急性髓系白血病（AML）被充斥着未分化的髓细胞，其突变谱是异质性的。IDH1和IDH2突变在AML病例中占20%。虽然已经做了很多工作来识别与白血病发生相关的基因，但AML状态转变的调控机制仍然未完全理解。为了解决这个问题，我们在这里开发了一种新的计算方法，该方法整合了来自不同数据源的基因组数据，包括基因表达和ATAC-seq数据集，策划的基因调控相互作用数据库，并采用数学建模来建立适应环境特定的核心基因调控网络（GRNs）模型，以深入理解IDH突变导致AML肿瘤形成的机制。该方法采用了一种新的优化过程，根据其在捕获基因表达状态准确性和允许足够的状态转换控制灵活性方面的表现选择最优网络。通过GRN建模，我们确定了与IDH突变功能有关的关键调控因子，如DNA甲基转移酶DNMT1，以及网络不稳定因子，如E2F1。构建的核心调控网络和体外网络扰动结果得到了AML患者的生存数据的支持。我们期望该结合了生物信息学和系统生物学建模方法的综合方法能够普遍适用于阐明疾病进展的基因调控。设计了一个结合了生物信息学和系统生物学建模方法来建模IDH突变AML的转录调控网络。网络建模确定了关键调控因子DNMT1和E2F1，并得到了患者生存数据的支持。

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.A combined bioinformatics and systems-biology modeling approach is designed to model a transcriptional regulatory network for AML with IDH mutations. Network modeling identifies key regulators DNMT1 and E2F1, which is supported by patient survival data.