Merging logical models: An application in Acute Myeloid Leukemia modeling
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Gene regulatory network (GRNs) models provide mechanistic understanding of the gene regulations and interactions that control various aspects of cellular behaviors. While researchers have constructed GRNs to model specific sets of gene regulations or interactions, little work has been made to integrate or merge these models into larger, more comprehensive ones that could encompass more genes, and improve the accuracy of predicting biological processes. Here, we present a workflow for merging logical GRN models, which requires sequential steps including model standardization, reproducing, merging and evaluations, and demonstrate its application in acute myeloid leukemia (AML) study. We demonstrate the feasibility and benefits of model merging by integrating two pairs of published models. Our integrated models were able to retain similar accuracy of the original publications, while increasing the coverage and explainability of the biological system. This approach highlights the integration of logical models in advancing system biology and enhancing the understanding of complex diseases.
Author summary
In our study, we tackle the challenges of integrating gene regulatory network (GRN) models to enhance our understanding of complex biological systems. GRNs are essential tools for understanding how genes regulate various cellular behaviors, but individual models often focus on specific sets of genes or interactions. We present a novel workflow that merges these individual logical GRN models into more comprehensive ones, providing a broader view of gene regulation. We applied this workflow to Acute Myeloid Leukemia (AML), a highly aggressive form of blood cancer. AML is challenging to treat due to its genetic complexity and the frequent occurrence of treatment-resistant mutations. Our integrated models retain the accuracy of the original models while offering improved coverage of the biological processes. This approach offers valuable insights into the disease’s underlying mechanisms through a combination of models that describe different aspects of AML. We envision that the proposed workflow will improve predictions, generate deeper insights, and improve our understanding and treatment of complex diseases like AML.
