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摘要翻译：
癌变过程是一个复杂的过程，它涉及在微环境扰动的影响下动态地相互连接的模块化子网络，以非随机的伪马尔可夫链过程演化。因此，一个恰当的癌变N阶段模型涉及复杂功能基因组和细胞间期非线性动态转换的N值逻辑处理。细胞中遗传网络和信号通路的Lukasiewicz代数逻辑模型是根据具有n态成分的非线性动力系统建立的，它允许对先前的、布尔或“模糊”的、体内遗传活动的逻辑模型进行推广。然后，基于NCI支持的CGAP数据库中非常广泛的基因组转录和翻译数据，这些模型被应用于癌变过程中的细胞转化。这些模型用一个具有n值Lukasiewicz代数逻辑子对象分类器描述的Lukasiewicz-topos表示，该分类器描述了非随机和非线性网络活动及其致癌性转换。通过对LT非随机、伪马尔可夫链过程的动态状态空间的描述，以及基于cDNA和蛋白质组学的网络模型，利用超灵敏技术进行高通量分析，得到了不同类型癌症的具体模型。这项新的理论分析是基于广泛的人类肿瘤CGAP基因组数据，以及最近发表的关于细胞周期蛋白信号传导的研究。通过在III期癌症中重建细胞周期抑制，几个这样的特异模型提示了新的临床试验和合理的癌症治疗方法。
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英文标题：
《Complex Systems Analysis of Cell Cycling Models in Carcinogenesis》
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作者：
I.C. Baianu
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最新提交年份：
2004
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
Carcinogenesis is a complex process that involves dynamically inter-connected modular sub-networks that evolve under the influence of micro-environmentally induced perturbations, in non-random, pseudo-Markov chain processes. An appropriate n-stage model of carcinogenesis involves therefore n-valued Logic treatments of nonlinear dynamic transformations of complex functional genomes and cell interactomes. Lukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous, Boolean or "fuzzy", logic models of genetic activities in vivo. Such models are then applied to cell transformations during carcinogenesis based on very extensive genomic transcription and translation data from the CGAP databases supported by NCI. Such models are represented in a Lukasiewicz-Topos with an n-valued Lukasiewicz Algebraic Logics subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in carcinogeness. Specific models for different types of cancer are then derived from representations of the dynamic state-space of LT non-random, pseudo-Markov chain process, network models in terms of cDNA and proteomic, high throughput analyses by ultra-sensitive techniques. This novel theoretical analysis is based on extensive CGAP genomic data for human tumors, as well as recently published studies of cyclin signaling. Several such specific models suggest novel clinical trials and rational therapies of cancer through re-establishment of cell cycling inhibition in stage III cancers.
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PDF链接：
https://arxiv.org/pdf/q-bio/0406045
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