Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo 36208, Spain.
Department of Chemistry, Stanford University, Stanford, CA 94305, USA.
Cells. 2013 May 10;2(2):306-29. doi: 10.3390/cells2020306.
Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets.
构建细胞网络的数学模型是系统生物学的核心。这涉及到重建分子成分之间相互作用的结构，这被称为网络推断或反向工程。信息论可以帮助从可用数据中提取尽可能多的信息。在文献中提出了大量基于这些概念的方法，不仅在生物学期刊上，而且在广泛的领域中。由于不同的重点和采用不同的术语，对它们进行批判性比较是很困难的。在这里，我们试图回顾一些现有的网络推断信息论方法，并澄清它们的差异。虽然这些方法中的一些已经取得了显著的成功，但仍有许多挑战需要克服，其中包括处理不完整的测量、噪声数据、非线性关系或反馈循环产生的反直觉行为，以及处理大数据集的计算负担。
