Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization

Shi Tiange (), Yu Han () and Blair Rachael Hageman ()
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Shi Tiange: University at Buffalo, Biostatistics, Buffalo, USA
Yu Han: Roswell Park Comprehensive Cancer Center, Biostatistics and Bioinformatics, Buffalo, USA
Blair Rachael Hageman: University at Buffalo, Biostatistics, Institute for Artificial Intelligence and Data Science, Buffalo, USA

Statistical Applications in Genetics and Molecular Biology, 2023, vol. 22, issue 1, 20

Abstract: Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types’ metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.

Keywords: Bayesian Network; constraint-based models; fluxome; single-cell RNA; cancer (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/sagmb-2022-0054

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