This Week in #MathOnco
This week in
Mathematical Oncology
February 15, 2018 ~ Issue 8
From the editor
Welcome to another week of the #MathOnco newsletter. A special thanks to those of you who have shared with me exciting new publications for this week's edition (and for the rest of you, don't be afraid to use the submit button below to draw my attention to something I've missed).
Today's issue includes topics such as EMT tristability, combinatorial breast cancer treatments, and a new drug response metric.
Last week's issue: here.
Enjoy,
-Jeffrey West
#MathOnco Publications
Heritable tumor cell division rate heterogeneity induces clonal dominance
Authors: Margriet m. Palm, Marjet Elemans, Joost B. Beltman
Distinguishing mechanism underlying EMT tristability
Authors: Dongya Jia, Mohit Kumar Jolly, Satyendra C. Tripathi, ..., Herbert Levine
A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer
Authors: Jorge Gomez Tejeda Zanudo, Maurizio Scaltriti, Reka Albert
#MathOnco Preprints
A normalized drug response metric improves accuracy and consistency of drug sensitivity quantification in cell-based screening
Authors: Anhishekh Gupta, Prson Gautam, Krister Wennerberg, Tero Aittokallio
High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow
Authors: Jonathan Ozik, Nicholson Collier, Justin Wozniak, ..., Paul Macklin
Computational model of chimeric antigen receptors explains site-specific phosphorylation kinetics
Authors: Jennifer A Rohrs, Dongqing Zheng, Nicholas A Graham, Pin Wang, Stacey Finley
Transition state characteristics during cell differentiation
Authors: Rowan Brackston, Eszter Lakatos, Michael P H Stumpf
Extended logistic growth model for heterogeneous populations
Authors: Wang Jin, Scott McCue, Matthew Simpson
Markov chain models of cancer metastasis
Authors: Jeremy Mason, Paul K Newton
#MathOnco Books
Introduction to Mathematical Oncology
The name says it all: the book "presents biologically well-motivated and mathematically tractable models that facilitate both a deep understanding of cancer biology and better cancer treatment designs. It covers the medical and biological background of the diseases, modeling issues, and existing methods and their limitations."
Do you see something we missed? Click the submit button below to send us an idea for next week's issue.
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