预测代谢网络中的合成救援
Predicting synthetic rescues in metabolic networks
摘要：
医学研究的一个重要目标是开发恢复由于突变和其他缺陷而丧失的细胞功能的方法。许多基于基因治疗的方法旨在修复缺陷基因或插入具有补偿功能的基因。在这里，我们提出了一种替代的，基于网络的策略，旨在通过迫使细胞绕过受缺陷基因影响的功能，或补偿失去的功能来恢复生物功能。专注于单细胞生物体的代谢，我们计算研究缺乏必需酶的突变体，因此无法生长或生长速度显着降低。我们表明，这些突变体中的几个可以变成可行的生物体通过额外的基因缺失，恢复其生长速度。以一种相当违反直觉的方式，这是通过对代谢网络的额外损害来实现的。使用通量平衡为基础的方法，我们确定了一些合成可行的基因对，其中一个酶编码基因的去除导致一个不可行的表型，而第二个酶编码基因的删除拯救生物体。系统的基于网络的补偿性拯救效应的识别可能为遗传干预开辟新的途径。
英文摘要：
An important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory function. Here, we propose an alternative, network-based strategy that aims to restore biological function by forcing the cell to either bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of single-cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network. Using flux balance-based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one enzyme-encoding gene results in a nonviable phenotype, while the deletion of a second enzyme-encoding gene rescues the organism. The systematic network-based identification of compensatory rescue effects may open new avenues for genetic interventions.
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