Literature DB >> 28634857

Computational Modelling of Cancer Development and Growth: Modelling at Multiple Scales and Multiscale Modelling.

Zuzanna Szymańska1, Maciej Cytowski1, Elaine Mitchell2, Cicely K Macnamara3, Mark A J Chaplain4.   

Abstract

In this paper, we present two mathematical models related to different aspects and scales of cancer growth. The first model is a stochastic spatiotemporal model of both a synthetic gene regulatory network (the example of a three-gene repressilator is given) and an actual gene regulatory network, the NF-[Formula: see text]B pathway. The second model is a force-based individual-based model of the development of a solid avascular tumour with specific application to tumour cords, i.e. a mass of cancer cells growing around a central blood vessel. In each case, we compare our computational simulation results with experimental data. In the final discussion section, we outline how to take the work forward through the development of a multiscale model focussed at the cell level. This would incorporate key intracellular signalling pathways associated with cancer within each cell (e.g. p53-Mdm2, NF-[Formula: see text]B) and through the use of high-performance computing be capable of simulating up to [Formula: see text] cells, i.e. the tissue scale. In this way, mathematical models at multiple scales would be combined to formulate a multiscale computational model.

Entities:  

Keywords:  Computational simulations; Gene regulatory network; Individual-based model; Multiscale cancer modelling; Spatial stochastic model

Mesh:

Year:  2017        PMID: 28634857     DOI: 10.1007/s11538-017-0292-3

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  7 in total

Review 1.  Mathematical modeling of tumor-immune cell interactions.

Authors:  Grace E Mahlbacher; Kara C Reihmer; Hermann B Frieboes
Journal:  J Theor Biol       Date:  2019-03-02       Impact factor: 2.691

2.  RFC4 promotes the progression and growth of Oral Tongue squamous cell carcinoma in vivo and vitro.

Authors:  Jian Zhang; Linlin Wang; Xiao Xie
Journal:  J Clin Lab Anal       Date:  2021-03-30       Impact factor: 2.352

Review 3.  A Review of Cell-Based Computational Modeling in Cancer Biology.

Authors:  John Metzcar; Yafei Wang; Randy Heiland; Paul Macklin
Journal:  JCO Clin Cancer Inform       Date:  2019-02

4.  A 3D Multiscale Model to Explore the Role of EGFR Overexpression in Tumourigenesis.

Authors:  Anass Bouchnita; Stefan Hellander; Andreas Hellander
Journal:  Bull Math Biol       Date:  2019-04-23       Impact factor: 1.758

5.  Identification of significant genes signatures and prognostic biomarkers in cervical squamous carcinoma via bioinformatic data.

Authors:  Yunan He; Shunjie Hu; Jiaojiao Zhong; Anran Cheng; Nianchun Shan
Journal:  PeerJ       Date:  2020-12-02       Impact factor: 2.984

6.  Identification of Significant Gene Signatures and Prognostic Biomarkers for Patients With Cervical Cancer by Integrated Bioinformatic Methods.

Authors:  Xiaofang Li; Run Tian; Hugh Gao; Feng Yan; Le Ying; Yongkang Yang; Pei Yang; Yan'e Gao
Journal:  Technol Cancer Res Treat       Date:  2018-01-01

7.  Models of communication and control for brain networks: distinctions, convergence, and future outlook.

Authors:  Pragya Srivastava; Erfan Nozari; Jason Z Kim; Harang Ju; Dale Zhou; Cassiano Becker; Fabio Pasqualetti; George J Pappas; Danielle S Bassett
Journal:  Netw Neurosci       Date:  2020-11-01
  7 in total

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