This week in MathOnco 187

Spatial quantitative systems pharmacology, causality in cancer, nano-particle drug delivery, antifragility, drug resistance, hierarchical tissue organization

“This week in Mathematical Oncology” — Nov. 11, 2021
> mathematical-oncology.org
From the editor:

This week’s edition includes topics such as personalized radiotherapy, causality in cancer, nano-particle drug delivery, antifragility, drug resistance, hierarchical tissue organization, and more!

Enjoy!

Jeffrey West
jeffrey.west@moffitt.org

  1. A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor–Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy
    Chang Gong, Alvaro Ruiz-Martinez, Holly Kimko, Aleksander S. Popel

  2. Philosophy of Biology: Characterizing causality in cancer
    Elena Rondeau, Nicolas Larmonier, Thomas Pradeu, Andreas Bikfalvi

  3. A hybrid PDE–ABM model for viral dynamics with application to SARS-CoV-2 and influenza
    Sadegh Marzban, Renji Han, Nóra Juhász, Gergely Röst

  4. Using Parallel Coordinates in Optimization of Nano-Particle Drug Delivery
    Timoleon Kipouros, Ibrahim Chamseddine, Michael Kokkolaras

  5. Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach
    Farshad Moradi Kashkooli, M. Soltani

  6. Modeling of drug resistance: Comparison of two hypotheses for slowly proliferating tumors on the example of low-grade gliomas
    Marek Bodnar, Urszula Foryś

  7. Measuring as a new mode of inquiry that bridges evolutionary game theory and cancer biology
    Artem Kaznatcheev, Chia-Hua Lin

  8. A Useful and Sustainable Role for N-of-1 Trials in the Healthcare Ecosystem
    Harry P. Selker, Theodora Cohen, Ralph B. D’Agostino, Willard H. Dere, …, Christopher H. Schmid, Vicki Seyfert-Margolis, Mark Trusheim, Hans-Georg Eichler

  1. Maintaining and escaping feedback control in hierarchically organised tissue: a case study of the intestinal epithelium
    Matthias M. Fischer, Hanspeter Herzel, Nils Blüthgen

  2. Improving mathematical models of cancer by including resistance to therapy: a study in non-small cell lung cancer
    Virginia Ardévol Martinez, Narmin Ghaffari Laleh, Monica Salvioli, Frank Thuijsman, Joel S. Brown, Rachel Cavill, Jakob Nikolas Kather, Katerina Stankova

  3. Heterogeneity, turn-over rate and karyotype space shape susceptibility to missegregation-induced extinction
    Gregory J Kimmel, Thomas Veith, Samuel Bakhoum, Philipp Martin Altrock, Noemi Andor

  4. Emerging Topics in Cancer Evolution
    Mohammed El-Kebir, Quaid Morris, Layla Oesper, S. Cenk Sahinalp

  5. Joint inference of repeated evolutionary trajectories and patterns of clonal exclusivity or co-occurrence from tumor mutation trees
    Xiang Ge Luo, Jack Kuipers, Niko Beerenwinkel

  6. Decreased cell stiffness facilitates detachment and migration of breast cancer cells in 3D collagen matrices: An exploratory study
    Ghodeejah Higgins, Jessica E Kim, Jacopo Ferruzzi, Tamer Abdalrahman, Thomas Franz, Muhammad H Zaman

  1. Cancer Research and Anti-fragile therapy
    Lindy Talk with Jeffrey West: “Our conversation spanned the breadth of evolution & ecology in cancer. How can evolutionary principles can be useful in designing cancer treatment? We specifically discuss antifragility and future work that he is doing to design antifragile cancer therapy."

The newsletter now has a dedicated homepage where we post the cover artwork for each issue. We encourage submissions that coincide with the release of a recent paper from your group.

Caption: Tumor morphology is affected by cancer stem-like cell (CSC) mechanisms, such as migration rate and asymmetric division probability. In our recent paper, we developed a platform spQSP-IO which combines the strength of QSP models and spatially resolved agent-based models (ABM), and created a model of cancer development in the context of anti-cancer immunity and immune checkpoint inhibition therapy. In the figure, red, brown and grey indicate CSC, progenitor and senescent cells, respectively. Asymmetric division probability is low in row 1 and 2, and high in row 3 and 4. Migration rate is low in row 1 and 3, and high in row 2 and 4.  Column 1-3 show the temporal change of tumor shape (on day 60, 120 and 180); the last column shows cancer cells with color corresponding to their CSC progeny.

Created by: Chang Gong & Aleksander S. Popel.

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