Top-Down Multiscale Simulation of Tumor Response to Treatment in the Context of In Silico Oncology. The Notion of Oncosimulator

  • Georgios Stamatakos
Part of the SIMAI Springer Series book series (SEMA SIMAI)


The aim of this chapter is to provide a brief introduction into the basics of a top-down multilevel tumor dynamics modeling method primarily based on discrete entity consideration and manipulation. The method is clinically oriented, one of its major goals being to support patient individualized treatment optimization through experimentation in silico (= on the computer). Therefore, modeling of the treatment response of clinical tumors lies at the epicenter of the approach. Macroscopic data, including i.a. anatomic and metabolic tomographic images of the tumor, provide the framework for the integration of data and mechanisms pertaining to lower and lower biocomplexity levels such as clinically approved cellular and molecular biomarkers. The method also provides a powerful framework for the investigation of multilevel (multiscale) tumor biology in the generic investigational context. The Oncosimulator, a multiscale physics and biomedical engineering concept and construct tightly associated with the method and currently undergoing clinical adaptation, optimization and validation, is also sketched. A brief outline of the approach is provided in natural language. Two specific models of tumor response to chemotherapeutic and radiotherapeutic schemes are briefly outlined and indicative results are presented in order to exemplify the application potential of the method. The chapter concludes with a discussion of several important aspects of the method including i.a. numerical analysis aspects, technological issues, model extensions and validation within the framework of actual running clinico-genomic trials. Future perspectives and challenges are also addressed.


Biological Cell Discretization Mesh Candidate Scheme Discrete Time Point Tumor Dynamic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been supported in part by the European Commission under the projects “ACGT: Advancing Clinicogenomic Trials on Cancer” (FP6-2005-IST-026996), Contra Cancrum: Clinically Oriented Translational Cancer Multilevel Modelling” (FP7-ICT-2007-2- 223979), TUMOR: Transatlantic Tumor Model Repositories (FP7-ICT-2009.5.4-247754) and p- Medicine: Personalized Medicine (FP7-ICT-2009.5.3-270089).


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© Springer-Verlag Italia 2012

Authors and Affiliations

  • Georgios Stamatakos
    • 1
  1. 1.Silico Oncology Group. Laboratory of Microwaves and Fiber Optics. Institute of Communication and Computer SystemsNational Technical University of AthensZografosGreece

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