This week in MathOnco 370

“This week in Mathematical Oncology” — May 7, 2026

> mathematical-oncology.org

From the editor:

I’m currently reading “The Scaling Era,” Dwarkesh Patel’s recent book on AI, published by Stripe Press. It’s a very compelling and well-written series of interviews with notable AI engineers and architects. The footnotes alone are worth the price of the book. One such footnote is the essay “The Unreasonable Effectiveness of Data” — which plays off the theme of Eugene Wigner’s 1960 classic essay, “The Unreasonable Effectiveness of Mathematics in the Natural Sciences.” Given the importance of the latter in math biology, I also suggest the former to you. It’s clear that scaling laws in AI are concrete enough for major investment in very expensive training runs. Is this something we’ll need to reconcile in our own field? These “memorization” algorithms may be sufficient for the application, given a large enough dataset.

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. How mathematical forms of chemotherapy and radiotherapy bias model-optimized predictions: Implications for model selection

   Changin Oh, Kathleen P. Wilkie

2. Age-structured mechanical models for tumor growth
   Doron Levy, Hyunah Lim, Antoine Mellet, Maeve Wildes

3. Dynamics of genetic and somatic trade-offs in ageing and mortality
   Danny Arends, David G. Ashbrook, Suheeta Roy, Lu Lu, …, Johan Auwerx, Evan G. Williams, Richard A. Miller, Robert W. Williams

4. Conditional success of adaptive therapy: The role of treatment thresholds and non-existence of optimal strategies revealed by mathematical modelling and optimal control
   Lanfei Sun, Haifeng Zhang, Kai Kang, Xiaoxin Wang, Leyi Zhang, Yanan Cai, Lei Zhang, Changjing Zhuge

5. Predictive digital twins with quantified uncertainty for patient-specific decision making in oncology
   Graham Pash, Umberto Villa, David A Hormuth II, Thomas E Yankeelov, Karen Willcox

6. Mathematical modeling of neural stem cell migration within brain using multi-fiber tractography
   Austin Hansen, Russell Rockne, Vikram Adhikarla, Margarita Gutova, Heyrim Cho

1. Theory of adhesion-driven self-organisation in growing tissues
   Carles Falcó, Samuel W. S. Johnson, Mohit P. Dalwadi, Philip K. Maini

2. Physics-Informed Neural Networks for Biological 2D+T Reaction-Diffusion Systems
   William Lavery, Jodie A. Cochrane, Christian Olesen, Dagim S. Tadele, John T. Nardini, Sara Hamis

1. The Unreasonable Effectiveness of Mathematics in the Natural Sciences
   Alon Halevy, Peter Norvig, Fernando Pereira
   Google, 2009

2. The Bitter Lesson

   Rich Sutton

   IncompleteIdeas.net, 2019

3. Informal connections outweigh coauthorship ties in academic impact
   Lluís Danús, William Dinneen, Carolina Torreblanca, Sandra González-Bailón

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: Toroidal Search Algorithm: A Topology-Inspired Metaheuristic with Applications to ODE Parameterization in Mathematical Oncology available at BioRxiv

Artist: Kathleen Wilkie and Changin Oh with assistance from Gemini

Caption: The image visually captures the essence of the Toroidal Search Algorithm (TSA): an optimization search strategy involving wandering agents traversing a rugged toroidal domain. Peaks and valleys represent the challenges of a complex objective landscape, and the explorers symbolize agents searching for promising regions of optimality. The torus reflects the periodic, wrapped boundaries that motivates TSA, allowing efficient search over the domain in a way that preserves continuity and encourages expansive exploration, without boundary stagnation. The sweeping arrow suggests winding behaviour on the torus, which is used by the algorithm for global exploration and local refinement. TSA is a robust and efficient topology-informed global search strategy for difficult optimization problems, such as inverse problems and virtual patient generation in mathematical oncology.

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.

1. Jobs

• Staff Associate I in Computational Oncology within Khanh N. Dinh Lab (Irving Institute for Cancer Dynamics, Columbia University) - Deadline: Jun 04, 2026

2. Conferences / Meetings

3. Special issues

• Tutorials on the Applications of AI/ML in Classical Mathematical Biosciences

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