| pubmed-article:21187230 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:21187230 | lifeskim:mentions | umls-concept:C0013227 | lld:lifeskim |
| pubmed-article:21187230 | lifeskim:mentions | umls-concept:C0006826 | lld:lifeskim |
| pubmed-article:21187230 | lifeskim:mentions | umls-concept:C0012634 | lld:lifeskim |
| pubmed-article:21187230 | lifeskim:mentions | umls-concept:C1511790 | lld:lifeskim |
| pubmed-article:21187230 | lifeskim:mentions | umls-concept:C1704259 | lld:lifeskim |
| pubmed-article:21187230 | lifeskim:mentions | umls-concept:C0439855 | lld:lifeskim |
| pubmed-article:21187230 | lifeskim:mentions | umls-concept:C0870071 | lld:lifeskim |
| pubmed-article:21187230 | lifeskim:mentions | umls-concept:C0336791 | lld:lifeskim |
| pubmed-article:21187230 | lifeskim:mentions | umls-concept:C1521840 | lld:lifeskim |
| pubmed-article:21187230 | pubmed:dateCreated | 2010-12-28 | lld:pubmed |
| pubmed-article:21187230 | pubmed:abstractText | In the near future, computational tools and methods based on the mathematical modeling of biomedically relevant networks and pathways will be necessary for the design of therapeutic strategies that fight complex multifactorial diseases. Beyond the use of pharmacokinetic and pharmacodynamic approaches, we propose here the use of dynamic modeling as a tool for describing and analyzing the structure and responses of signaling, genetic and metabolic networks involved in such diseases. Specifically, we discuss the design and construction of meaningful models of biochemical networks, as well as tools, concepts, and strategies for using these models in the search of potential drug targets. We describe three different families of computational tools: predictive model simulations as tools for designing optimal drug profiles and doses; sensitivity analysis as a method to detect key interactions that affect critical outcomes and other characteristics of the network; and other tools integrating mathematical modeling with advanced computation and optimization for detecting potential drug targets. Furthermore, we show how potential drug targets detected with these approaches can be used in a computer-aided context to design or select new drug molecules. All concepts are illustrated with simplified examples and with actual case studies extracted from the recent literature. | lld:pubmed |
| pubmed-article:21187230 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:21187230 | pubmed:language | eng | lld:pubmed |
| pubmed-article:21187230 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:21187230 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:21187230 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:21187230 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:21187230 | pubmed:issn | 1557-7988 | lld:pubmed |
| pubmed-article:21187230 | pubmed:author | pubmed-author:VoitEberhard... | lld:pubmed |
| pubmed-article:21187230 | pubmed:author | pubmed-author:VeraJulioJ | lld:pubmed |
| pubmed-article:21187230 | pubmed:author | pubmed-author:Marin-Sanguin... | lld:pubmed |
| pubmed-article:21187230 | pubmed:author | pubmed-author:GuptaShailend... | lld:pubmed |
| pubmed-article:21187230 | pubmed:copyrightInfo | © 2011 Elsevier Inc. All rights reserved. | lld:pubmed |
| pubmed-article:21187230 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:21187230 | pubmed:volume | 487 | lld:pubmed |
| pubmed-article:21187230 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:21187230 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:21187230 | pubmed:pagination | 319-69 | lld:pubmed |
| pubmed-article:21187230 | pubmed:meshHeading | pubmed-meshheading:21187230... | lld:pubmed |
| pubmed-article:21187230 | pubmed:meshHeading | pubmed-meshheading:21187230... | lld:pubmed |
| pubmed-article:21187230 | pubmed:meshHeading | pubmed-meshheading:21187230... | lld:pubmed |
| pubmed-article:21187230 | pubmed:meshHeading | pubmed-meshheading:21187230... | lld:pubmed |
| pubmed-article:21187230 | pubmed:meshHeading | pubmed-meshheading:21187230... | lld:pubmed |
| pubmed-article:21187230 | pubmed:meshHeading | pubmed-meshheading:21187230... | lld:pubmed |
| pubmed-article:21187230 | pubmed:meshHeading | pubmed-meshheading:21187230... | lld:pubmed |
| pubmed-article:21187230 | pubmed:meshHeading | pubmed-meshheading:21187230... | lld:pubmed |
| pubmed-article:21187230 | pubmed:meshHeading | pubmed-meshheading:21187230... | lld:pubmed |
| pubmed-article:21187230 | pubmed:year | 2011 | lld:pubmed |
| pubmed-article:21187230 | pubmed:articleTitle | Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases. | lld:pubmed |
| pubmed-article:21187230 | pubmed:affiliation | Department of Membrane Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany. | lld:pubmed |
| pubmed-article:21187230 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:21187230 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| pubmed-article:21187230 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:21187230 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
