#MathOnco Issue 118: optimal control, cancer invasion, growth laws, a heterogeneity unifying framework, and mechanism vs machine learning.
This week in
Math Oncology
June 11, 2020 ~ Issue 118
From the editor
Dear Readers,
This week's edition includes articles on optimal control, cancer invasion, growth laws, a heterogeneity unifying framework, and mechanism vs machine learning. This week's issue is a bit more packed than usual, but I trust it will prove useful!
A new month means a new book recommendation: check out "The Cheating Cell."
Enjoy,
-Jeffrey West
#MathOnco Publications
The initial engraftment of tumor cells is critical for the future growth pattern: a mathematical study based on simulations and animal experiments
Authors: Bertin Hoffmann, Tobias Lange, Vera Labitzky, Kristoffer Riecken, Andreas Wree, Udo Schumacher, Gero Wedemann
An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments
Authors: Fabrizio Angaroni, Alex Graudenzi, Marco Rossignolo, Davide Maspero, Tommaso Calarco, Rocco Piazza, Simone Montangero, Marco Antoniotti
Cell-Scale Degradation of Peritumoural Extracellular Matrix Fibre Network and Its Role Within Tissue-Scale Cancer Invasion
Authors: Robyn Shuttleworth, Dumitru Trucu
Sustained Coevolution in a Stochastic Model of Cancer–Immune Interaction
Authors: Jason T. George, Herbert Levine
Multi-objective optimization of tumor response to drug release from vasculature-bound nanoparticles
Authors: Ibrahim M. Chamseddine, Hermann B. Frieboes, Michael Kokkolaras
Convergent Evolution, Evolving Evolvability, and the Origins of Lethal Cancer
Authors: Kenneth J. Pienta, Emma U. Hammarlund, Robert Axelrod, Sarah R. Amend, Joel S. Brown
The interface between ecology, evolution and cancer: more than ever a relevant research direction for both oncologists and ecologists
Authors: Frédéric Thomas, Benjamin Roche, Mathieu Giraudeau, Rodrigo Hamede, Beata Ujvari
Comparison of Drug Inhibitory Effects (IC50) in Monolayer and Spheroid Cultures
Authors: Catherine Berrouet, Naika Dorilas, Katarzyna A. Rejniak, Necibe Tuncer
Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring
Authors: Asaf Zviran, Rafael C. Schulman, Minita Shah, Steven T. K. Hill, ..., Genevieve Boland, Nicolas Robine, Nasser K. Altorki, Dan A. Landau
#MathOnco Preprints
Competition delays multi-drug resistance evolution during combination therapy
Authors: Ernesto Berríos-Caro, Danna R. Gifford, Tobias Galla
Understanding the Role of Macrophages in Lung Inflammation Through Mathematical Modeling
Authors: Sarah B. Minucci, Rebecca L. Heise, Michael S. Valentine, Franck J. Kamga Gninzeko, Angela M. Reynolds
A unifying framework disentangles genetic, epigenetic, and stochastic sources of drug-response variability in an in vitro model of tumor heterogeneity
Authors: Corey E. Hayford, Darren R. Tyson, C. Jack Robbins III, Peter L. Frick, Vito Quaranta, Leonard A. Harris
Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer
Authors: Cristian Axenie, Daria Kurz
Penrose: from mathematical notation to beautiful diagrams
Katherine Ye, Wode Ni, Max Krieger, Dor Ma'ayan, Jenna Wise, Jonathan Aldrich, Joshua Sunshine, Keenan Crane: "We introduce a system called Penrose for creating mathematical diagrams. Its basic functionality is to translate abstract statements written in familiar math-like notation into one or more possible visual representations. Rather than rely on a fixed library of visualization tools, the visual representation is user-defined in a constraint-based specification language; diagrams are then generated automatically via constrained numerical optimization. The system is user-extensible to many domains of mathematics, and is fast enough for iterative design exploration. In contrast to tools that specify diagrams via direct manipulation or low-level graphics programming, Penrose enables rapid creation and exploration of diagrams that faithfully preserve the underlying mathematical meaning. We demonstrate the effectiveness and generality of the system by showing how it can be used to illustrate a diverse set of concepts from mathematics and computer graphics."
#MathOnco - Book of the month
The Cheating Cell
Athena Aktipis: "When we think of the forces driving cancer, we don’t necessarily think of evolution. But evolution and cancer are closely linked, for the historical processes that created life also created cancer. The Cheating Cell delves into this extraordinary relationship, and shows that by understanding cancer’s evolutionary origins, researchers can come up with more effective, revolutionary treatments."
Jobs
Mathematical modelling of cancer ecology and evolution – PhD Studentships (Rob Noble)
Research Associate, Postdoc, and Research Faculty positions – Mathematical Oncology (Russ Rockne)
Systems Biology Modeler Positions in Biopharma Consulting Company (Helen Moore)
Computational Approaches to Breast Cancer Evolution - Postdoc (Marc Ryser)
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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