标题： 异步布尔网络模型的最优推断 显示英文标题

标题： Optimal Inference of Asynchronous Boolean Network Models

摘要： 表型与基因组和表观基因组标记之间的关联通常是通过相关性得出的。系统生物学旨在通过建模产生表型的细胞机制，从而建立更稳健的联系并揭示更广泛的见解。选择建模方法的问题具有核心重要性。一个不能足够贴近生物现实的模型将无法解释系统的行为。同时，高度详细的模型受到计算限制，并可能过度拟合数据。布尔网络在复杂性和描述性之间取得了平衡，因此受到了越来越多的关注。我们之前描述了一种算法，用于将布尔网络拟合到高通量实验数据，该算法可以找到相对于给定数据集信息的最佳网络。在本研究中，我们描述了一个简单的扩展，使能够对异步动态进行建模，即不同网络节点的不同反应时间。我们的方法大大简化了针对时间序列数据集的布尔网络模型构建，其中异步性经常发生。我们通过整合来自转录组实验的真实数据来演示我们的方法，并提供了一个可被社区用于使用任何高通量数据集进行网络重建的实现。我们的方法显著扩大了布尔网络模型在实验数据中的适用性。 显示英文摘要

摘要： Associations between phenotype and genomic and epigenomic markers are often derived by correlation. Systems Biology aims to make more robust connections and uncover broader insights by modeling the cellular mechanisms that produce a phenotype. The question of choosing the modeling methodology is of central importance. A model that does not capture biological reality closely enough will not explain the system's behavior. At the same time, highly detailed models suffer from computational limitations and are likely to overfit the data. Boolean networks strike a balance between complexity and descriptiveness and thus have received increasing interest. We previously described an algorithm for fitting Boolean networks to high-throughout experimental data that finds the optimal network with respect to the information in a given dataset. In this work, we describe a simple extension that enables the modeling of asynchronous dynamics, i.e. different reaction times for different network nodes. Our approach greatly simplifies the construction of Boolean network models for time-series datasets, where asynchronicty often occurs. We demonstrate our methodology by integrating real data from transcriptomic experiments, and provide an implementation that can be used by the community for network reconstruction using any high-throughout dataset. Our approach significantly expands the applicability of the Boolean network model to experimental data.