Deciphering gene regulatory networks’ functioning is an essential step for better understanding of life, as these networks play a fundamental role in the control of cellular processes. Boolean networks have been widely used to represent gene regulatory networks. They allow to describe the dynamics of complex gene regulatory networks straightforwardly and efficiently. The attractors are essential in the analysis of the dynamics of a Boolean network. They explain that a particular cell can acquire specific phenotypes that may be transmitted over several generations. In this work, we consider a new representation of Boolean networks’ dynamics based on a new semantics used in Answer Set Programming (ASP). We use logic programs and ASP to express and deal with gene regulatory networks seen as Boolean networks, and develop a method to detect all the attractors of such networks. We first show how to represent and deal with general Boolean networks for the synchronous and asynchronous updates modes, where the computation of attractors requires a simulation of these networks’ dynamics. Then, we propose an approach for the particular case of circular networks where no simulation is needed. This last specific case plays an essential role in biological systems. We show several theoretical properties; in particular, simple attractors of the gene networks are represented by the stable models of the corresponding logic programs and cyclic attractors by its extra-stable models. These extra-stable models correspond to the extra-extensions of the new semantics that are not captured by the semantics of stable models. We then evaluate the proposed approach for general Boolean networks on real biological networks and the one dedicated to the case of circular networks on Boolean networks generated randomly. The obtained results for both approaches are encouraging.

破译基因调控网络的功能是更好地理解生命的重要一步，因为这些网络在细胞过程的控制中发挥着基础作用。布尔网络已被广泛用于表示基因调控网络。它们可以直接有效地描述复杂基因调控网络的动态。吸引子对于布尔网络的动态分析至关重要。他们解释说，特定的细胞可以获得特定的表型，这些表型可能会遗传几代。在这项工作中，我们考虑基于答案集编程（ASP）中使用的新语义的布尔网络动态的新表示。我们使用逻辑程序和ASP来表达和处理被视为布尔网络的基因调控网络，并开发一种方法来检测此类网络的所有吸引子。我们首先展示如何表示和处理同步和异步更新模式的通用布尔网络，其中吸引子的计算需要模拟这些网络的动态。然后，我们针对不需要模拟的循环网络的特殊情况提出了一种方法。最后一个具体案例在生物系统中起着至关重要的作用。我们展示了几个理论特性；特别是，基因网络的简单吸引子由相应逻辑程序的稳定模型表示，循环吸引子由其超稳定模型表示。这些超稳定模型对应于稳定模型语义未捕获的新语义的额外扩展。然后，我们评估了针对真实生物网络上的一般布尔网络所提出的方法，以及专门用于随机生成的布尔网络上的循环网络情况的方法。两种方法所获得的结果都令人鼓舞。