#MathOnco Issue 86: in silico Darwinian evolution, transcriptional dynamics, genomic instability, pop genetics, and CAR-T response
This week in
Math Oncology
Oct. 10, 2019 ~ Issue 86
From the editor
Hello!
This issue contains in silico Darwinian evolution, transcriptional dynamics, genomic instability, pop genetics, CAR-T response, and more.
It seems that a few of our Math Onco friends are also interested in other types of evolutionary models as well. To that end, I've included a few non-cancer models this week, too.
Please enjoy!
-Jeffrey West
PS. This preprint may be of interest to many of you: "Insights from a survey-based analysis of the academic job market." Authors note that "many postdoctoral fellows in the STEM fields enter the academic job market with little knowledge of the process and expectations [...] demystifying this process is critical."
#MathOnco Publications
In Silico Implementation of Evolutionary Paradigm in Therapy Design: Towards Anti-Cancer Therapy as Darwinian Process
Authors: B. Brutovsky, D.Horvath
Leveraging transcriptional dynamics to improve BRAF inhibitor responses in melanoma
Authors: Inna Smalley, Eunjung Kim, Jiannong Li, Paige Spence, ..., Robert A. Gatenby, Y. Ann Chen, Alexander R.A. Anderson, Keiran S.M. Smalley
Mathematical modelling the pathway of genomic instability in lung cancer
Authors: Lingling Li, Xinan Zhang, Tianhai Tian & Liuyong Pang
Evolutionary Dynamics in Structured Populations Under Strong Population Genetic Forces
Authors: Alison F. Feder, Pleuni S. Pennings, Joachim Hermisson, Dmitri A. Petrov
The feedback between selection and demography shapes genomic diversity during coevolution
Authors: Cas Retel, Vienna Kowallik, Weini Huang, Benjamin Werner, Sven Künzel, Lutz Becks, Philine G. D. Feulner
#MathOnco Preprints
A unified simulation model for understanding the diversity of cancer evolution
Authors: Atsushi Niida, Takanori Hasegawa, Tatsuhiro Shibata, Koshi Mimori, Satoru Miyano
Range expansion shifts clonal interference patterns in evolving populations
Authors: Nikhil Krishnan, Jacob G Scott
A mathematical modeling approach to explore kinetics of Chimeric Antigen Receptor (CAR) T-cell Response in glioma: the CARRGO model
Authors: Prativa Sahoo, Xin Yang, Daniel Abler, Davide Maestrini, ..., Margarita Gutova, Sergio Branciamore, Christine E. Brown, Russell C. Rockne
PhysiCell Tools : python-loader
Paul Macklin: "The newest tool for PhysiCell provides an easy way to load your PhysiCell output data into python for analysis."
Cancer Is Still Beating Us—We Need a New Start
Azra Raza: "What we need now is a paradigm shift. Today, the newest methods generating the most research and expense tend to be focused on treating the worst cases—chasing after the last cancer cells in end-stage patients whose prognoses are the worst. We need instead to commit to anticipating, finding and destroying the first cancer cells. We must reliably detect the faint footprints of cancer at the beginning and stop it in its tracks. Such prevention represents the cheapest, fastest and safest alternative to the terrible, longstanding treatment trio of slash, poison and burn."
#MathOnco - Book of the month
The Maths of Life and Death
Kit Yates: "In this eye-opening and extraordinary book, Yates explores the true stories of life-changing events in which the application - or misapplication - of mathematics has played a critical role: patients crippled by faulty genes and entrepreneurs bankrupted by faulty algorithms; innocent victims of miscarriages of justice and the unwitting victims of software glitches. We follow stories of investors who have lost fortunes and parents who have lost children, all because of mathematical misunderstandings."
Most clicked links of September
Systems biology approaches to measure and model phenotypic heterogeneity in cancer
Modeling genetic heterogeneity of drug response and resistance in cancer
A Monte Carlo method to estimate cell population heterogeneity
Jobs
Math/statistical models of stem cell lineage dynamics and cancer genomics - Postdoc (Adam MacLean)
Data-driven modeling of breast cancer metastasis - Postdoc (Paul Macklin)
Postdoctoral Research Position in Computational Oncology (Tom Yankeelov)
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