This week in MathOnco 202
Optimizing adaptive therapy, perturbation, analysis, evolution of resistance, intermittent therapy, and more.
“This week in Mathematical Oncology” — Mar. 24, 2022
From the editor:
Lots of interesting publications from the field this week: optimizing adaptive therapy, perturbation, analysis, evolution of resistance, intermittent therapy, and more.
Gattaca: Base pair resolution mutation tracking for somatic evolution studies using agent-based models
Ryan O Schenck, Gabriel Brosula, Jeffrey West, Simon Leedham, Darryl Shibata, Alexander RA Anderson
Identifying Optimal Adaptive Therapeutic Schedules for Prostate Cancer through Combining Mathematical Modeling and Dynamic Optimization
Ruiyang Liu, Shun Wang, Xuewen Tan, Xiufen Zou
Intermittent treatment of BRAFV600E melanoma cells delays resistance by adaptive resensitization to drug rechallenge
Andrew J. Kavran, Scott A. Stuart, Kristyn R. Hayashi, Joel M. Basken, Barbara J. Brandhuber, Natalie G. Ahn
Anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance
Anyue Yin, Johan G. C. van Hasselt, Henk-Jan Guchelaar, Lena E. Friberg, Dirk Jan A. R. Moes
Adaptive Evolution: How Bacteria and Cancer Cells Survive Stressful Conditions and Drug Treatment
Mariangela Russo, Alberto Sogari, Alberto Bardelli
Acid-Base Homeostasis and Implications to the Phenotypic Behaviors of Cancer
Yi Zhou, Wennan Chang, Xiaoyu Lu, Jin Wang, Chi Zhang, Ying Xu
Does deterministic coexistence theory matter in a finite world?
Sebastian J. Schreiber, Jonathan M. Levine, Oscar Godoy, Nathan J.B. Kraft, Simon P. Hart
On optimal temozolomide scheduling for slowly growing gliomas
Berta Segura-Collar, Juan Jiménez-Sánchez, Ricardo Gargini, Miodrag Dragoj, Juan M. Sepúlveda, Milica Pešić, Pilar Sánchez-Gómez, Víctor M. Pérez-García
Invasiveness of Cancer Populations in a Two-dimensional Percolation cluster: a Stochastic Mathematical Approach
Renlong Yang, Yuanzhi Shao, Chongming Jiang
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.
Caption: This artwork is a representation of our recent manuscript published in Molecular Biology and Evolution illustrating our new base pair resolution in silico genome tool for agent-based modeling, Gattaca. Gattaca is named after the four bases, Adenine, Cytosine, Guanine, and Thymine. The artwork represents this by depicting a Clustal multiple sequence alignment of sequences generated from randomly sampling nucleotides from the tumor suppressor gene, TP53. This gene will almost always be used as a gene of interest in the set of genes that Gattaca would use during simulations. We insert the bases GATTACA at a random site within these generated sequences to ensure sequence identity for this motif prior to alignment. The colors pay homage to sequence alignment visualizers that always use a specific color pattern when visualizing sequence data in a GUI. Although fun to visualize, Gattaca serves as a novel and powerful tool that provides us with the ability to directly compare mechanistic agent-based model genomes with patient sequence data.
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