【佳学基因检测】癌症建模：动态系统中“组学”信息的整合

上海肿瘤基因检测机构机会

参加学术会议时了解《J Bioinform Comput Biol》在 2007 Aug;5(4):977-86发表了一篇题目为《癌症建模：动态系统中“组学”信息的整合》肿瘤靶向药物治疗基因检测临床研究文章。该研究由Beatriz Stransky, Junior Barrera, Lucila Ohno-Machado, Sandro J De Souza等完成。促进了肿瘤的精准治疗与个性化用药的发展，进一步强调了基因信息检测与分析的重要性。

肿瘤靶向药物及精准治疗临床研究内容关键词：

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肿瘤靶向治疗基因检测临床应用结果

过去 10 年见证了许多技术的兴起，这些技术从许多生物中产生了前所未有的基因组规模数据。尽管研究界已经成功地探索了这些数据，但仍然存在许多挑战。其中之一是将这些数据集有效地直接集成到基于生物系统数学建模的方法中。在癌症中的应用就是一个很好的例子。癌症信息和建模之间的桥梁可以通过两种主要类型的互补策略来实现。首先，有一种自下而上的方法，其中数据生成有关给定系统组件之间的结构和关系的信息。此外，还有一种自上而下的方法，其中控制论和系统理论知识用于创建描述系统机制和动态的模型。这些方法也可以与产生结合详细机制和广泛生物学范围的多尺度模型联系起来。在这里，我们给出了该领域的总体情况，并讨论了应对未来主要挑战的可能策略。

肿瘤发生与复发转移国际数据库描述：

The last 10 years have seen the rise of many technologies that produce an unprecedented amount of genome-scale data from many organisms. Although the research community has been successful in exploring these data, many challenges still persist. One of them is the effective integration of such data sets directly into approaches based on mathematical modeling of biological systems. Applications in cancer are a good example. The bridge between information and modeling in cancer can be achieved by two major types of complementary strategies. First, there is a bottom-up approach, in which data generates information about structure and relationship between components of a given system. In addition, there is a top-down approach, where cybernetic and systems-theoretical knowledge are used to create models that describe mechanisms and dynamics of the system. These approaches can also be linked to yield multi-scale models combining detailed mechanism and wide biological scope. Here we give an overall picture of this field and discuss possible strategies to approach the major challenges ahead.