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CDS Progress Meeting: Dr. Phillip Altrock, Mathematical models of diversity in cancer: from clonal interactions to cancer

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CDS Progress Meeting: Dr. Phillip Altrock, Mathematical models of diversity in cancer: from clonal interactions to cancer stem cells to pre-cancer screening

  • Datum: 02.03.2017
  • Uhrzeit: 13:00 - 14:00
  • Vortragender: Dr. Philipp Altrock
  • Assistant Member, Moffitt Cancer Center, Tampa, Florida, USA
  • Ort: Otto-von-Guericke-Universität Magdeburg, Gebäude 28
  • Raum: Besprechungsraum 027
  • Gastgeber: Forschungszentrum Dynamische Systeme: Systems Engineering
  • Kontakt: someyer@ovgu.de

Das Forschungszentrum Dynamische Systeme: Systems Engineering (CDS) vereinigt Forschergruppen mit ingenieurwissenschaftlichem, systemtheoretischem, mathematischem, medizinischem und biologischem Hintergrund. Wissenschaftler aus fünf Fakultäten der Otto-von-Guericke-Universität und aus dem Max-Planck-Institut arbeiten eng und fachübergreifend zusammen, um die gemeinsam gesetzten Ziele zu erreichen.

Das CDS ist gemäß des Hochschulgesetzes des Landes Sachsen-Anhalt ein Forschungszentrum der Otto-von-Guericke-Universität Magdeburg.

Regelmäßig finden Vorträge zur aktuellen Forschung im Rahmen der Reihe CDS Progress Report statt.


Abstract Dr. Philipp Altrock

Cancer is a probabilistic and nonlinear dynamics process. Heterogeneity on the genetic and epigenetic, cellular, and environmental levels can lead to drastic variability in patient outcomes. Mathematical and computational models of disease onset, progression and treatment response have the potential to improve our mechanistic understanding of how underlying processes affect this variability, and promise translatability from findings in one disease to another. I will describe three ways to model different levels of heterogeneity. First, I will describe how non-cell-autonomous interactions can lead to maintenance of diversity during tumor growth, and diversity loss during treatment. Second, I will describe a basic mathematical approach to describe the dynamics of cancer stem cells and differentiated cancer cells, which can be used to analyze individual patient trajectories in leukemia and inform treatment continuation. Third, I will present a quantitative method to use epidemiological data of pre-cancer incidence and progression to inform potential screening strategies using the example of multiple myeloma. Future efforts in quantitative systems medicine will have to address whether and how such different levels of description of disease can be used synergistically for personalized cancer treatment, and whether there are limitations to personalized approaches that stem from inherent heterogeneity.




 
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