Veeramani Balaji, Bader Joel S
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
J Comput Biol. 2009 Feb;16(2):291-302. doi: 10.1089/cmb.2008.14TT.
Many diseases are caused by failures of metabolic enzymes. These enzymes exist in the context of networks defined by the static topology of enzyme-metabolite interactions and by the reaction fluxes that are feasible at steady state. We use the local topology and the flux correlations to identify how failures in the metabolic network may lead to disease. First, using yeast as a model, we show that flux correlations are a powerful predictor of pairwise mutations that lead to cell death -- more powerful, in fact, than computational models that directly estimate the effects of mutations on cell fitness. These flux correlations, which can exist between enzymes far-separated in the metabolic network, add information to the structural correlations evident from shared metabolites. Second, we show that flux correlations in human align with similarities in Mendelian phenotypes ascribed to known genes. These methods will be useful in predicting genetic interactions in model organisms and understanding the combinatorial effects of genetic variations in humans.
许多疾病是由代谢酶功能异常引起的。这些酶存在于由酶-代谢物相互作用的静态拓扑结构以及稳态下可行的反应通量所定义的网络环境中。我们利用局部拓扑结构和通量相关性来确定代谢网络中的功能异常是如何导致疾病的。首先，以酵母为模型，我们表明通量相关性是导致细胞死亡的成对突变的有力预测指标——事实上，比直接估计突变对细胞适应性影响的计算模型更具预测力。这些通量相关性可能存在于代谢网络中相距甚远的酶之间，为从共享代谢物中明显看出的结构相关性增添了信息。其次，我们表明人类中的通量相关性与归因于已知基因的孟德尔表型的相似性相符。这些方法将有助于预测模式生物中的基因相互作用，并理解人类基因变异的组合效应。
