biostudies-literatureUnknownKatebi ANCI NIH HHSNIGMS NIH HHSAcute 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 adoptsbioRxiv : the preprint server for biology2023.07.29.551111https://www.ebi.ac.uk/biostudies/studies/S-EPMC10418072biostudies-literatureData-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML.PMC10418072P30 CA034196R35 GM128717Lu MLi SKatebi AChen XfalseData-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML.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 adopts2023-01-01T00:00:00Z2023 Jul2026-04-08T14:44:23.488Z2025-04-06T16:40:12.953ZS-EPMC104180723757752610.1101/2023.07.29.551111