基于通用组合方法的分子网络逆向工程
Reverse Engineering of Molecular Networks from a Common Combinatorial Approach
摘要：
分子细胞生物学的理解需要深入了解网络的结构和动力学，网络由成千上万的DNA，RNA，蛋白质，代谢物和其他成分的相互作用分子组成。系统生物学的中心目标之一是解开这些组件之间尚未充分表征的复杂相互作用网络。由于在实验工作中不断发现新的物种和相互作用，需要开发自适应和快速的网络构建和更新算法，因此这项工作变得更加困难。因此，从数据中对网络进行“逆向工程”已经成为系统生物学研究的中心问题之一。基于来自统计学、机器学习和其他数学领域的工具，已经开发了各种逆向工程方法。为了有效地使用这些方法，有必要了解这些算法的基本特征。考虑到这一点，本章致力于生物系统的逆向工程。具体来说，我们把注意力集中在一类特殊的方法进行逆向工程，即那些依靠算法上的所谓的“命中集”的问题，这是一个经典的组合和计算机科学问题，这些方法中的每一个利用不同的算法，以获得精确或近似的解决方案的命中集问题。我们将探索替代算法对已发表的计算机生物网络的推断的最终影响。
英文摘要：
The understanding of molecular cell biology requires insight into the structure and dynamics of networks that are made up of thousands of interacting molecules of DNA, RNA, proteins, metabolites, and other components. One of the central goals of systems biology is the unraveling of the as yet poorly characterized complex web of interactions among these components. This work is made harder by the fact that new species and interactions are continuously discovered in experimental work, necessitating the development of adaptive and fast algorithms for network construction and updating. Thus, the "reverse-engineering" of networks from data has emerged as one of the central concern of systems biology research. A variety of reverse-engineering methods have been developed, based on tools from statistics, machine learning, and other mathematical domains. In order to effectively use these methods, it is essential to develop an understanding of the fundamental characteristics of these algorithms. With that in mind, this chapter is dedicated to the reverse-engineering of biological systems. Specifically, we focus our attention on a particular class of methods for reverse-engineering, namely those that rely algorithmically upon the so-called "hitting-set" problem, which is a classical combinatorial and computer science problem, Each of these methods utilizes a different algorithm in order to obtain an exact or an approximate solution of the hitting set problem. We will explore the ultimate impact that the alternative algorithms have on the inference of published in silico biological networks.
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