Current status of mathematical modeling of cancer – From the viewpoint of cancer hallmarks

Magi, Shigeyuki; Iwamoto, Kenji; Okada‐Hatakeyama, Mariko

doi:10.1016/j.coisb.2017.02.008

Cited by 37 publications

(28 citation statements)

References 91 publications

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“…Mathematical models represent an adequate way to formalize the knowledge of living systems obtained through a Systems Biology approach. 16,17 The models make the description of essential regularities possible and are useful to provide adequate guidelines for the development of therapies, and so on. For the analysis of this process, the model previously developed by us was used 15 , the network structure shown in Figure 1.…”

Section: The Methodology Of Workmentioning

confidence: 99%

Do mole-rats follow the same rules in their longevity and aging in nature?

Montero¹,

Silva²,

Mansilla³

et al. 2019

MOJGG

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A non-autonomous ordinary differential equations model was developed from the theoretical model of a nonlinear network previously developed by us. The proposed network-modified model qualitatively shows that mole-rates, when subjected to hostile conditions such as variations of the oxygen of the environment, maintain high robustness to environmental changes, which ensures its adaptability to the surroundings.

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Section: The Methodology Of Workmentioning

confidence: 99%

Do mole-rats follow the same rules in their longevity and aging in nature?

Montero¹,

Silva²,

Mansilla³

et al. 2019

MOJGG

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“…Flux balance analysis is a mathematical model that uses the flow of metabolites through a stoichiometric metabolic network. 54 …”

Section: Applications Of Basic Tools In Computational Modeling Of Tummentioning

confidence: 99%

“…In response to DNA damage, p53 can show a transient response leading to cell cycle arrest and DNA repair. 54 Pulsatile responses induce cell apoptosis, and sustained responses mainly trigger senescence. The additional loops have been shown, through computational studies and tested using Monte Carlo approach, to impart some performance advantages over simple negative feedback loops.…”

Section: Applications Of Basic Tools In Computational Modeling Of Tummentioning

confidence: 99%

An Introduction to the Mathematical Modeling in the Study of Cancer Systems Biology

2018

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Background:Frequently occurring in cancer are the aberrant alterations of regulatory onco-metabolites, various oncogenes/epigenetic stochasticity, and suppressor genes, as well as the deficient mismatch repair mechanism, chronic inflammation, or those deviations belonging to the other cancer characteristics. How these aberrations that evolve overtime determine the global phenotype of malignant tumors remains to be completely understood. Dynamic analysis may have potential to reveal the mechanism of carcinogenesis and can offer new therapeutic intervention.Aims:We introduce simplified mathematical tools to model serial quantitative data of cancer biomarkers. We also highlight an introductory overview of mathematical tools and models as they apply from the viewpoint of known cancer features.Methods:Mathematical modeling of potentially actionable genomic products and how they proceed overtime during tumorigenesis are explored. This report is intended to be instinctive without being overly technical.Results:To date, many mathematical models of the common features of cancer have been developed. However, the dynamic of integrated heterogeneous processes and their cross talks related to carcinogenesis remains to be resolved.Conclusions:In cancer research, outlining mathematical modeling of experimentally obtained data snapshots of molecular species may provide insights into a better understanding of the multiple biochemical circuits. Recent discoveries have provided support for the existence of complex cancer progression in dynamics that span from a simple 1-dimensional deterministic system to a stochastic (ie, probabilistic) or to an oscillatory and multistable networks. Further research in mathematical modeling of cancer progression, based on the evolving molecular kinetics (time series), could inform a specific and a predictive behavior about the global systems biology of vulnerable tumor cells in their earlier stages of oncogenesis. On this footing, new preventive measures and anticancer therapy could then be constructed.

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“…However, the abovementioned effects of TGF-β/SMAD-induced EMT are typically studied in isolation with focus on a few nodes of the pathway, hence neglecting the effects of crosstalk among multiple signaling pathways. Such crosstalk can often generate feedback loops with nonlinear dynamics, giving rise to emergent, complex, and non-intuitive behavior [24]. Hence, a systems biology approach integrating computational and experimental components can be essential to elucidating the dynamics of underlying interconnected cellular circuitry and identifying the fundamental organizational principles driving tumor progression [25].…”

Section: Introductionmentioning

confidence: 99%

An integrative systems biology and experimental approach identifies convergence of epithelial plasticity, metabolism, and autophagy to promote chemoresistance

Ware

Ding

et al. 2018

Preprint

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26The evolution of therapeutic resistance is a major cause of death for patients with solid 27 tumors. The development of therapy resistance is shaped by the ecological dynamics 28 within the tumor microenvironment and the selective pressure induced by the host 29 immune system. These ecological and selective forces often lead to evolutionary 30 convergence on one or more pathways or hallmarks that drive progression. These 31 hallmarks are, in turn, intimately linked to each other through gene expression 32 networks. Thus, a deeper understanding of the evolutionary convergences that occur at 33 the gene expression level could reveal vulnerabilities that could be targeted to treat 34 therapy-resistant cancer. To this end, we used a combination of phylogenetic clustering, 35 systems biology analyses, and wet-bench molecular experimentation to identify 36 convergences in gene expression data onto common signaling pathways. We applied 37 these methods to derive new insights about the networks at play during TGF-β-38 mediated epithelial-mesenchymal transition in a lung cancer model system. 39Phylogenetics analyses of gene expression data from TGF-β treated cells revealed 40 evolutionary convergence of cells toward amine-metabolic pathways and autophagy 41 during TGF-β treatment. Using high-throughput drug screens, we found that 42 knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to 43 cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-44 mediated chemoresistance mechanism. Analysis of publicly-available clinical data sets 45 validated the adverse prognostic importance of ATG16L expression in multiple cancer 46 types including kidney, lung, and colon cancer patients. These analyses reveal the 47 usefulness of combining evolutionary and systems biology methods with experimental 48 validation to illuminate new therapeutic vulnerabilities. 49 50

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Current status of mathematical modeling of cancer – From the viewpoint of cancer hallmarks

Cited by 37 publications

References 91 publications

Do mole-rats follow the same rules in their longevity and aging in nature?

Do mole-rats follow the same rules in their longevity and aging in nature?

An Introduction to the Mathematical Modeling in the Study of Cancer Systems Biology

An integrative systems biology and experimental approach identifies convergence of epithelial plasticity, metabolism, and autophagy to promote chemoresistance

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