目的 癌细胞致死基因的研究是治疗癌症的重要尝试,其发现依赖于生物学上的基因敲除实验.鉴于此类实验的高代价、长周期等不足,本文给出一种基于计算机仿真的预测癌细胞致死基因的方法,旨在运用计算机仿真技术预测对癌细胞研究有价值的信息,为基因敲除的生物实验提供线索,帮助降低实验成本.方法 计算机仿真实验基于人类全基因组代谢网络,融合癌细胞的基因表达数据,用一种基于约束建模的方法,以预测癌细胞代谢网络中有活性的反应,并将这些反应命名为"core reactions",将"core reactions"作为输入,重构出一致的癌症工作网络.在该网络上,使用基于约束的流量平衡基因敲除算法,获取使得工作网络biomass产量为0的基因,即致死基因.结果 以肾透明细胞癌为例,根据上述算法,计算结果给出了肾透明细胞癌潜在的7个致死基因.结论 本文给出一种高通量数据结合代谢网络,构造癌症特异网络,并通过计算机仿真基因敲除以获取癌细胞致死基因的方法.此方法通用、高效,有助于更好地开展生物学实验.%Objective Research on essential genes in cancer cells is an important attempt to treat cancer,its discovery depends on gene knockout experiments. Considering the high cost and long periods of this kind of tests, we present a method for predicting the essential genes in cancer cells based on computer simulation. We aim to use computer simulation techniques to predict valuable information in cancer cell research,provide clues to knockout experiments,and reduce experimental costs. Methods The in silico knockout test is based on the genome?scale human metabolic network. Integrating the transcriptomics data of cancer cells, we use a mathematic model to predict the "core reactions" of cancer cells. Then these reactions are utilized to reconstruct the cancer cell specific network. The in silico knockout experiment is based on this network. Finally we get the essential genes which make biomass production to be 0 after knockout by using constraint?based flux balance analysis ( FBA ) knockout algorithm. Results We choose clear cell kidney carcinoma cells as an example. According to the whole set of algorithms mentioned in Methods section, we calculate 7 potential essential genes. Conclusions This work proposes a simulation based method which connects transcriptomics data and metabolic network to create a cancer specific network to study on essential genes of cancer cells. The method is universal and efficient, and helpful to make wet experiments better.
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