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Probabilistic model checking of cancer metabolism
Meir D Friedenberg1, Adrian Lita2, Mark R Gilbert2
1Cornell University, Ithaca, NY, 14850, USA.
Scientific Reports
|November 7, 2022
Summary
Computational models reveal how cancer cell metabolism adapts to IDH1/2 mutations. Gene expression data helps predict metabolic activity, offering insights into cancer vulnerabilities.
Area of Science:
- Oncology
- Metabolic Engineering
- Computational Biology
Background:
- Cancer cell metabolism is frequently altered to support rapid growth and biosynthesis.
- Understanding metabolic deregulation is key to identifying cancer's targetable vulnerabilities.
- Probing cancer cell metabolism is challenging due to varying experimental conditions and resolution.
Purpose of the Study:
- To construct computational models of glucose and glutamine metabolism in cancer.
- To investigate the impact of isocitrate dehydrogenase (IDH) 1 and 2 mutations on cancer metabolism.
- To explore computational methods for revealing biological behavior and identifying metabolic vulnerabilities.
Main Methods:
- Utilized experimental metabolic flux data and patient-derived gene expression data.
- Developed computational models focusing on IDH1/2 mutations in cancer.
- Employed probabilistic model checking using the Probabilistic Symbolic Model Checker.
Main Results:
- Models showed an experimental IDH1-mutant model uses glutamine for lactate production under hypoxia.
- The experimental model did not fully replicate the patient phenotype under normoxia.
- Gene expression data proved useful as a proxy for relative metabolic activity differences.
Conclusions:
- Computational modeling can reveal complex biological behaviors in cancer metabolism.
- IDH mutations significantly alter cancer cell metabolic pathways, particularly glutamine utilization.
- Gene expression data integrated with metabolic models offers a powerful approach to study cancer metabolism and identify therapeutic targets.
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