This week in MathOnco 372

“This week in Mathematical Oncology” — May 21, 2026

> mathematical-oncology.org

From the editor:

Growth dynamics (also known as “growth laws”) have always been a part of mathematical oncology, and might even represent the very first published example of a math model in oncology. Nearly 150 years later, there remain many open questions about the dynamics of natural tumor progression. In today’s issue, we add to the decades-old conversation with our recent publication on the role of contact inhibition in cell growth dynamics.

In other news, we posted a couple of new conferences posted on our conferences page, notably the London Math Bio Sept 3-4.

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

TWiMO is brought to you by Maximilian Strobl, Sarah Groves, Veronika Hofmann, Yifan Chen, Franco Pradelli, and Sandy Anderson. Find out more about the team here.

1. Universal principles of cell population growth follow from local contact inhibition
   Gregory J. Kimmel, Sadegh Marzban, Mehdi Damaghi, Arne Traulsen, Alexander R.A. Anderson, Jeffrey West, Philipp M. Altrock

2. Ecotypes of triple-negative breast cancer in response to chemotherapy
   Yun Yan, Yiyun Lin, Tapsi Kumar, Shanshan Bai, …, Lei Huo, Stacy Moulder, Clinton Yam, Nicholas Navin

3. Antiangiogenic therapy enhances CAR-T cell efficacy in solid tumors: Insights from a hybrid multiscale model
   Sayyed Mohammad Ali Mortazavi, Bahar Firoozabadi

4. Advancing Cancer Prevention through Precision Prediction
   Miquel Angel Pujana, Joan Brunet, Antonis C. Antoniou

5. Model-supported patient stratification using multi-objective synergy optimization in combination therapy
   Jana L. Gevertz, Irina Kareva

6. A Hybrid Machine Learning Framework for Mechanistically Interpretable Latent Parameter Inference in a Spatiotemporal CAR-T Therapy Model for Solid Tumours
   Maxim Polyakov

7. Optimization of sequential therapies to maximize extinction of resistant bacteria through collateral sensitivity
   Javier Molina-Hernández, José A. Cuesta, Beatriz Pascual-Escudero, Saúl Ares, Pablo Catalán

8. Harnessing myeloid cell plasticity for cancer therapy
   Emilio Sanseviero, Simon T. Barry & Dmitry I. Gabrilovich

9. A Network Based Model for Predicting Spatial Progression of Metastasis
   Khimeer Singh & Byron A. Jacobs

1. Language Models Use Trigonometry to Do Addition
   Subhash Kantamneni, Max Tegmark

2. Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
   Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Iris Braunstein, Pattie Maes

1. Introducing camdl: engineering rigor for stochastic compartmental modelling

   Vince Buffalo

2. How to design effective scientific figures
   Ryosuke Fujii

The newsletter now has a dedicated homepage where we post the cover artwork for each issue, curated by Maximilian Strobl, Veronika Hofmann, Yifan Chen, and Sarah Groves. We encourage submissions that coincide with the release of a recent paper from your group. This week’s artwork:

Based on the preprint: Pseudodynamics+: Reconstructing Population Dynamics from Time-Resolved Single Cell Landscapes with Physics Informed Neural Networks available at bioRxiv

Artist: Weizhong Zheng (LinkedIn) assisted by Nano Banana 2

Caption: This artwork illustrates the core innovation of pseudodynamics+: the use of Physics-Informed Neural Networks (PINNs) to solve the "flow equations" of stem cell differentiation. In this visualization, the cellular landscape is not a static map but a dynamic system governed by a rigorous physical law. The millions of glowing points represent the individual cells that constitute the "mass" of a tissue. Guiding these particles are the mathematical terms of the partial differential equation (PDE), including growth, drift, and diffusion. By solving these equations directly on high-dimensional single-cell data without discretization, pseudodynamics+ allows us to see how molecular changes at the single-cell level translate into the massive, coordinated expansion and differentiation of entire populations. This "population-aware" approach provides a quantitative bridge between molecular profiles and the physical reality of tissue development and disease.

Visit the mathematical oncology page to view jobs, meetings, and special issues. We will post new additions here, but the full list can found at mathematical-oncology.org.

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