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Mathematical models of tumor cell proliferation: A review of the literature
- Source :
- Expert Review of Anticancer Therapy. 18:1271-1286
- Publication Year :
- 2018
- Publisher :
- Informa UK Limited, 2018.
-
Abstract
- INTRODUCTION: A defining hallmark of cancer is aberrant cell proliferation. Efforts to understand the generative properties of cancer cells span all biological scales: from genetic deviations and alterations of metabolic pathways, to physical stresses due to overcrowding, as well as the effects of therapeutics and the immune system. While these factors have long been studied in the laboratory, mathematical and computational techniques are being increasingly applied to help understand and forecast tumor growth and treatment response. Advantages of mathematical modeling of proliferation include the ability to simulate and predict the spatiotemporal development of tumors across multiple experimental scales. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. AREAS COVERED: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and clinical evidence, with a particular emphasis on emerging, non-invasive imaging technologies. EXPERT COMMENTARY: The data required to legitimize mathematical models are often difficult or (currently) impossible to obtain. We suggest areas for further investigation to establish mathematical models that more effectively utilize available data to make informed predictions on tumor cell proliferation.
- Subjects :
- Diagnostic Imaging
0301 basic medicine
Cell growth
business.industry
Aberrant cell
Cancer
Tumor cells
Models, Theoretical
medicine.disease
Models, Biological
Article
03 medical and health sciences
030104 developmental biology
Oncology
Neoplasms
Cancer cell
Cancer research
medicine
Humans
Pharmacology (medical)
business
Cell Proliferation
Subjects
Details
- ISSN :
- 17448328 and 14737140
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- Expert Review of Anticancer Therapy
- Accession number :
- edsair.doi.dedup.....96026d88e1dc15bc8921a0bfa7057f6d