BACKGROUND Boolean models of biological signalling-regulatory networks are increasingly used to formally describe and understand complex biological processes. These models may become inconsistent as new data become available and need to be repaired. In the past, the focus has been shed on the inference of (classes of) models given an interaction network and time-series data sets. However, repair of existing models against new data is still in its infancy, where the process is still manually performed and therefore slow and prone to errors. RESULTS In this work, we propose a method with an associated tool to suggest repairs over inconsistent Boolean models, based on a set of atomic repair operations. Answer Set Programming is used to encode the minimal repair problem as a combinatorial optimization problem. In particular, given an inconsistent model, the tool provides the minimal repairs that render the model capable of generating dynamics coherent with a (set of) time-series data set(s), considering either a synchronous or an asynchronous updating scheme. CONCLUSIONS The method was validated using known biological models from different species, as well as synthetic models obtained from randomly generated networks. We discuss the method's limitations regarding each of the updating schemes and the considered minimization algorithm.

中文翻译：

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使用答案集编程从时间序列数据中修复布尔逻辑模型。

背景技术生物信号调节网络的布尔模型越来越多地用于正式描述和理解复杂的生物过程。随着新数据可用并需要修复，这些模型可能会变得不一致。过去，重点已经转移到给定交互网络和时间序列数据集的（类别）模型的推断上。然而，针对新数据修复现有模型仍处于起步阶段，该过程仍然是手动执行的，因此速度慢且容易出错。结果在这项工作中，我们提出了一种带有相关工具的方法，该方法基于一组原子修复操作来建议对不一致的布尔模型进行修复。答案集编程用于将最小修复问题编码为组合优化问题。尤其，给定一个不一致的模型，该工具提供最小的修复，使模型能够生成与（一组）时间序列数据集一致的动态，考虑同步或异步更新方案。结论 该方法使用来自不同物种的已知生物模型以及从随机生成的网络获得的合成模型进行了验证。我们讨论了该方法在每个更新方案和考虑的最小化算法方面的局限性。以及从随机生成的网络中获得的合成模型。我们讨论了该方法在每个更新方案和考虑的最小化算法方面的局限性。以及从随机生成的网络中获得的合成模型。我们讨论了该方法在每个更新方案和考虑的最小化算法方面的局限性。