This week in MathOnco 375
Reinforcement learning, systems biology tumor growth, and more...
“This week in Mathematical Oncology” — July 9, 2026
> mathematical-oncology.org
From the editor:
Readers of TWiMO probably do not need to be convinced that mathematics provides an exceptional tool for unraveling cancer complexity. From intra- and inter-tumor heterogeneity to angiogenesis, immunity, pharmacokinetics, pharmacodynamics, and evolution, tumor progression and treatment response are shaped by many interacting processes. Mathematical Oncology has contributed to all of them. Yet one question remains surprisingly important: how often do we truly bring these pieces together?
One of the most interesting patterns we observed in our recent analysis “140 Years of mathematical modeling in oncology through AI-assisted curation“ is a persistent separation between researchers working on drug delivery, pharmacokinetics and pharmacodynamics, and those focused on cancer evolution and ecological interactions. Most of us would probably agree that all these aspects are essential for translating mathematical models into clinical practice. Yet, eco-evolutionary Mathematical Oncology still often treats treatment as an abstract pressure, rather than as a dynamic process shaped by drug delivery, exposure, clearance, and tissue penetration.
Perhaps this is one of the central challenges for the next generation of Mathematical Oncology. In an era in which Claude can generate thousands of lines of code in minutes, the limiting factor may no longer be implementation, but integration. Can we close this intellectual gap by building models in which pharmacokinetics is not an afterthought, but a driver of cancer evolution?
Enjoy,
Franco Pradelli
franco.pradelli@moffitt.org
PS Check out the postdoc job post at Roskilde University and Novo Nordisk (Johnny T. Ottesen, Johnny@ruc.dk).
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.
Early-stage cancer results in a multiplicative increase in cell-free DNA originating from healthy tissue
Konstantinos Mamis, Ivana BozicMathematical modeling of JAK2V617F clonal expansion in a general population cohort
Jordan Snyder, Morten Andersen, Johanne Gudmand-Høyer, …, Hans C. Hasselbalch, Johnny T. Ottesen, Thomas StiehlMathematical modeling of immune counter-regulation predicts efficacy of fractionated CD8+ T cell dosing strategies
Hiroki Kasai, Koji Nagaoka, Artem Lysenko, Kazuhiro Kakimi & Tatsuhiko TsunodaIn silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives
Matteo Italia, José Garcia Otero, Juan Jiménez-Sánchez, Fabio Dercole & Juan Belmonte-BeitiaA Precision Tumor Growth Model Integrating Time-Resolved Flow Cytometry: Predicting Fractionation Efficacy and Immunotherapy Scheduling.
Di Y, Huang L, Zhao L, Gao D, Yang H, Zhang Z, Zhong Y, Hu W.A new cancer progression model: From synthetic tumors to real data and back. Volpatto D, Contaldo SG, Pernice S, Beccuti M, Cordero F, Sirovich R.
When effective anticancer therapies are, in fact, destabilizing the tumor’s Group Phenotypic Composition
Frédéric Thomas, Antoine M. Dujon, Andriy Marusyk, James DeGregori, …, Jordan Meliani, Robert Noble, Aurora M. Nedelcu & Robert GatenbyReinforcement learning for chemotherapy scheduling in a stochastic tumor evolution model
M. Giles, P. K. NewtonDetermining parameter impact in systems biology models via sensitivity analysis: a comparative approach
Kelsey I. Gasior
Indirect genomic effects shape cancer risk across species
George Butler, Srividya Ramakrishnan, Tyler Collins, Joanna Baker, Sarah R Amend, The Vertebrate Genomes Project Consortium Phase I, Michael C Schatz, Chris Venditti, Kenneth J Pienta
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 paper: The Role of Viral Dynamics and Infectivity in Models of Oncolytic Virotherapy for Tumours with Different Motility published in Bulletin of Mathematical Biology
Artist: David Morselli (LinkedIn, Webpage)
Caption: The use of oncolytic viruses as cancer treatment has received considerable attention in recent years, however the spatial dynamics of this viral infection are still poorly understood. In this work, we compare probabilistic, individual approaches with continuous, spatially inhomogeneous models and investigate the importance of different tumour motility and different mathematical representations of viral infectivity. Persistent oscillations as the ones shown in the artwork are only possible when the viral population is explicitly modelled. The rich spatiotemporal structures observed in deterministic oncolytic virotherapy models has been recently linked to the complex Ginzburg-Landau amplitude equation (Bansod and Hillen, 2026). On the other hand, this artwork shows that the stochasticity further enhances the formation of highly dynamic patterns.
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