This week in MathOnco 250

Clonal architecture, evolutionary treatment strategies, chemoattractants, disruptive science, and more

“This week in Mathematical Oncology” — Apr. 13, 2023

> mathematical-oncology.org

From the editor:

Today we feature articles on clonal architecture, evolutionary treatment strategies, chemoattractants, disruptive science, and more.

I am looking forward to greeting & meeting many of you at AACR in Orlando this weekend — if you’re going drop me a tweet or reply to this email!

Thanks,

Jeffrey West
jeffrey.west@moffitt.org

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“If one’s goal is explanation rather than description then different criteria must be applied. The most important criterion, in our view, was enunciated by Einstein: ‘A model should be as simple as possible. But no simpler.’ That is, a model should seek to explain the underlying principles of a phenomenon, but no more. We are not trying to fit data nor make quantitative predictions. Rather we seek to understand. Thus we ask only that our models describe qualitative features in the simplest possible way."
- J. Murray¹

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1. Evolution-Informed Strategies for Combating Drug Resistance in Cancer
   Kristi Lin-Rahardja, Davis T. Weaver, Jessica A. Scarborough, Jacob G. Scott

2. A modular fuzzy expert system for chemotherapy drug dose scheduling
   Rahat Hossain Faisal, Sajal Debnath, Md. Minhaj Ul Islam, Silvia Sifath, Salma Akter Kakon, Md. Shafiul Alam, Nazmul Siddique

3. Competition between chemoattractants causes unexpected complexity and can explain negative chemotaxis
   Adam Dowdell, Peggy I. Paschke, Peter A. Thomason, Luke Tweedy, Robert H. Insall

4. Make science disruptive again
   Itai Yanai & Martin J. Lercher

5. Clonal architecture evolution in Myeloproliferative Neoplasms: from a driver mutation to a complex heterogeneous mutational and phenotypic landscape
   Nabih Maslah, Lina Benajiba, Stephane Giraudier, Jean-Jacques Kiladjian & Bruno Cassinat

6. Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems
   Sara Hamis, Panu Somervuo, J. Arvid Ågren, Dagim Shiferaw Tadele, Juha Kesseli, Jacob G. Scott, Matti Nykter, Philip Gerlee, Dmitri Finkelshtein & Otso Ovaskainen

7. Clonal interactions in cancer: integrating quantitative models with experimental and clinical data
   Nathan D. Lee, Kamran Kaveh and Ivana Bozic

8. Julia for biologists
   Elisabeth Roesch, Joe G. Greener, Adam L. MacLean, Huda Nassar, Christopher Rackauckas, Timothy E. Holy & Michael P. H. Stumpf

9. Towards a systems-level probing of tumor clonality
   Emanuelle I. Grody, Ajay Abraham, Vipul Shukla, Yogesh Goyal

1. Scale-free correlations and criticality in an experimental model of brain cancer
   Kevin B. Wood, Andrea Comba, Sebastien Motsch, Tomás S. Grigera, Pedro Lowenstein

2. Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance
   Davis T. Weaver, Jeff Maltas, Jacob G. Scott

3. Resistance Management for Cancer: Lessons from Farmers
   Sareh Seyedi, Valerie K. Harris, Stefania E. Kapsetaki, Daniel Saha, …, Dawn H. Gouge, Luis Cisneros, Peter C. Ellsworth, Carlo C. Maley

4. Learning the functional landscape of microbial communities
   Abigail Skwara, Karna Gowda, Mahmoud Yousef, Juan Diaz-Colunga, Arjun S. Raman, Alvaro Sanchez, Mikhail Tikhonov, Seppe Kuehn

1. Turing on Turing
   VisualPDE: “VisualPDE is a collection of online tools designed to bring the world of partial differential equations to a wider audience through the use of real-time interactive web-based simulations usable on almost any device. The current website and tools are written and maintained by Benjamin Walker, Adam Townsend, and Andrew Krause.”

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: Neuroblastoma arises in early fetal development and its evolutionary duration predicts outcome, in Nature Genetics.

Artist: Verena Körber

Caption: “When cancer is diagnosed, the evolutionary processes shaping its genetic makeup have been active for an unknown time span, posing a formidable challenge for understanding the dynamics of tumor initiation and growth. In our recent paper, we combined deep whole genome sequencing with mathematical modeling to retrospectively infer when and how neuroblastoma, the most frequent solid tumor in infants with very diverse clinical outcome, arises. Using neutral mutation accumulation as a molecular clock, our analysis revealed that the first oncogenic mutations occurred in a transient population of neuroblasts within the first trimester of pregnancy, irrespective of clinical subtype. However, while low-risk tumors immediately commenced growing, high-risk tumors showed prolonged evolution at tumor initiation and started to expand only after telomere-maintenance mechanisms had been positively selected in a pre-malignant cell population. We propose that the clonal mutation burden, which scales with the duration of initial evolution and is hence higher in high-risk than in low-risk tumors, may be an accurate predictor of clinical outcome.”

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

• Junior professor; tenure track (University of Montpellier) in AI and multiscale modeling in biology and bio-medicine

• Research Fellow - Computational Radiation Biology and Oncology (Jamie Dean, UCL Department of Medical Physics and Biomedical Engineering)

2. Conferences / Meetings

3. Special issues

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1. H/T Andrew Krause (https://empathicdynamics.wordpress.com/2019/07/25/quotes-about-mathematical-modelling/)