Research Institute for Signals, Systems and Computational Intelligence (sınc(i)), FICH-UNL/CONICET, Ciudad Universitaria UNL, (S3000), Santa Fe, Argentina.
Sci Rep. 2018 Nov 6;8(1):16398. doi: 10.1038/s41598-018-34454-z.
One of the current challenges in bioinformatics is to discover new ways to transform a set of compounds into specific products. The usual approach is finding the reactions to synthesize a particular product, from a given substrate, by means of classical searching algorithms. However, they have three main limitations: difficulty in handling large amounts of reactions and compounds; absence of a step that verifies the availability of substrates; and inability to find branched pathways. We present here a novel bio-inspired algorithm for synthesizing linear and branched metabolic pathways. It allows relating several compounds simultaneously, ensuring the availability of substrates for every reaction in the solution. Comparisons with classical searching algorithms and other recent metaheuristic approaches show clear advantages of this proposal, fully recovering well-known pathways. Furthermore, solutions found can be analyzed in a simple way through graphical representations on the web.
生物信息学目前面临的挑战之一是发现将一组化合物转化为特定产物的新方法。通常的方法是通过经典的搜索算法找到从给定的底物合成特定产物的反应。然而，它们有三个主要的局限性：难以处理大量的反应和化合物；缺乏验证底物可用性的步骤；以及无法找到分支途径。我们在这里提出了一种新的生物启发算法，用于合成线性和分支代谢途径。它允许同时关联几种化合物，确保解决方案中每个反应的底物可用性。与经典的搜索算法和其他最近的元启发式方法的比较表明了该方法的明显优势，完全恢复了众所周知的途径。此外，通过在网络上的图形表示，可以以简单的方式分析找到的解决方案。
