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

Alameddine, Abdallah K.; Conlin, Frederick; Binnall, Brian

doi:10.1177/1176935118799754

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…Bioinformatics have aroused as a useful tool to analyze biochemically the cancer microenvironment and have contributed to increase our knowledge as well, besides offering new therapeutic strategies in order to control and avoid cancer progression [19]. Bioinformatics approaches, such as, systems biology and the investigation of hot spots on the interface of protein-protein interactions (PPIs), have helped the identification and design of small bioactive molecules to modulate PPIs and inactivate tumorous cells [20][21][22]. Hot spot residues are generally conserved among proteins with similar function or protein families carrying certain domains.…”

Section: Introductionmentioning

confidence: 99%

Hot spots and single nucleotide polymorphisms on the interaction interface of RAD51 and p53 complex

Silva¹

2018

J Tre Bio Res

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Cancer statistics have increased worldwide over the years. The molecular basis for the onset of a cancer cell lies on genetic predisposition, endogenous compounds produced by cell metabolism, certain types of infection and environmental factors. Accumulated genetic alterations, such as mutations and polymorphisms, lead to a genome instability and consequently makes a regular cell into a cancer cell. Bioinformatics approaches, such as, systems biology and the investigation of hot spots on the interface of protein-protein interactions (PPIs), have helped the identification and design of small bioactive molecules to modulate PPIs and inactivate tumorous cells. Identifying polymorphic hot spot residues within protein domains or within interface of PPIs makes possible to reveal biochemical features and organization of pathways related to the onset of cancer that would confer survivability to cancer cells due to entropic advantage. RAD51 and p53 are strong candidate genes that influence susceptibility to cancer. Both proteins contain functional domains and interact with several other proteins, including tumor suppressors, oncogenes and DNA repairing proteins. Cancer shows a very complex genetic, molecular and biochemical signature. Biomedical research is driven towards a wider comprehension of the functional importance of polymorphisms and their association with human diseases. Here, we analyzed the interface of interaction between RAD51 and p53 and identified hot spots that could be of importance for the conformational structure of those proteins, their function and pattern of interaction with their partners. We have also shown that some of the hot spot amino acid residues are polymorphic, which could disrupt the interaction between p53 and RAD5, leading to a higher susceptibility to cancer. Future studies should be conducted in order to design small molecules that could modulate the interaction between RAD51 and p53 and guarantee proper homologous recombination and maintenance of genomic stability.

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Section: Introductionmentioning

confidence: 99%

Hot spots and single nucleotide polymorphisms on the interaction interface of RAD51 and p53 complex

Silva¹

2018

J Tre Bio Res

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“…Oyelami [11], in 2018, proposed a mathematical model for cancer cells and the immunization response of the body. In 2018, Alameddine [12] presented a simplified and latest study tool to model cancer systems. In 2016, Baar et al [13] presented modeling of the malignant tumor by randomizing effect.…”

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confidence: 99%

Computational Algorithms for the Analysis of Cancer Virotherapy Model

Raza¹,

Băleanu²,

Rafiq³

et al. 2022

Computers, Materials &Amp; Continua

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Cancer is a common term for many diseases that can affect any part of the body. In 2020, ten million people will die due to cancer. A worldwide leading cause of death is cancer by the World Health Organization (WHO) report. Interaction of cancer cells, viral therapy, and immune response are identified in this model. Mathematical and computational modeling is an effective tool to predict the dynamics of cancer virotherapy. The cell population is categorized into three parts like uninfected cells (x), infected cells (y), virus-free cells (v), and immune cells (z). The modeling of cancerlike diseases is based on the law of mass action (the rate of change of reacting substances is directly proportional to the product of interacting substances). Positivity, boundedness, equilibria, threshold analysis, are part of deterministic modeling. Later on, a numerical analysis is designed by using the standard and non-standard finite difference methods. The non-standard finite difference method is developed to study the long-term behavior of the cancer model. For its efficiency, a comparison of the methods is investigated.

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“…Mathematical modeling has been successfully applied in the context of computational systems biology to develop comprehensive mathematical descriptions of several pathologies [1][2][3][4][5][6][7][8][9] . Mathematical models are calibrated through experimental data to simulate in silico biological systems and test hypotheses, for example regarding the regulative mechanisms of complex diseases [10][11][12][13][14][15][16] .…”

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confidence: 99%

A QSP model of prostate cancer immunotherapy to identify effective combination therapies

Coletti

Leonardelli

Parolo

et al. 2020

Sci Rep

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dependent on androgen signaling, androgen deprivation therapy, either by chemical or surgical castration, is the first-line treatment for advanced PCa 40. Although this therapy is initially highly effective in most of the patients, in some cases the tumor evolves into an androgen independent form that currently lacks efficacious therapeutic options 41. The transition from the androgen dependent to the androgen independent state may occur through several mechanisms that are not yet completely understood 42. In this context, immunotherapy represents a highly promising new treatment approach. Over the last few years, numerous pre-clinical and clinical studies have been performed to develop and test different PCa immunotherapies 43. A main achievement has been the US Food and Drug Administration (FDA) approval of sipuleucel-T, so far the only approved immunotherapy for PCa treatment 40. Compared with other types of cancer, PCa is relatively insensitive to the most popular immunotherapies and additional studies are needed to understand the mechanisms underlying this lack of immune responsiveness 44. Thus, evaluating combination therapies is another important step to improve therapeutic benefits 45. We herein propose a QSP model of prostate cancer that extends a previous one published by Peng et al. 46 based on data from a murine Pten prostate cancer model 47,48. Although Peng and coworkers already described the action of CTLs, dendritic cells, Tregs, androgens and interleukin-2 (IL-2) in the tumor microenvironment, we extended the characterization of the immune system by including MDSCs and Natural Killer (NK) cells as potential targets for new prostate cancer therapies. The main goal of this contribution is, indeed, to provide a mathematical model able to test the efficacy of several immunotherapies and their combinations. We incorporate a wide range of experimental data from literature 31,46,49-51 to implement seven treatments. The efficacy of therapies is assessed considering the model-predicted tumor inhibitory effect and the synergy of combination therapies. Synergy between treatments plays a crucial role to reduce the dosage of each drug maintaining a satisfactory overall treatment efficacy, improving patients' quality of life 52. The paper is organized as follows. In the result section we provide a description of the model diagram and a detailed explanation of the Ordinary Differential Equations (ODEs) constituting the mathematical model. We provide the model simulations to verify the reproducibility of the experimental data and to compare the predicted outcomes with known biological mechanisms. We identify the most effective treatment combinations for prostate cancer through the model-predicted tumor size and the treatment synergy. Moreover, we emphasize the androgen deprivation therapy as leading treatment in a decision tree to choose the best protocol to treat androgen independent prostate cancer. Finally, a discussion of the results and a description of the study limitations is provided. Results The mathema...

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

Cited by 8 publications

References 59 publications

Hot spots and single nucleotide polymorphisms on the interaction interface of RAD51 and p53 complex

Hot spots and single nucleotide polymorphisms on the interaction interface of RAD51 and p53 complex

Computational Algorithms for the Analysis of Cancer Virotherapy Model

A QSP model of prostate cancer immunotherapy to identify effective combination therapies

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