Trinh Van-Giang, Park Kyu Hyong, Pastva Samuel, Rozum Jordan C
LIRICA Team, Aix-Marseille University, Marseille 13397, France.
Department of Physics, Pennsylvania State University, University Park, PA 16802, United States.
Bioinformatics. 2025 May 6;41(5). doi: 10.1093/bioinformatics/btaf280.
Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram (SD) describes this interplay in a discrete analog of Waddington's epigenetic attractor landscape that allows for fast identification of attractors and attractor control strategies. Efficient computational tools for studying SDs are essential for the understanding of Boolean attractor landscapes and connecting them to their biological functions.
We present a new approach to SD construction for asynchronously updated Boolean networks, implemented in the biologist's Boolean attractor landscape mapper, biobalm. We compare biobalm to similar tools and find a substantial performance increase in SD construction, attractor identification, and attractor control. We perform the most comprehensive comparative analysis to date of the SD structure in experimentally-validated Boolean models of cell processes and random ensembles. We find that random models (including critical Kauffman networks) have relatively small SDs, indicating simple decision structures. In contrast, nonrandom models from the literature are enriched in extremely large SDs, indicating an abundance of decision points and suggesting the presence of complex Waddington landscapes in nature.
The tool biobalm is available online at https://github.com/jcrozum/biobalm. Further data, scripts for testing, analysis, and figure generation are available online at https://github.com/jcrozum/biobalm-analysis and in the reproducibility artefact at https://doi.org/10.5281/zenodo.13854760.
布尔网络是系统生物学中细胞过程的流行动力学模型。它们的吸引子对关键调控子电路相互作用产生的表型进行建模。连续图（SD）在沃丁顿表观遗传吸引子景观的离散模拟中描述了这种相互作用，从而能够快速识别吸引子和吸引子控制策略。用于研究连续图的高效计算工具对于理解布尔吸引子景观并将其与生物学功能联系起来至关重要。
我们提出了一种用于异步更新布尔网络的连续图构建新方法，该方法在生物学家的布尔吸引子景观映射器biobalm中实现。我们将biobalm与类似工具进行比较，发现在连续图构建、吸引子识别和吸引子控制方面性能有显著提升。我们对细胞过程的实验验证布尔模型和随机集合中的连续图结构进行了迄今为止最全面的比较分析。我们发现随机模型（包括临界考夫曼网络）的连续图相对较小，表明决策结构简单。相比之下，文献中的非随机模型有大量极大的连续图，表明存在大量决策点，并暗示自然界中存在复杂的沃丁顿景观。
