#MathOnco Issue 30: evolutionary dynamics of hemoglobinuria, chromosomal instability, RNA velocity of single cells, cancer hallmark acquisition
This week in
Mathematical Oncology
Aug. 9, 2018 ~ Issue 30
From the editor
Hello all,
I hope that you're enjoying the summer -- it's fast approaching it's end! If not, consider some math oncology articles to brighten your day, including evolutionary dynamics model of hemoglobinuria, chromosomal instability and heterogeneity, RNA velocity of single cells, and an interesting model of cancer hallmark acquisition!
Enjoy,
-Jeffrey West
#MathOnco Publications
Evolutionary dynamics of paroxysmal nocturnal hemoglobinuria
Authors: Nathaniel Mon Père, Tom Lenaerts, Jorge M. Pacheco, David Dingli
Long-term stability and computational analysis of migration patterns of L-MYC immortalized neural stem cells in the brain
Authors: Russell C. Rockne , Vikram Adhikarla , Lusine Tsaturyan, Zhongqi Li, Meher B. Masihi, Karen S. Aboody, Michael E. Barish, Margarita Gutova
Extracellular control of chromosomal instability and maintenance of intra-tumoral heterogeneity
Authors: Yi-Hong Zhou, Kambiz Afrasiabi, Mark E. Linskey
RNA velocity of single cells
Authors: Gioele La Manno, Ruslan Soldatov, Amit Zeisel, ..., Peter V. Kharchenko
The Importance of Spatial Randomness in the Evolutionary Dynamics of Mutants
Authors: Mohammad Kohandel and Natalia L. Komarova
#MathOnco Preprints
Tumor subclonal progression model for cancer hallmark acquisition
Authors: Yusuke Matsui, Satoru Miyano, Teppei Shimamura
A stochastic model of metastatic bottleneck predicts patient outcome and therapy response
Authors: Ewa Szczurek, Tyll Krueger, Barbara Klink, Niko Beerenwinkel
Inferring rates of metastatic dissemination using stochastic network models
Authors: Philip Gerlee, Mia Johansson
Maximum entropy framework for inference of cell population heterogeneity in signaling network dynamics
Authors: Purushottam Dixit, Eugenia Lyashenko, Mario Niepel, Dennis Vitkup
#MathOnco News
Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes
Kristoffer Magnusson: "In this post, I will deal with linear mixed-effects models (LMM) that use a log-transformed outcome variable. This will be the first part of a three-part tutorial on some of the finer details of (G)LMMs, and how Bayes can make your (frequentist) life easier."
#MathOnco - Book of the month
Ecology and Evolution of Cancer
B. Ujvari, B. Roche, F. Thomas: "Cancer is now generally accepted to be an evolutionary and ecological process with complex interactions between tumor cells and their environment sharing many similarities with organismal evolution. This work engages the expertise of a multidisciplinary research team to collate and review the latest knowledge and developments in this exciting research field."
#MathOnco - Best of last month
Most clicked links of July
Cancer-causing somatic mutations: they are neither necessary nor sufficient
Eco-evolutionary causes and consequences of temporal changes in intratumoural blood flow
Mechanistic models versus machine learning, a fight worth fighting for the biological community?
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