“This week in Mathematical Oncology” — February 01, 2023
> mathematical-oncology.org
From the editor:
Another great week for papers! This week features topics like altruism, evolutionary dynamics, optimal control, and metabolism.
Papers are arguably the cornerstone of the scientific knowledge base, but I do love books, too. I think the Popular Science section can be genuinely useful. During a recent talk, I put up a slide of my favorites. All that to say: read widely, read across fields, read for inspiration, read whom you disagree, and most of all — send me your book recommendations.
Enjoy,
Jeffrey West
jeffrey.west@moffitt.org
Dynamic altruistic cooperation within breast tumors
Muhammad Sufyan Bin Masroni, Kee Wah Lee, Victor Kwan Min Lee, Siok Bian Ng, …, Soo Yong Tan, Evelyn Siew-Chuan Koay, Marco Archetti & Sai Mun LeongA new treatment for breast cancer using a combination of two drugs: AZD9496 and palbociclib
Ophir Nave, Yehuda Shor, Raziel Bar, Eliezer Elimelech Segal & Moriah SigronEvolutionary dynamics of glucose-deprived cancer cells: insights from experimentally informed mathematical modelling
Luis Almeida, Jérôme Alexandre Denis, Nathalie Ferrand, Tommaso Lorenzi, Antonin Prunet, Michéle Sabbah and Chiara VillaSafe optimal control of cancer using a Control Barrier Function technique
Zahra Ahmadi, Abolhassan RazminiaRecreating metabolic interactions of the tumour microenvironment
Rodrigo Curvello, Nikolaus Berndt, Sandra Hauser, Daniela LoessnerCharacterizing chromosomal instability-driven cancer evolution and cell fitness at a glance Icon for The Forest of Biologists
Andréa E. Tijhuis, Floris FoijerThe impact of radio-chemotherapy on tumour cells interaction with optimal control and sensitivity analysis
Arjun Kumar, Uma S. Dubey, Balram DubeyUnderstanding the Interplay of CAR-NK Cells and Triple-Negative Breast Cancer: Insights from Computational Modeling
Abazar Arabameri & Samaneh Arab
Deciphering the diversity and sequence of extracellular matrix and cellular spatial patterns in lung adenocarcinoma using topological data analysis
Iris H.R. Yoon, Robert Jenkins, Emma Colliver, Hanyun Zhang, David Novo, David Moore, Zoe Ramsden, Antonio Rullan, Xiao Fu, Yinyin Yuan, Heather A. Harrington, Charles Swanton, Helen M. Byrne, Erik SahaiA mathematical model of clonal hematopoiesis explaining phase transitions in myeloid leukemia
Lorand Gabriel Parajdi, Xue Bai, David Kegyes, Ciprian Tomuleasa
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:
Based on the paper: Learning from failed model predictions by Sara Hamis on the MathOnco Blog
Artist: DALL-E
Caption: Undeniably, a central part of contemporary mathematical oncology entails fitting mathematical models to bio-medical data. The ongoing surge in bio-medical data is exciting and, as a modeller, there is nothing quite like the feeling of seeing your model beautifully predict unseen data. When this happens, you high-five your collaborators, have a good night’s sleep, and prepare to publish! But what happens when your model predictions do not match (all) unseen data? Check out Sara’s blog post to read more.
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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