This week in MathOnco 166

Kronecker structure, radiotherapy forecasting, adaptive robustness, angiogenesis & resistance, phenotypic heterogeneity, and more

“This week in Mathematical Oncology” — Newsletter
June 10, 2021
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jeffrey.west@moffitt.org
From the editor:

Welcome to another edition of “This week in MathOnco,” with exciting topics such as Kronecker structure, radiotherapy forecasting, adaptive robustness, angiogenesis & resistance, phenotypic heterogeneity, and more. As always, scroll down to see this week’s epic cover artwork.

Next week promises to be an exciting week of MathOnco at the Society for Math Bio meeting. If you plan to attend, please let us know — we would love to meet you & chat MathOnco science, or brainstorm ideas to improve community engagement through this newsletter.

-Jeffrey West & Maximilian Strobl

  1. Mathematical modeling of multiple pathways in colorectal carcinogenesis using dynamical systems with Kronecker structure
    Saskia Haupt, Alexander Zeilmann, Aysel Ahadova, Hendrik Bläker, Magnus von Knebel Doeberitz, Matthias Kloor, Vincent Heuveline

  2. Forecasting Individual Patient Response to Radiotherapy in Head and Neck Cancer with a Dynamic Carrying Capacity Model
    Mohammad U. Zahid, Nuverah Mohsin, Abdallah S.R. Mohamed, Jimmy J. Caudell, Louis B.Harrison, Clifton D. Fuller, Eduardo G. Moros, Heiko Enderling

  3. Are Adaptive Chemotherapy Schedules Robust? A Three-Strategy Stochastic Evolutionary Game Theory Model
    Rajvir Dua, Yongqian Ma, Paul K. Newton

  4. A time-resolved experimental-mathematical model for predicting the response of glioma cells to single-dose radiation therapy
    Junyan Liu, David A Hormuth, Tessa Davis, Jianchen Yang, Matthew T McKenna, Angela M Jarrett, Heiko Enderling, Amy Brock, Thomas E Yankeelov

  5. Boolean dynamic modeling of cancer signaling networks: Prognosis, progression, and therapeutics
    Shubhank Sherekar, Ganesh A. Viswanathan

  6. Investigating epithelial-mesenchymal heterogeneity of tumors and circulating tumor cells with transcriptomic analysis and biophysical modeling
    Federico Bocci, Susmita Mandal, Tanishq Tejaswi, Mohit Kumar Jolly

  7. A validated mathematical model of FGFR3-mediated tumor growth reveals pathways to harness the benefits of combination targeted therapy and immunotherapy in bladder cancer
    Kamaldeen Okuneye, Daniel Bergman, Jeffrey C. Bloodworth, Alexander T. Pearson, Randy F. Sweis, Trachette L. Jackson

  8. Virtual Clinical Trial Simulations for a Novel KRASG12C Inhibitor (ASP2453) in Non-Small Cell Lung Cancer
    Hiroyuki Sayama, Diana Marcantonio, Takeyuki Nagashima, Masashi Shimazaki, Tsuyoshi Minematsu, Joshua F Apgar, John M Burke, Lucia Wille, Yasuhisa Nagasaka, Daniel C Kirouac

  9. Angiogenesis and chemotherapy resistance: optimizing chemotherapy scheduling using mathematical modeling
    Mariusz Bodzioch, Piotr Bajger, Urszula Foryś

  1. A mathematical model for phenotypic heterogeneity in breast cancer with implications for therapeutic strategies
    Xin Li, D. Thirumalai

  2. Higher-order effects, continuous species interactions, and trait evolution shape microbial spatial dynamics
    Anshuman Swain, Levi Fussell, William F Fagan

  1. Modeling multiple pathways of carcinogenesis using the Kronecker structure
    The Mathematical Oncology Blog

The newsletter now has a dedicated homepage (thisweekmathonco.substack.com), which allows us to post cover artwork for each issue. We encourage submissions that coincide with the release of a recent paper from your group. Today’s submission was contributed by Saskia Haupt and Alexander Zeilmann, and is inspired by their recent work on the oncogenesis of colorectal cancer:

Caption: Colorectal cancer usually develops from normal mucosa over precursor lesions to a carcinoma which can be analyzed using a histopathological section (from bottom to top, left part of figure). We developed a mathematical model to describe this process, including multiple pathways of carcinogenesis, with ordinary differential equations and the Kronecker structure (right part of figure). This specific matrix structure is the key to our paper. It allows for detailed mathematical analysis, low computational costs and medical interpretability.

To find out more, read our paper or our blog post on the MathOnco Blog, reach out on Twitter, or join our mini-symposium at SMB 2021 (Session 11 and 12).

Created by Saskia Haupt & Alexander Zeilmann, submitted in conjunction with their recent publication.

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