This week in MathOnco 295

Prediction, spatial models, machine learning & mechanisms, discrete-continuum models, and more

“This week in Mathematical Oncology” — May 16, 2024

> mathematical-oncology.org

From the editor:

Do you enjoy these Math Onco resources? Would you consider joining our team? Reply to this email to volunteer. We are looking for volunteers at all career stages: graduate students, postdocs, or group leaders.

Currently, volunteers help in various ways such as 1) updating the website, 2) uploading submitted blog posts 3) uploading lab groups description & location, and of course 4) writing blog posts. If you have other ideas you’d like to see within these Math Onco resources, we’d love to have you join the team.

Thanks,

Jeffrey West
jeffrey.west@moffitt.org

1. A seven-step guide to spatial, agent-based modelling of tumour evolution
   Blair Colyer, Maciej Bak, David Basanta, Robert Noble

2. Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment
   Sebastien Benzekry ,Michalis Mastri,Chiara Nicolò,John M. L. Ebos

3. A stochastic programming approach to the antibiotics time machine problem
   Oğuz Mesüm, Ali Rana Atilgan, Burak Kocuk

4. Spatially resolved analysis of microenvironmental gradient impact on cancer cell phenotypes
   Jamie Auxillos, Roxane Crouigneau, Yan- Fang Li, Yifan Dai, Arnaud Stigliani, Isabella Tavernaro, Ute Resch-Genger, Albin Sandelin, Rodolphe Marie, Stine F. Pedersen

1. Estimating scale-specific and localized spatial patterns in allele frequency
   Jesse R. Lasky, Margarita Takou, Diana Gamba, Timothy H. Keitt

2. Simulations probe the role of space in the interplay between drug-sensitive and drug-resistant cancer cells
   Kira Pugh, Sara Hamis, Gibin Powathil, Rhys DO Jones

3. Relapse prediction in multiple myeloma patients treated with isatuximab, carfilzomib, and dexamethasone
   Even Moa Myklebust, Fredrik Schjesvold, Arnoldo Frigessi, Kevin Leder, Jasmine Foo, Alvaro Köhn-Luque

4. A shift from pleiotropic to modular adaptation revealed by a high-resolution two-step adaptive walk
   Grant Kinsler, Yuping Li, Gavin Sherlock, Dmitri Petrov

5. A hybrid discrete-continuum modelling approach for the interactions of the immune system with oncolytic viral infections
   David Morselli, Marcello E. Delitala, Adrianne L. Jenner, Federico Frascoli

6. From hyperinsulinemia to cancer progression: how diminishing glucose storage capacity fuels insulin resistance
   Irina Kareva

7. COEXIST: Coordinated single-cell integration of serial multiplexed tissue images
   Robert T. Heussner, Cameron F. Watson, Christopher Z. Eddy, Kunlun Wang, Eric M. Cramer, Allison L. Creason, Gordon B. Mills, Young Hwan Chang

1. Graduate Course on Mathematical Oncology at the Fields Institute in Fall 2024

   During the Thematic Program in Mathematical Oncology at the Fields Institute in Toronto, we will offer a graduate level course in Mathematical Oncology in Fall 2024. The course is hybrid and can be taken online or in person.

2. graph-tool
   ”Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other Python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.”

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. This week’s artwork:

Artist: Jeffrey West

Caption: This week’s artwork highlights MathOnco, with a call to action! Are you interested in joining our team? Click here to learn more about our current team. Reply to this email to volunteer. Currently, volunteers help in various ways such as 1) updating the website, 2) uploading submitted blog posts 3) uploading lab groups description & location, and of course 4) writing blog posts. If you have other ideas you’d like to see within these Math Onco resources, we’d love to have you join the team.

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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