#MathOnco Issue 51: Moran models, genetic instability in immune surveillance, hybrid modeling, antibiotic resistance
This week in
Mathematical Oncology
Jan. 24, 2019 ~ Issue 51
From the editor
Hello #MathOnco friends,
This week's issue contains math oncology publications on Moran models, genetic instability in immune surveillance, hybrid modeling, and more. I've also included a neat publication and an interesting preprint on the topic of collateral sensitivity in antibiotic resistance.
Please enjoy!
-Jeffrey West
#MathOnco Publications
Environmental fitness heterogeneity in the Moran process
Authors: Kamran Kaveh , Alex McAvoy and Martin A. Nowak
Antibiotic collateral sensitivity is contingent on the repeatability of evolution
Authors: Daniel Nichol, Joseph Rutter, Christopher Bryant, Andrea M. Hujer, Sai Lek, Mark D. Adams, Peter Jeavons, Alexander R. A. Anderson, Robert A. Bonomo & Jacob G. Scott
Bacterial persistence promotes the evolution of antibiotic resistance by increasing survival and mutation rates
Authors: Etthel Martha Windels, Joran Elie Michiels, Maarten Fauvart, Tom Wenseleers, Bram Van den Bergh, Jan Michiels
#MathOnco Preprints
Genetic instability as a driver for immune surveillance
Authors: Guim Aguade, Ricard Sole
Mathematical Models for the Influence of Cytarabine on White Blood Cell Dynamics in Acute Myeloid Leukemia
Authors: Felix Jost, Enrico Schalk, Kristine Rinke, Thomas Fischer, Sebastian Sager
Hybrid Modeling in Oncology: successes, challenges and hopes
Authors: Angelique Stephanou, Pascal Ballet, Gibin Powathil
Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance
Authors: Jeff Maltas, Kevin B Wood
#MathOnco News
Fibre
Many of us utilize ODEs or PDEs to build mathematical oncology models. Fibre may be a good way to visualize trajectories in model space.
Fibre is a WebGL application for visualizing and coding 3d vector fields and dynamical systems. In three (or more) dimensions, a non-linear continuous dynamical system may exhibit chaos, which is characterised by sensitive dependence of the solution trajectories to the initial conditions, and evolution governed by a so-called "strange attractor".
#MathOnco - Book of the month
Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Examples, and Analysis
Andreas Deutsch & Sabine Dormann:
"This text explores the use of cellular automata in modeling pattern formation in biological systems. It describes several mathematical modeling approaches utilizing cellular automata that can be used to study the dynamics of interacting cell systems both in simulation and in practice."
Most clicked links of December
Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory
A mathematical framework for modelling the metastatic spread of cancer
Jobs
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