LEADER 03748nam 22005535 450 001 9910337938503321 005 20230217192008.0 010 $a3-030-19080-3 024 7 $a10.1007/978-3-030-19080-4 035 $a(CKB)4100000008340236 035 $a(MiAaPQ)EBC5782530 035 $a(DE-He213)978-3-030-19080-4 035 $a(PPN)238493105 035 $a(EXLCZ)994100000008340236 100 $a20190601d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSpatiotemporal Modeling of Cancer Immunotherapy $ePartial Differential Equation Analysis in R /$fby William E. Schiesser 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (116 pages) $cillustrations 311 $a3-030-17635-5 327 $aFixed Boundary PDE Model Formulation -- Fixed Boundary PDE Model Implementation -- Fixed Boundary PDE Model Output -- Moving Boundary PDE Model Implementation -- Moving Boundary PDE Model Output -- Index. 330 $aThe focus of this book is a detailed discussion of a dual cancer vaccine (CV)-immune checkpoint inhibitor (ICI) mathematical model formulated as a system of partial differential equations (PDEs) defining the spatiotemporal distribution of cells and biochemicals during tumor growth. A computer implementation of the model is discussed in detail for the quantitative evaluation of CV-ICI therapy. The coding (programming) consists of a series of routines in R, a quality, open-source scientific computing system that is readily available from the internet. The routines are based on the method of lines (MOL), a general PDE algorithm that can be executed on modest computers within the basic R system. The reader can download and use the routines to confirm the model solutions reported in the book, then experiment with the model by varying the parameters and modifying/extending the equations, and even studying alternative models with the PDE methodology demonstrated by the CV-ICI model. Spatiotemporal Modeling of Cancer Immunotherapy: Partial Differential Equation Analysis in R facilitates the use of the model, and more generally, computer- based analysis of cancer immunotherapy mathematical models, as a step toward the development and quantitative evaluation of the immunotherapy approach to the treatment of cancer. 606 $aBiomedical engineering 606 $aMathematical models 606 $aCancer$xResearch 606 $aR (Computer program language) 606 $aBiomedical Engineering/Biotechnology$3https://scigraph.springernature.com/ontologies/product-market-codes/B24000 606 $aMathematical Modeling and Industrial Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M14068 606 $aCancer Research$3https://scigraph.springernature.com/ontologies/product-market-codes/B11001 606 $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X 615 0$aBiomedical engineering. 615 0$aMathematical models. 615 0$aCancer$xResearch. 615 0$aR (Computer program language) 615 14$aBiomedical Engineering/Biotechnology. 615 24$aMathematical Modeling and Industrial Mathematics. 615 24$aCancer Research. 615 24$aBiomedical Engineering and Bioengineering. 676 $a515.353 700 $aSchiesser$b William E$4aut$4http://id.loc.gov/vocabulary/relators/aut$0506133 906 $aBOOK 912 $a9910337938503321 996 $aSpatiotemporal Modeling of Cancer Immunotherapy$91959940 997 $aUNINA