Institute of Medical Systems Biology, Ulm University, Ulm, Baden-Wuerttemberg 89081, Germany.
Bioinformatics. 2022 Oct 31;38(21):4893-4900. doi: 10.1093/bioinformatics/btac623.
Biological processes are complex systems with distinct behaviour. Despite the growing amount of available data, knowledge is sparse and often insufficient to investigate the complex regulatory behaviour of these systems. Moreover, different cellular phenotypes are possible under varying conditions. Mathematical models attempt to unravel these mechanisms by investigating the dynamics of regulatory networks. Therefore, a major challenge is to combine regulations and phenotypical information as well as the underlying mechanisms. To predict regulatory links in these models, we established an approach called CANTATA to support the integration of information into regulatory networks and retrieve potential underlying regulations. This is achieved by optimizing both static and dynamic properties of these networks.
Initial results show that the algorithm predicts missing interactions by recapitulating the known phenotypes while preserving the original topology and optimizing the robustness of the model. The resulting models allow for hypothesizing about the biological impact of certain regulatory dependencies.
Source code of the application, example files and results are available at https://github.com/sysbio-bioinf/Cantata.
Supplementary data are available at Bioinformatics online.
生物过程是具有独特行为的复杂系统。尽管可用数据量不断增加，但知识仍然稀缺，往往不足以研究这些系统的复杂调节行为。此外，在不同的条件下可能存在不同的细胞表型。数学模型试图通过研究调节网络的动态来揭示这些机制。因此，一个主要的挑战是将调节和表型信息以及潜在的机制结合起来。为了预测这些模型中的调节链接，我们建立了一种称为 CANTATA 的方法来支持将信息整合到调节网络中，并检索潜在的调节。这是通过优化这些网络的静态和动态特性来实现的。
初步结果表明，该算法通过再现已知表型来预测缺失的相互作用，同时保留原始拓扑结构并优化模型的稳健性。由此产生的模型可以假设某些调节依赖性的生物学影响。
应用程序的源代码、示例文件和结果可在 https://github.com/sysbio-bioinf/Cantata 上获得。
补充数据可在 Bioinformatics 在线获得。
