#MathOnco Issue 83: genetic heterogeneity, designing therapy using machine learning, deleterious mutations, and oxygenation heterogeneity
This week in
Math Oncology
Sept. 19, 2019 ~ Issue 83
From the editor
#MathOnco friends,
This week in math oncology includes topics like genetic heterogeneity of drug response, designing therapy using machine learning, deleterious mutations, and oxygenation heterogeneity.
As a reminder, you can always view old issues of this newsletter online, here.
Please enjoy!
-Jeffrey West
#MathOnco Publications
Systems biology approaches to measure and model phenotypic heterogeneity in cancer
Authors: Aaron S. Meyer, Laura M. Heiser
Modeling genetic heterogeneity of drug response and resistance in cancer
Authors: Teemu D. Laajala, Travis Gerke, Svitlana Tyekucheva, James C. Costello
Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors
Authors: Yuanyuan Qi, Dikshant Pradhan, Mohammed El-Kebir
Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2+ breast cancer
Authors: Angela M. Jarrett, Alay Shah, Meghan J. Bloom, Matthew T. McKenna, David A. Hormuth II, Thomas E. Yankeelov & Anna G. Sorace
Optimization of Cancer Treatment in the Frequency Domain
Authors: Pascal Schulthess, Vivi Rottschäfer, James W. T. Yates, Piet H. van der Graaf
Designing combination therapies with modeling chaperoned machine learning
Authors: Yin Zhang, Julie M. Huynh, Guan-Sheng Liu, Richard Ballweg, Kayenat S. Aryeh, Andrew L. Paek, Tongli Zhang
#MathOnco Preprints
Abnormal morphology biases haematocrit distribution in tumour vasculature and contributes to heterogeneity in tissue oxygenation
Authors: Miguel O. Bernabeu, Jakub Köry, James A. Grogan, Bostjan Markelc, Albert Beardo Ricol, Mayeul d’Avezac, Jakob Kaeppler, Nicholas Daly, James Hetherington, Timm Krüger, Philip K. Maini, Joe M. Pitt-Francis, Ruth J. Muschel, Tomás Alarcón, Helen M. Byrne
Most cancers carry a substantial deleterious load due to Hill-Robertson interference
Authors: Susanne Tilk, Christina Curtis, Dmitri Petrov, Christopher Dennis McFarland
Trade-offs between causes of mortality in life history evolution: the case of cancers
Authors: Samuel Pavard, Cje Metcalf
How the body’s nerves become accomplices in the spread of cancer
Kelly Servick: "Ayala hadn't planned to do research full time, but a little-explored feature of cancer enticed him: the tendency of some cancer cells to wrap around nerves and grow along them. He had seen that "perineural invasion" in cancer patients and knew it often signaled an aggressive tumor and a poor prognosis. "But nobody knew how it happened," Ayala says. "There was no biology."
#MathOnco - Book of the month
An Editor's Guide to Writing and Publishing Science
Michael Hochberg: "Publishing is rapidly changing, and needs to be explained with a fresh perspective. Simply writing good, clear, concise, science is no longer enough-there is a different mind-set now required that students need to adopt if they are to succeed. The purpose of this book is to provide the foundations of this new approach for both young scientists at the start of their careers, as well as for more experienced scientists to teach the younger generation."
Most clicked links of August
A short comment on statistical versus mathematical modelling
Mathematical Models of Cancer: When to Predict Novel Therapies, and When Not to
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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