Mathematical modeling of cancer metabolism

Medina, Miguel Ángel

doi:10.1016/j.critrevonc.2018.02.004

Cited by 34 publications

(15 citation statements)

References 58 publications

(40 reference statements)

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“…These studies serve as examples of how systems biology approaches can be formulated and applied to probe cancer metabolism. A detailed review of the mathematical models of metabolism can be found in 98 . Future progress on using computational models to elucidate the coupled decision-making of EMT-MR must combine these independent studies after a careful assessment of their cross-talk.…”

Section: To Solve the Emt-mr Puzzle: Systems Biologymentioning

confidence: 99%

Towards decoding the coupled decision-making of metabolism and epithelial-to-mesenchymal transition in cancer

et al. 2021

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Cancer cells have the plasticity to adjust their metabolic phenotypes for survival and metastasis.During metastasis, a developmental program known as the epithelial-mesenchymal transition (EMT) plays a critical role. There is extensive cross-talk between metabolism and EMT, but how this leads to coordinated physiological changes is still uncertain. The elusive connection between metabolism and EMT compromises the efficacy of metabolic therapies targeting metastasis. In this review, we aim for clarifying causation between metabolism and EMT based on recent 2 experimental studies and propose integrated theoretical-experimental efforts to better understand the coupled decision-making of metabolism and EMT.

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Section: To Solve the Emt-mr Puzzle: Systems Biologymentioning

confidence: 99%

Towards decoding the coupled decision-making of metabolism and epithelial-to-mesenchymal transition in cancer

et al. 2021

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“…Our work serves as a platform to target abnormal metabolism in cancer by modulating both genes and metabolic pathways. most widely used approaches to simulate cancer metabolism (23,24). Applications include quantifying the extent of aerobic glycolysis of the NCI-60 cell lines (25), characterizing their primary uptake pathways (26), and identifying the change of their metabolic states upon gene knockdown and/or drug treatment (27).…”

mentioning

confidence: 99%

Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways

Jia

Jung

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

268

235

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Metabolic plasticity enables cancer cells to switch their metabolism phenotypes between glycolysis and oxidative phosphorylation (OXPHOS) during tumorigenesis and metastasis. However, it is still largely unknown how cancer cells orchestrate gene regulation to balance their glycolysis and OXPHOS activities. Previously, by modeling the gene regulation of cancer metabolism we have reported that cancer cells can acquire a stable hybrid metabolic state in which both glycolysis and OXPHOS can be used. Here, to comprehensively characterize cancer metabolic activity, we establish a theoretical framework by coupling gene regulation with metabolic pathways. Our modeling results demonstrate a direct association between the activities of AMPK and HIF-1, master regulators of OXPHOS and glycolysis, respectively, with the activities of three major metabolic pathways: glucose oxidation, glycolysis, and fatty acid oxidation. Our model further characterizes the hybrid metabolic state and a metabolically inactive state where cells have low activity of both glycolysis and OXPHOS. We verify the model prediction using metabolomics and transcriptomics data from paired tumor and adjacent benign tissue samples from a cohort of breast cancer patients and RNA-sequencing data from The Cancer Genome Atlas. We further validate the model prediction by in vitro studies of aggressive triple-negative breast cancer (TNBC) cells. The experimental results confirm that TNBC cells can maintain a hybrid metabolic phenotype and targeting both glycolysis and OXPHOS is necessary to eliminate their metabolic plasticity. In summary, our work serves as a platform to symmetrically study how tuning gene activity modulates metabolic pathway activity, and vice versa.

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“…Recently, some authors investigated that the patients prognosis of acute myeloid leukemia is due to cytokineindependent leukemic cell proliferation see [7]. Miguel [8] & Rodriguesa et.al [9], also contributed their results for cancer metabolism and presented a detailed mathematical model for chemoimmunotherapy for lymphocytic leukemia describing its normal conditions and stability analysis of immunotherapy. Leukemia and tumor based on several other mathematical models were studied by a few more researchers such as [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Predator-Prey Model and Hawk-Dove Game for Modelling Leukemia

Sultana

Khan

Khalid

et al. 2022

Computational Intelligence and Neuroscience

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Game theory is an excellent mathematical tool to describe the interaction between the immune system and cancerous leukocytes c . l e u . The feature of cancerous leukocytes to differentiate and mutate to give rise to leukemia is in the domain of ecological models as well. In this work, the dynamic of leukemia is described and compared by two models: firstly by a simple probabilistic mathematical model using the zero-sum two player game of Hawk and Dove, and secondly by Leslie Predator Prey model of ecology. The main goal of this study is to compare the results of both models and then discuss the treatment of leukemia i.e., Hematopoietic Stem cell transplant with the best model among them. Hawk and Dove model also describes the cell to cell interaction of cancerous leukocytes and healthy leukocytes l e u after diagnoses and the condition of the patient before and after treatments. In this work, Hematopoietic Stem cell transplant is discussed by using concepts of a zero-sum three player game. Also, both models will be characterized by determining the stability properties, identifying basins of attraction, and locating the equilibrium points to see, at what extent the patient’s survival is possible with leukemia in its body. Results for both models will be presented graphically.

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Mathematical modeling of cancer metabolism

Cited by 34 publications

References 58 publications

Towards decoding the coupled decision-making of metabolism and epithelial-to-mesenchymal transition in cancer

Towards decoding the coupled decision-making of metabolism and epithelial-to-mesenchymal transition in cancer

Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways

Comparison of Predator-Prey Model and Hawk-Dove Game for Modelling Leukemia

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