Beijing Wuzi University, School of Information, Beijing, People's Republic of China.
IET Syst Biol. 2009 Nov;3(6):523-33. doi: 10.1049/iet-syb.2008.0166.
High-throughput techniques produce massive data on a genome-wide scale which facilitate pharmaceutical research. Drug target discovery is a crucial step in the drug discovery process and also plays a vital role in therapeutics. In this study, the problem of detecting drug targets was addressed, which finds a set of enzymes whose inhibition stops the production of a given set of target compounds and meanwhile minimally eliminates non-target compounds in the context of metabolic networks. The model aims to make the side effects of drugs as small as possible and thus has practical significance of potential pharmaceutical applications. Specifically, by exploiting special features of metabolic systems, a novel approach was proposed to exactly formulate this drug target detection problem as an integer linear programming model, which ensures that optimal solutions can be found efficiently without any heuristic manipulations. To verify the effectiveness of our approach, computational experiments on both Escherichia coli and Homo sapiens metabolic pathways were conducted. The results show that our approach can identify the optimal drug targets in an exact and efficient manner. In particular, it can be applied to large-scale networks including the whole metabolic networks from most organisms.
高通量技术在全基因组范围内产生了大量数据，这为药物研究提供了便利。药物靶点发现是药物发现过程中的关键步骤，在治疗学中也起着至关重要的作用。在这项研究中，我们解决了药物靶点检测的问题，找到了一组酶，如果抑制这些酶的活性，就会阻止给定的一组靶化合物的产生，同时最大限度地减少代谢网络中靶化合物的非靶化合物。该模型旨在使药物的副作用尽可能小，因此具有潜在药物应用的实际意义。具体来说，通过利用代谢系统的特殊性质，我们提出了一种新的方法，可以将这个药物靶点检测问题精确地表述为一个整数线性规划模型，从而确保可以有效地找到最优解，而无需任何启发式操作。为了验证我们方法的有效性，我们在大肠杆菌和人类代谢途径上进行了计算实验。结果表明，我们的方法可以以精确和高效的方式识别最佳药物靶点。特别是，它可以应用于包括大多数生物体的整个代谢网络在内的大规模网络。
