Li Luna Xingyu, Aguilar Boris, Gennari John, Qin Guangrong
Institute for Systems Biology, Seattle, WA, 98109, USA.
Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA.
BMC Bioinformatics. 2025 Jul 15;26(1):178. doi: 10.1186/s12859-025-06212-2.
Gene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision medicine. While researchers have built many models to describe specific subsets of gene interactions, more comprehensive models that cover a broader range of genes are challenging to build. This necessitates the development of approaches for improving the models through model merging.
We present LM-Merger, a workflow for semi-automatically merging logical GRN models. The workflow consists of five main steps: (a) model identification, (b) model standardization and annotation, (c) model verification, (d) model merging, and (e) model evaluation. We demonstrate the feasibility and benefit of this workflow with two pairs of published models pertaining to acute myeloid leukemia (AML). The integrated models were able to retain the predictive accuracy of the original models, while expanding coverage of the biological system. Notably, when applied to a new dataset, the integrated models outperformed the individual models in predicting patient response.
This study highlights the potential of logical model merging to advance systems biology research and our understanding of complex diseases. By enabling the construction of more comprehensive models, LM-Merger facilitates deeper insights into disease mechanisms and enhances predictive modeling for precision medicine applications.
Not applicable.
基因调控网络（GRN）模型为调控基因表达从而影响细胞行为的遗传相互作用提供了机制性理解。基因表达失调在疾病进展和治疗反应中起着关键作用，这使得GRN模型成为精准医学的一个有前景的工具。虽然研究人员已经构建了许多模型来描述基因相互作用的特定子集，但构建涵盖更广泛基因的更全面模型具有挑战性。这就需要开发通过模型合并来改进模型的方法。
我们提出了LM-Merger，一种用于半自动合并逻辑GRN模型的工作流程。该工作流程包括五个主要步骤：（a）模型识别，（b）模型标准化和注释，（c）模型验证，（d）模型合并，以及（e）模型评估。我们用两对已发表的与急性髓系白血病（AML）相关的模型证明了该工作流程的可行性和益处。整合后的模型能够保留原始模型的预测准确性，同时扩大生物系统的覆盖范围。值得注意的是，当应用于新数据集时，整合后的模型在预测患者反应方面优于单个模型。
本研究强调了逻辑模型合并在推进系统生物学研究以及我们对复杂疾病理解方面的潜力。通过构建更全面的模型，LM-Merger有助于更深入地洞察疾病机制，并增强精准医学应用的预测建模。
不适用。
