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有各种技术用于捕获mRNA、蛋白质的动态变化和翻译后修饰。<font color="#FF8000">生物力学 Mechanobiology</font>，跨尺度的力学和物理性质，以及它们与其他调节机制的相互作用；生物符号学，分析有机体或其他生物系统的符号关系系统；生理组学，生物学中生理的系统研究。

有各种技术用于捕获mRNA、蛋白质的动态变化和翻译后修饰。<font color="#FF8000">生物力学 Mechanobiology</font>，跨尺度的力学和物理性质，以及它们与其他调节机制的相互作用；生物符号学，分析有机体或其他生物系统的符号关系系统；生理组学，生物学中生理的系统研究。

 

[[Cancer systems biology]] is an example of the systems biology approach, which can be distinguished by the specific object of study ([[tumorigenesis]] and [[Cancer treatment|treatment of cancer]]). It works with the specific data  (patient samples, high-throughput data with particular attention to characterizing [[Cancer genome sequencing|cancer genome]] in patient tumour samples) and tools (immortalized cancer [[cell lines]], [[Animal testing on rodents|mouse models]] of tumorigenesis, [[xenograft]] models, [[high-throughput sequencing]] methods, siRNA-based gene knocking down [[high-throughput screening]]s, computational modeling of the consequences of somatic [[mutations]] and [[genome instability]]).<ref name="barillot13">{{cite book|last1=Barillot|first1 =Emmanuel |last2=Calzone|first2=Laurence|last3=Hupe|first3=Philippe|last4=Vert|first4 =Jean-Philippe|last5=Zinovyev|first5=Andrei|title=Computational Systems Biology of Cancer|year=2012|publisher=Chapman & Hall/CRCMathematical & Computational Biology|isbn=978-1439831441|page=461}}</ref> The long-term objective of the systems biology of cancer is ability to better diagnose cancer, classify it and better predict the outcome of a suggested treatment, which is a basis for [[Personalized medicine#Cancer management|personalized cancer medicine]] and [[Virtual Physiological Human|virtual cancer patient]] in more distant prospective. Significant efforts in computational systems biology of cancer have been made in creating realistic multi-scale ''[[in silico]]'' models of various tumours.<ref name="byrne2010">

[[Cancer systems biology]] is an example of the systems biology approach, which can be distinguished by the specific object of study ([[tumorigenesis]] and [[Cancer treatment|treatment of cancer]]). It works with the specific data  (patient samples, high-throughput data with particular attention to characterizing [[Cancer genome sequencing|cancer genome]] in patient tumour samples) and tools (immortalized cancer [[cell lines]], [[Animal testing on rodents|mouse models]] of tumorigenesis, [[xenograft]] models, [[high-throughput sequencing]] methods, siRNA-based gene knocking down [[high-throughput screening]]s, computational modeling of the consequences of somatic [[mutations]] and [[genome instability]]).<ref name="barillot13">{{cite book|last1=Barillot|first1 =Emmanuel |last2=Calzone|first2=Laurence|last3=Hupe|first3=Philippe|last4=Vert|first4 =Jean-Philippe|last5=Zinovyev|first5=Andrei|title=Computational Systems Biology of Cancer|year=2012|publisher=Chapman & Hall/CRCMathematical & Computational Biology|isbn=978-1439831441|page=461}}</ref> The long-term objective of the systems biology of cancer is ability to better diagnose cancer, classify it and better predict the outcome of a suggested treatment, which is a basis for [[Personalized medicine#Cancer management|personalized cancer medicine]] and [[Virtual Physiological Human|virtual cancer patient]] in more distant prospective. Significant efforts in computational systems biology of cancer have been made in creating realistic multi-scale ''[[in silico]]'' models of various tumours.<ref name="byrne2010">