Increasingly complex in silico modeling approa ches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models\, such as those based on partia l differential equations\, are computationally affordable and allow large tumor sizes and long temporal windows to be studied\, but miss the discret e nature of many key underlying cellular processes. Individual-based appro aches provide a much more detailed description of tumors\, but have diffic ulties when trying to handle full-sized real cancers. Thus\, there exists a trade-off between the integration of macroscopic and microscopic informa tion\, now widely available\, and the ability to attain clinically-relevan t tumor sizes.
In this talk I will put forward a stochastic mesosco pic simulation framework that incorporates key cellular processes during t umor progression while keeping computational costs to a minimum. This fram ework captures a physical scale that allows both the incorporation of micr oscopic information\, tracking the spatio-temporal emergence of tumor hete rogeneity and the underlying evolutionary dynamics\, and the reconstructio n of clinically sized tumors at the resolution of standard medical imaging \, with the additional benefit of low computational cost.
Then\, I will illustrate the application of this modeling framework to two relevant problems in the context of clinical oncology: biomarker discovery\, where I will talk about the identification of a novel biomarker for breast and lung cancer\, NHOC\; and therapy optimization\, where I will talk about the search for optimal treatment schedules of temozolomide in glioblastoma \, such that patient survival is prolonged\, therapy-resistance emergence is delayed\, and toxicity is reduced.
Juan Jiménez-Sánchez got hi s BSc in Biotechnology in 2016 (Universidad Politécnica de Madrid\, Spain )\, and after earning a MSc in Biophysics (Universidad Autónoma de Madrid \, Spain)\, he received his PhD in Physics and Mathematics for his work at the Mathematical Oncology Laboratory (MOLAB)\, under the supervision of V íctor M. Pérez-García. Currently\, he is a postdoctoral fellow at the P olitecnico di Torino (Italy)\, working with Tommaso Lorenzi on the mathema tical modeling of muscular dystrophies. His interests lie on the mathemati cal and computational modeling of tumor growth and evolution\, with a stro ng focus on clinical applications\, and more specifically\, on therapy opt imization using virtual clinical trial approaches.
ONLINE: Link ZIH-Colloquia (https://bbb.tu-dresden.de/b/har-oa6-col-lmy)< /p> DTSTAMP:20260407T042323Z CREATED:20240913T053955Z LAST-MODIFIED:20241024T054253Z END:VEVENT END:VCALENDAR