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Practical Guidelines for Incorporating Knowledge-Based and Data-Driven Strategies into the Inference of Gene Regulatory Networks

机译:将基于知识和数据驱动策略纳入基因调控网络推理的实用指南

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Modeling gene regulatory networks (GRNs) is essential for conceptualizing how genes are expressed and how they influence each other. Typically, a reverse engineering approach is employed; this strategy is effective in reproducing possible fitting models of GRNs. To use this strategy, however, two daunting tasks must be undertaken: one task is to optimize the accuracy of inferred network behaviors; and the other task is to designate valid biological topologies for target networks. Although existing studies have addressed these two tasks for years, few of the studies can satisfy both of the requirements simultaneously. To address these difficulties, we propose an integrative modeling framework that combines knowledge-based and data-driven input sources to construct biological topologies with their corresponding network behaviors. To validate the proposed approach, a real dataset collected from the cell cycle of the yeast S. cerevisiae is used. The results show that the proposed framework can successfully infer solutions that meet the requirements of both the network behaviors and biological structures. Therefore, the outcomes are exploitable for future in vivo experimental design.
机译:建立基因调控网络(GRN)模型对于概念化基因表达方式以及它们如何相互影响至关重要。通常,采用逆向工程方法。该策略有效地再现了GRN的可能拟合模型。但是,要使用这种策略,必须执行两项艰巨的任务:一项任务是优化推断的网络行为的准确性;另一项是优化网络行为的准确性。另一个任务是为目标网络指定有效的生物拓扑。尽管现有的研究已经解决了这两个任务多年,但很少有研究能够同时满足这两个要求。为了解决这些困难,我们提出了一个集成的建模框架,该框架结合了基于知识和数据驱动的输入源,以构建具有相应网络行为的生物拓扑。为了验证所提出的方法,使用了从酵母酿酒酵母的细胞周期收集的真实数据集。结果表明,提出的框架可以成功地推断出满足网络行为和生物学结构要求的解决方案。因此,结果可用于将来的体内实验设计。

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