#MathOnco Issue 100: one hundredth issue!
This week in
Math Oncology
Jan. 30, 2019 ~ Issue 100
From the editor
Happy 100th issue!
I first came up with the idea for this newsletter about two years ago, during the ISEEC conference in Arizona. I had just joined Moffitt as a postdoc on Sandy Anderson's team. I am very thankful that Sandy has encouraged me to write this newsletter every week. He sets an example of service to the math oncology community which I endeavor to follow!
A few random thoughts on the past 100 issues:
Even within the last two years, the pace of research output is increasing. These newsletters are getting fatter!
Many have asked how I find articles. My sources are primarily Twitter, the Arxiv/Biorxiv RSS feeds, and direct email from subscribers. Please continue to email me your publications!
Anecdotally, review articles (or articles with review-like titles) tend to be more popular (i.e. more clicks!).
My hope is that the newsletter has a kind of 'domino effect.' The newsletter showcases the current advancements and limitations in mathematical oncology, which helps to define the potential impact of this field. As we slowly converge on a unified vision for math oncology, we are no doubt becoming known as a definitive subfield of oncology.
So, thank you all for supporting this newsletter. It's your research that helps to inspire more collaborations, better science, and progress toward the cure, prevention, and management of cancer.
Onward!
-Jeffrey West
#MathOnco Publications
Mitigating temozolomide resistance in glioblastoma via DNA damage-repair inhibition
Authors: Inmaculada C. Sorribes, Samuel K. Handelman and Harsh V. Jain
The Promising Connection Between Data Science and Evolutionary Theory in Oncology
Authors: Jonathan R. Goodman and Hutan Ashrafian
The Dynamic Shift Detector: An algorithm to identify changes in parameter values governing populations
Authors: Christie A. Bahlai, Elise F. Zipkin
A size and space structured model describing interactions of tumor cells with immune cells reveals cancer persistent equilibrium states in tumorigenesis
Authors: Kevin Atsou, Fabienne Anjuère, Véronique Braud, Thierry Goudon
Close Encounters of the Cell Kind: The Impact of Contact Inhibition on Tumour Growth and Cancer Models
Authors: David Robert Grimes, Alexander G. Fletcher
An in silico hybrid continuum-/agent-based procedure to modelling cancer development: interrogating the interplay amongst glioma invasion, vascularity and necrosis
Authors: Jean de Montigny, Alexandros Iosif, Lukas Breitwieser, Marco Manca, Roman Bauer, Vasileios Vavourakis
#MathOnco Preprints
Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction
Authors: Marco Tognetti, Attila Gabor, Mi Yang, Valentina Cappelletti, .., Andreas Beyer, Paola Picotti, Julio Saez-Rodriguez, Bernd Bodenmiller
Post-prediction Inference
Authors: Siruo Wang, Tyler H. McCormick, Jeffrey T. Leek
A unified simulation model for understanding the diversity of cancer evolution
Authors: Atsushi Niida, Takanori Hasegawa, Hideki Innan, Tatsuhiro Shibata, Koshi Mimori, Satoru Miyano
Philip K. Maini's 60th Birthday Meeting Opening Remarks
The Mathematical Oncology Blog
Santiago Schnell: "These are Santiago Schnell's notes for the opening remarks for Philip Maini’s 60th birthday Workshop held at the University of Oxford on September 18-19th, 2019. The meeting was entitled “On growth and pattern formation. A celebration of Philip Maini's 60th birthday”. Organising committee was Ruth Baker (University of Oxford), Derek Moulton (University of Oxford), Helen Byrne (University of Oxford), Santiago Schnell (University of Michigan) and Mark Chaplain (University of St Andrews). These notes were not in true sense what was exactly said during the opening remarks, but they were specifically written for the occasion and edited with references for publication."
Folding our knowledge in with the data—where systems biology could be headed
Aaron Meyer: "However, while very high parameter models have given us accurate predictions, especially with ever-growing training data, these models are poor at extrapolation and interpretation. These two properties are especially critical to understanding biological systems; measurements are essentially always data starved, and the complexities of biological systems are such that even our highest-throughput experiments do not comprehensively sample every possible intervention we could make. In other words, we can take lots of pictures of stop signs to teach a model to identify stop signs, but we almost always build models of cells to predict things we can’t or haven’t ever measured yet. We have to be able to see into the uncharted territory. Can your model identify stop signs if it had never seen one before?"
#MathOnco - Book of the month
Antifragile: Things That Gain from Disorder
Nassim Nicholas Taleb: "Just as human bones get stronger when subjected to stress and tension, and rumors or riots intensify when someone tries to repress them, many things in life benefit from stress, disorder, volatility, and turmoil. What Taleb has identified and calls “antifragile” is that category of things that not only gain from chaos but need it in order to survive and flourish. The antifragile is beyond the resilient or robust. The resilient resists shocks and stays the same; the antifragile gets better and better. Furthermore, the antifragile is immune to prediction errors and protected from adverse events."
Most clicked links of December
Tumor diversity and the trade-off between universal cancer tasks
Opportunities for improving cancer treatment using systems biology
Inferring growth and genetic evolution of tumors from genome sequences
Jobs
Postdoctoral Fellow in Mathematical Oncology (Russell Rockne)
PhD in Dynamic interplay of cell shape and tumour evolution (Fabian Spill)
PhD in Dynamics of Mitochondria in Health and Disease (Fabian Spill)
PhD in Mathematical Modelling of Cancer-Cell Transmigration Through Blood Vessels (Fabian Spill)
Postdoc: University of Birmingham - Institute of Metabolism and Systems Research
Pre-leukemic Dynamics – MSc or PhD Studentship (Morgan Craig)
Quantitative Systems Pharmacology (QSP) Modeler - Cell Therapy (Dean Bottino)
Math/statistical models of stem cell lineage dynamics and cancer genomics - Postdoc (Adam MacLean)
Postdoctoral Research Position in Computational Oncology (Tom Yankeelov)
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