| pubmed-article:16766361 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:16766361 | lifeskim:mentions | umls-concept:C0006826 | lld:lifeskim |
| pubmed-article:16766361 | lifeskim:mentions | umls-concept:C1552617 | lld:lifeskim |
| pubmed-article:16766361 | lifeskim:mentions | umls-concept:C0282443 | lld:lifeskim |
| pubmed-article:16766361 | lifeskim:mentions | umls-concept:C1879746 | lld:lifeskim |
| pubmed-article:16766361 | lifeskim:mentions | umls-concept:C1547011 | lld:lifeskim |
| pubmed-article:16766361 | pubmed:issue | 1843 | lld:pubmed |
| pubmed-article:16766361 | pubmed:dateCreated | 2006-6-12 | lld:pubmed |
| pubmed-article:16766361 | 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. | lld:pubmed |
| pubmed-article:16766361 | pubmed:language | eng | lld:pubmed |
| pubmed-article:16766361 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16766361 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:16766361 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:16766361 | pubmed:month | Jun | lld:pubmed |
| pubmed-article:16766361 | pubmed:issn | 1364-503X | lld:pubmed |
| pubmed-article:16766361 | pubmed:author | pubmed-author:WebbS DSD | lld:pubmed |
| pubmed-article:16766361 | pubmed:author | pubmed-author:MainiP KPK | lld:pubmed |
| pubmed-article:16766361 | pubmed:author | pubmed-author:NIWAKK | lld:pubmed |
| pubmed-article:16766361 | pubmed:author | pubmed-author:AlarconTT | lld:pubmed |
| pubmed-article:16766361 | pubmed:author | pubmed-author:ByrneH MHM | lld:pubmed |
| pubmed-article:16766361 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:16766361 | pubmed:day | 15 | lld:pubmed |
| pubmed-article:16766361 | pubmed:volume | 364 | lld:pubmed |
| pubmed-article:16766361 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:16766361 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:16766361 | pubmed:pagination | 1563-78 | lld:pubmed |
| pubmed-article:16766361 | pubmed:dateRevised | 2009-4-16 | lld:pubmed |
| pubmed-article:16766361 | pubmed:meshHeading | pubmed-meshheading:16766361... | lld:pubmed |
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| pubmed-article:16766361 | pubmed:meshHeading | pubmed-meshheading:16766361... | lld:pubmed |
| pubmed-article:16766361 | pubmed:meshHeading | pubmed-meshheading:16766361... | lld:pubmed |
| pubmed-article:16766361 | pubmed:meshHeading | pubmed-meshheading:16766361... | lld:pubmed |
| pubmed-article:16766361 | pubmed:meshHeading | pubmed-meshheading:16766361... | lld:pubmed |
| pubmed-article:16766361 | pubmed:meshHeading | pubmed-meshheading:16766361... | lld:pubmed |
| pubmed-article:16766361 | pubmed:year | 2006 | lld:pubmed |
| pubmed-article:16766361 | pubmed:articleTitle | Modelling aspects of cancer dynamics: a review. | lld:pubmed |
| pubmed-article:16766361 | pubmed:affiliation | Centre for Mathematical Medicine, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK. helen.byrne@maths.nottingham.ac.uk | lld:pubmed |
| pubmed-article:16766361 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:16766361 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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