【佳學基因檢測】癌癥建模：動態(tài)系統(tǒng)中“組學”信息的整合

上海腫瘤基因檢測機構機會

參加學術會議時了解《J Bioinform Comput Biol》在?2007 Aug;5(4):977-86發(fā)表了一篇題目為《癌癥建模：動態(tài)系統(tǒng)中“組學”信息的整合》腫瘤靶向藥物治療基因檢測臨床研究文章。該研究由Beatriz Stransky,?Junior Barrera,?Lucila Ohno-Machado,?Sandro J De Souza等完成。促進了腫瘤的正確治療與個性化用藥的發(fā)展，進一步強調(diào)了基因信息檢測與分析的重要性。

腫瘤靶向藥物及正確治療臨床研究內(nèi)容關鍵詞：

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腫瘤靶向治療基因檢測臨床應用結果

過去 10 年見證了許多技術的興起，這些技術從許多生物中產(chǎn)生了前所未有的基因組規(guī)模數(shù)據(jù)。盡管研究界已經(jīng)成功地探索了這些數(shù)據(jù)，但仍然存在許多挑戰(zhàn)。其中之一是將這些數(shù)據(jù)集有效地直接集成到基于生物系統(tǒng)數(shù)學建模的方法中。在癌癥中的應用就是一個很好的例子。癌癥信息和建模之間的橋梁可以通過兩種主要類型的互補策略來實現(xiàn)。首先，有一種自下而上的方法，其中數(shù)據(jù)生成有關給定系統(tǒng)組件之間的結構和關系的信息。此外，還有一種自上而下的方法，其中控制論和系統(tǒng)理論知識用于創(chuàng)建描述系統(tǒng)機制和動態(tài)的模型。這些方法也可以與產(chǎn)生結合詳細機制和廣泛生物學范圍的多尺度模型聯(lián)系起來。在這里，我們給出了該領域的總體情況，并討論了應對未來主要挑戰(zhàn)的可能策略。

腫瘤發(fā)生與反復轉(zhuǎn)移國際數(shù)據(jù)庫描述：

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.