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.