Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modeling. In this undertaking, the network provides a suitable framework for modeling the interactions between molecules. A biological network comprises nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modeled by the definition of a dynamical system. Among the different network categories, the Boolean network offers a reliable qualitative framework for modeling the biological systems. Automatically synthesizing a Boolean network from experimental data, therefore, remains a necessary but challenging issue. This study, presents taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of Boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model regarding biological knowledge and experiments must be found. In the taboon work-flow, the selection of the fittest model is achieved by a Tabu-search algorithm. taboon is an automated method for Boolean Network inference from experimental data that helps biologists synthesize a reliable model faster and assist in evaluating and optimizing the biological networks’ dynamic behavior, further modeling and predictions.

中文翻译：

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基于禁忌搜索的TaBooN布尔网络综合


组学技术的最新发展通过产生多个维度和规模的分子数据彻底改变了生物学的研究。生物学上的这一突破提出了基于模型的解释的关键问题。在这项工作中，网络提供了一个合适的框架来模拟分子之间的相互作用。生物网络包含指代基因或蛋白质等成分的节点，以及形式化它们之间相互作用的边/弧。然后通过动力系统的定义对相互作用的演化进行建模。在不同的网络类别中，布尔网络为生物系统建模提供了可靠的定性框架。因此，从实验数据自动合成布尔网络仍然是一个必要但具有挑战性的问题。这项研究提出了t a boon ，这是一种从生物数据合成布尔网络的原始工作流程。该方法使用布尔配置文件形式的数据来推断所有潜在的局部公式推断。它们结合起来形成模型空间，必须从中找到有关生物知识和实验的最真实的模型。在禁忌工作流程中，最适合模型的选择是通过禁忌搜索算法实现的。 t a boo n是一种根据实验数据进行布尔网络推理的自动化方法，可帮助生物学家更快地合成可靠的模型，并协助评估和优化生物网络的动态行为，进一步建模和预测。