Uncovering the Underlying Mechanisms of Cancer Metabolism through the Landscapes and Probability Flux Quantifications

Cancer metabolism is critical for understanding the mechanism of tumorigenesis, yet the understanding is still challenging. We studied gene-metabolism regulatory interactions and quantified the global driving forces for cancer-metabolism dynamics as the underlying landscape and probability flux. We...

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Bibliographic Details
Published iniScience Vol. 23; no. 4; p. 101002
Main Authors Li, Wenbo, Wang, Jin
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 24.04.2020
Elsevier
Subjects
Cancer
Gene Network
Mathematical Biosciences
Metabolic Flux Analysis
Metabolic Flux Analysis
Gene Network
Mathematical Biosciences
Cancer
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ISSN2589-0042
2589-0042
DOI10.1016/j.isci.2020.101002

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Summary:Cancer metabolism is critical for understanding the mechanism of tumorigenesis, yet the understanding is still challenging. We studied gene-metabolism regulatory interactions and quantified the global driving forces for cancer-metabolism dynamics as the underlying landscape and probability flux. We uncovered four steady-state attractors: a normal state attractor, a cancer OXPHOS state attractor, a cancer glycolysis state attractor, and an intermediate cancer state attractor. We identified the key regulatory interactions through global sensitivity analysis based on the landscape topography. Different landscape topographies of glycolysis switch between normal cells and cancer cells were identified. We uncovered that the normal state to cancer state transformation is associated with the peaks of the probability flux and the thermodynamic dissipation, giving dynamical and thermodynamic origin of cancer formation. We found that cancer metabolism oscillations consume more energy to support cancer malignancy. This study provides a quantitative understanding of cancer metabolism and suggests a metabolic therapeutic strategy. [Display omitted] •We uncovered cancer network involving gene regulations and metabolic pathways•We uncovered the emergence of normal state and three cancer metabolic states•Flux and entropy production rate provide predictors for emergence of cancer states•This study provides a metabolic therapeutic strategy based on landscape-flux theory Gene Network; Mathematical Biosciences; Cancer; Metabolic Flux Analysis
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ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2020.101002