标题： 从共同组合方法中逆向工程分子网络 显示英文标题

标题： Reverse Engineering of Molecular Networks from a Common Combinatorial Approach

摘要： 理解分子细胞生物学需要深入了解由数千种相互作用的分子（如DNA、RNA、蛋白质、代谢物和其他成分）组成的网络的结构和动态。系统生物学的一个核心目标是揭示这些成分之间尚未充分表征的复杂相互作用网络。由于实验工作中不断发现新的物种和相互作用，这使得这项工作变得更加困难，从而需要开发适应性强且快速的算法来进行网络构建和更新。因此，从数据中“反向工程”网络已成为系统生物学研究的核心关注点之一。已经开发出多种反向工程方法，这些方法基于统计学、机器学习和其他数学领域的工具。为了有效使用这些方法，必须了解这些算法的基本特征。本着这一目的，本章专门讨论生物系统的反向工程。具体而言，我们关注一种特定的反向工程方法，即那些在算法上依赖于所谓的“击中集”问题的方法，这是一个经典的组合数学和计算机科学问题。这些方法中的每一种都使用不同的算法来获得击中集问题的精确或近似解。我们将探讨不同算法对已发表的体外生物网络推断的最终影响。 显示英文摘要

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.