Mathematical model for pancreatic cancer progression using non-constant gene mutation rate

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2 Citations (Scopus)

Abstract

Cancer of the pancreas is a highly lethal disease and has an extremely poor prognosis. Mathematical modelling and computer simulations have been proposed as important tool to predictor initiation and progression of cancer diseases, which are very important in cancer study. Among these studies, it is widely assumed that the gene mutation rate is unchanged, which is not realistic based on recently biological and medical studies. In this work, we present a new approach using non-constant mutation rate and hence reveal several important biological parameters of cancer progression. Under more realistic assumptions regarding gene mutation and a more reasonable mutation rate, our proposed model and calculated results may provide insights into dynamics of cancer metastasis and have clinic implications.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publication2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
EditorsXiaohua Hu, Yang Gong, Chi-Ren Shyu, Dmitry Korkin, Yana Bromberg, Illhoi Yoo, Jean Gao, Jane Huiru Zheng
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages138-141
Number of pages4
Volume2017-January
ISBN (Electronic)9781509030507, 9781509030514
DOIs
Publication statusPublished - 15 Dec 2017
EventIEEE International Conference on Bioinformatics and Biomedicine 2017 - Kansas City, United States of America
Duration: 13 Nov 201716 Nov 2017

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine 2017
Abbreviated titleBIBM 2017
CountryUnited States of America
CityKansas City
Period13/11/1716/11/17

Keywords

  • Mathematical model
  • pancreatic cancer
  • simulation
  • somatic mutation rate

Cite this

Sun, S., Klebaner, F., & Tian, T. (2017). Mathematical model for pancreatic cancer progression using non-constant gene mutation rate. In X. Hu, Y. Gong, C-R. Shyu, D. Korkin, Y. Bromberg, I. Yoo, J. Gao, & J. H. Zheng (Eds.), Proceedings: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (Vol. 2017-January, pp. 138-141). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BIBM.2017.8217639