Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation.
Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation.
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DOI：
10.1186/1752-0509-4-128
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发表时间：
2010-09-10
影响因子：
--
通讯作者：
Wang W
中科院分区：
文献类型：
--
作者：
Shen L;Chepelev I;Liu J;Wang W
遗传网络的一个令人兴奋的应用是预测环境线索或遗传扰动的表型后果。然而，基于网络拓扑的定量表型的从头预测始终是一项具有挑战性的任务。使用酵母孢子形成作为模型系统，我们从文献中组装了一个遗传网络，并利用布尔网络来预测删除单个基因后孢子形成效率的变化。我们观察到，基于策划网络的预测与实验测量值密切相关。此外，计算分析揭示了酵母孢子形成网络的鲁棒性和滞后性，并揭示了双基因缺失引起的孢子形成效率变化的几种模式。这些发现可能会指导未来对潜在机制的研究。我们还表明，根据时间微阵列数据和文献重建的杂交遗传网络能够对相同的定量表型实现令人满意的预测精度。本案例研究说明了基于遗传网络预测定量表型的价值，并提供了一种通用方法。
An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task. Using yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes. This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.