Linked Life Data

16766361

Source:http://linkedlifedata.com/resource/pubmed/id/16766361

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1843
pubmed:dateCreated
2006-6-12
pubmed:abstractText
Cancer is a complex disease in which a variety of factors interact over a wide range of spatial and temporal scales with huge datasets relating to the different scales available. However, these data do not always reveal the mechanisms underpinning the observed phenomena. In this paper, we explain why mathematics is a powerful tool for interpreting such data by presenting case studies that illustrate the types of insight that realistic theoretical models of solid tumour growth may yield. These range from discriminating between competing hypotheses for the formation of collagenous capsules associated with benign tumours to predicting the most likely stimulus for protease production in early breast cancer. We will also illustrate the benefits that may result when experimentalists and theoreticians collaborate by considering a novel anti-cancer therapy.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1364-503X
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
364
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1563-78
pubmed:dateRevised
2009-4-16
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Modelling aspects of cancer dynamics: a review.
pubmed:affiliation
Centre for Mathematical Medicine, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK. helen.byrne@maths.nottingham.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't