Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.
Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.
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DOI：
10.1371/journal.pcbi.0020052
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发表时间：
2006-05
影响因子：
4.3
通讯作者：
Palsson BO
中科院分区：
文献类型：
--
作者：
Barrett CL;Palsson BO
目前，完整的、公开的基因组序列数量已超过 200 个，随着宏基因组和环境测序工作的升级以及测序成本的下降，这一数字预计在不久的将来将迅速增长。为了利用这些数据来理解特定的生物体并辨别生物体如何发挥作用的一般原理，有必要重建它们的各种生化反应网络。其中最主要的是转录调控网络。考虑到这些网络的物理和逻辑复杂性、可用于阐明的各种来源（通常是嘈杂的）数据、所涉及的货币成本以及可以进行的大量潜在实验（~1012），实验设计算法将需要综合各种计算和实验数据，以最大限度地提高监管网络重建的效率。本文提出了一种实验设计算法，可以系统、有效地重建转录调控网络。它旨在与实验实验室组件结合迭代应用。该算法是在重建大肠杆菌代谢转录调控的背景下提出的，并且通过对之前进行的实验进行回顾性分析，我们表明所产生的实验设计符合人类设计实验的方式。该算法能够利用基于广泛的计算和实验来源的概率估计来建议最有可能发现最大数量的新监管知识的实验。近年来，全基因组测序的出现加强了对生命的探索。这个新的数据源极大地促进了基因组规模代谢网络的重建。代谢重建后，有必要发现生物体内运作的遗传控制机制。转录调控网络 (TRN) 重建在时间和金钱方面都是昂贵的，因此重建工作尽可能高效至关重要。实验的设计必须能够在每次实验中发现最新的监管知识。大量可能的实验（~1012）和可用于设计实验的大量异质数据压倒了人类的同化能力。作者开发了一种算法，利用重建代谢网络的数学模型与部分重建的 TRN 相结合，来识别最有可能产生最新监管知识的实验设计。作者表明，所产生的实验设计与人类专家所产生的实验设计相似，并且该算法具有整合任何相关数据源来设计此类实验的功能。
The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy) data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments (~1012) that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge. In recent years, the exploration of life has been bolstered through the advent of whole genome sequencing. This new data source significantly enables the reconstruction of genome-scale metabolic networks. After a metabolic reconstruction, it will be necessary to discover the genetic control mechanisms that operate within an organism. Transcriptional regulatory network (TRN) reconstruction is costly both in terms of time and money, so it is critical that the reconstruction efforts be made as efficient as possible. Experiments must be designed so that the most new regulatory knowledge is discovered in each experiment. The huge number of possible experiments (~1012) and the vast amount of heterogeneous data available for designing experiments overwhelms the human ability to assimilate. The authors have developed an algorithm that utilizes a mathematical model of a reconstructed metabolic network integrated with a partially reconstructed TRN to identify the experiment designs with the highest potential of yielding the most new regulatory knowledge. The authors show that the produced experiment designs are similar to those a human expert would produce, and that the algorithm has a facility to incorporate any relevant data source to design such experiments.
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