This week in MathOnco 195

Novelty in science, dose dependence, higher-order spatial interactions, spatial transcriptomics, predictive modeling, and more

“This week in Mathematical Oncology” — Feb. 3, 2022

> mathematical-oncology.org

From the editor:

Dear readers, please enjoy this week’s collection of articles on topics like novelty in science, dose dependence, higher-order spatial interactions, spatial transcriptomics, predictive modeling, and more!

Jeffrey West
jeffrey.west@moffitt.org

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

2. Analysis and Visualization of Spatial Transcriptomic Data
   Boxiang Liu, Yanjun Li, Liang Zhang

3. Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy
   Alexander B. Brummer, Xin Yang, Eric Ma, Margarita Gutova, Christine E. Brown, Russell C. Rockne

4. Combining dynamic modeling with machine learning can be the key for the integration of mathematical and clinical oncology: Comment on “Improving cancer treatments via dynamical biophysical models”
   Haralampos Hatzikirou

1. Computational Model of Heterogeneity in Melanoma: Designing Therapies and Predicting Outcomes
   Arran Hodgkinson, Dumitru Trucu, Matthieu Lacroix, Laurent Le Cam, Ovidiu Radulescu

2. How to use transcriptomic data for game-theoretic modeling of treatment-induced resistance in cancer cells? A case study in patient-derived glioblastoma organoids
   Weronika Gąska, Christer Lokh, Maikel Verduin, Marc Vooijs, Rachel Cavill, Kateřina Staňková

3. Mapping Spatiotemporal Heterogeneity in Tumour Progression by Integrating High-Throughput Imaging and Omics Analysis
   Pooja Annasaheb Patkulkar, Ayalur Raghu Subbalakshmi, Mohit Kumar Jolly, Sanhita Sinharay

4. Using mathematical modelling to identify data requirements for increased prediction accuracy in radiotherapy
   Thomas D. Lewin, Philip K. Maini, Eduardo G. Moros, Jimmy Caudell, Heiko Enderling, Helen M. Byrne

1. My LaTeX Workflow – Editor
   The “My LaTeX Workflow” Series
   Alexander Zeilmann: “This post is part of my series on my LaTeX workflow. In it, I explain which editor I use and how I configured it. I will start with a few general-purpose settings and useful extensions before diving into the LaTeX specific configurations.”

2. Novelty in Science: A guide for reviewers
   Perceiving Systems Blog
   Michael J. Black: “Reviewers have strong ideas about what makes a paper acceptable in top conferences like CVPR. They know that getting into such conferences is hard and that getting a paper in is prestigious. So, the papers that get in must be really special. This is true, but what makes a paper special? A key focus of many reviewers is novelty. But what is novelty in science?”

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: Understanding the evolution and stability of diversity in microbial communities, especially in the presence of ample antagonistic interactions, has been an important problem in theoretical biology. In our recent paper, we explored the dynamics of diversity and stability in microbial assemblages using a trait-focused agent-based model that takes into account a continuous species space, higher-order interaction among microbes and a range of mutational regimes. On this cover, we depict the growth of their microbial model in the space around the text. Color is used to represent microbial species, and the colors change as the microbes interact and mutate on the lattice. The particular parameters for this simulation result in a static and stable final state, but simple interaction rules embedded in their model can capture a range of dynamic behaviours. Although our model was aimed at answering questions in microbial assemblages, several elements, such as the inclusion of direct and indirect ecological interactions, the interplay of multiple cell lineages, and spatio-temporal heterogeneity in the community, make the model a general framework for investigating diverse biological systems, including oncological ones.

Created by: Anshuman Swain & Levi Fussell

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