interactions (both normal and abnormal) with virtual chemotherapeutic compounds (with known cell cycle effects), radiation therapy or even microgravity environments. The same approach could be applied to cell differentiation where time dependent variables or instance variables would trigger calls to cell differentiation functions.