Tegnér Jesper, Björkegren Johan
Division of Computational Biology, Department of Physics, Chemistry and Biology, The Institute of Technology, Linköping University, SE-581 83 Linköping, Sweden.
Trends Genet. 2007 Jan;23(1):34-41. doi: 10.1016/j.tig.2006.11.003. Epub 2006 Nov 13.
After the major achievements of the DNA sequencing projects, an equally important challenge now is to uncover the functional relationships among genes (i.e. gene networks). It has become increasingly clear that computational algorithms are crucial for extracting meaningful information from the massive amount of data generated by high-throughput genome-wide technologies. Here, we summarise how systems identification algorithms, originating from physics and control theory, have been adapted for use in biology. We also explain how experimental perturbations combined with genome-wide measurements are being used to uncover gene networks. Perturbation techniques could pave the way for identifying gene networks in more complex settings such as multifactorial diseases and for improving the efficacy of drug evaluation.
在DNA测序项目取得重大成果之后，当前一项同样重要的挑战是揭示基因之间的功能关系（即基因网络）。越来越明显的是，计算算法对于从高通量全基因组技术产生的海量数据中提取有意义的信息至关重要。在此，我们总结了源自物理学和控制理论的系统识别算法是如何被应用于生物学的。我们还解释了实验扰动与全基因组测量相结合是如何用于揭示基因网络的。扰动技术可能为在诸如多因素疾病等更复杂的情况下识别基因网络以及提高药物评估的效力铺平道路。
