Current State-of-The-Art and Future Directions in Systems Biology
Current State-of-The-Art and Future Directions in Systems Biology
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系统生物学的当前最新技术和未来方向
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
P. Imoukhuede
中科院分区：
文献类型：
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作者：
Si Chen;Ali Ansari;William Sterrett;Kate Hurley;Jeremy Kemball;Jared Weddell;P. Imoukhuede
系统生物学提供了解码遗传信息、优化药物设计和帮助发展精准医学的希望。这些进步需要从实验数据中获取信息并通过计算建模整合这些信息的双峰方法。然而，选择适当的实验分析和计算模型对输出的准确性和相关性至关重要。在这里，我们将深入研究几种常用建模方法的基本概念、它们的优点和局限性，以及潜在的应用。我们回顾和比较系统生物学中使用的实验分析，基于吞吐量，简单性和量化的可能性。此外，我们回顾了当前与分析相结合的实验模型，为计算建模提供参数和/或验证。最后，我们介绍了系统生物学在医学中的应用：案例研究，临床机会和系统生物学的未来方向
Systems Biology offers the promise of decoding genetic information, optimizing pharmaceutical design, and aiding in the development of precision medicine. These advances require the bimodal approach of deriving information from experimental data and integrating such information via computational modeling. However, choosing an appropriate experimental assay and computational model is paramount to the accuracy and relevancy of the output. Here, we delve into the fundamental concept of several commonly used modeling approaches, their advantages and limitations, as well as potential applications. We review and compare experimental assays used in systems biology, based on the throughput, simplicity and possibility for quantification. In addition, we review current experimental models used in conjunction with assays to provide parameters and/or validation for computational modeling. Lastly, we present applications of systems biology in medicine: case studies, clinical opportunities, and future directions of systems biology