Sayed Khaled, Bocan Kara N, Miskov-Zivanov Natasa
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5030-5033. doi: 10.1109/EMBC.2018.8513431.
The number of published results in biology and medicine is growing at an exceeding rate, and thus, extracting relevant information for building useful models is becoming very laborious. Furthermore, with the newly published information, previously built models need to be extended and updated, and with the voluminous literature, it is necessary to automate the model extension process. In this work, we introduce a methodology for extending logical models of cell signaling networks using a Genetic Algorithm (GA). The proposed procedure is developed to optimally search for a subset of biological interactions that extend logical models while preserving their desired behavior. To evaluate the effectiveness of the proposed methodology, we randomly removed a subset of elements from an existing T cell differentiation model, and mixed them with randomly created interactions to mimic the output of literature reading. We then used the GA to search for the extensions that optimally reconstructed the model. The simulation results showed that the GA was able to find a set of extensions that preserved the desired behavior of the model with fewer elements than the original model. The results demonstrate that the GA is an efficient tool for model extension, and suggest that it can be used for model reduction as well.
生物学和医学领域已发表的研究成果数量正以惊人的速度增长，因此，提取相关信息以构建有用的模型变得极为费力。此外，随着新信息的发表，先前构建的模型需要扩展和更新，面对海量文献，有必要实现模型扩展过程的自动化。在这项工作中，我们介绍了一种使用遗传算法（GA）扩展细胞信号网络逻辑模型的方法。所提出的程序旨在最佳地搜索生物相互作用的子集，以扩展逻辑模型同时保留其期望的行为。为了评估所提方法的有效性，我们从现有的T细胞分化模型中随机移除一部分元素，并将它们与随机创建的相互作用混合，以模拟文献阅读的输出。然后我们使用遗传算法搜索能够最佳重建模型的扩展。模拟结果表明，遗传算法能够找到一组扩展，这些扩展用比原始模型更少的元素保留了模型期望的行为。结果表明遗传算法是模型扩展的有效工具，并且表明它也可用于模型简化。
