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
>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.

• Networks in Cancer: From Symmetry Breaking to Targeted Therapy
  Guest Editor: Cristian Axenie, Roman Bauer, María Rodríguez Martínez

• Understanding the Evolutionary Dynamics and Ecology of Cancer in Treatment Resistance
  Guest Editor: David Basanta

• Frontiers in quantitative cancer modeling
  Guest Editors: Mohit Kumar Jolly, Heiko Enderling

• Mathematical Immuno-Oncology postdoc (Kasia Rejniak, Moffitt)

• Research Fellow in Computational Systems Biology Cancer Research (Simon Mitchell)

• PhD student - Measuring cancer evolution in a changing tumour microenvironment (Xiaowei Jiang)

• Postdoctoral Scholar - Genetics and Genome Sciences (Christopher McFarland)

• Postdoc on colorectal cancer evolution or cancer immunotherapy (Ivana Bozic)

• Postdoc on cancer/immune modeling and machine learning (Eduardo Sontag)

• Early Stage Researcher: Impact of different resistance mechanisms on the outcomes of cancer treatment game (Weini Huang)

• Early Stage Researcher: Evolutionary therapy in ovarian cancer (Ben Werner)

• CDC Steven M. Teutsch Prevention Effectiveness Fellowship

• Research Associate - Biostatistician (University of Manchester)

• Director Quantitative Clinical Pharmacology, Oncology

• Biosimulation Scientist (Physiomics)

• Systems Biology Modeler Positions in Biopharma Consulting Company (Helen Moore)

• Math/statistical models of stem cell lineage dynamics and cancer genomics - Postdoc (Adam MacLean)

• Machine Learning / Math Modeling Postdoc (Noemi Andor)

• Cancer immunotherapy / systems bio postdoc (Elana J Fertig)

• Integrated Math Oncology Ph.D. (Moffitt)

• Integrated Math Oncology Postdoc (Sandy Anderson)

• Postdoctoral Research Position in Computational Oncology (Tom Yankeelov)

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