#MathOnco Issue 23: cost of resistance, stromal macrophages & fibroblasts, evolutionary graph theory, combination immunotherapy
This week in
Mathematical Oncology
June 21, 2018 ~ Issue 23
From the editor
This week's issue is packed with exciting topics such as the cost of resistance, stromal macrophages & fibroblasts, evolutionary graph theory, combination immunotherapy, and more. Without further ado, enjoy the science reading,
-Jeffrey West
#MathOnco Publications
Three-Dimensional Spatiotemporal Modeling of Colon Cancer Organoids Reveals that Multimodal Control of Stem Cell Self-Renewal is a Critical Determinant of Size and Shape in Early Stages of Tumor Growth
Authors: Huaming Yan, Anna Konstorum, John S. Lowengrub
Modeling small cell lung cancer (SCLC) biology through deterministic and stochastic mathematical models
Authors: Ravi Salgia, Isa Mambetsariev, Blake Hewelt, Srisairam Achuthan, Haiqing Li, Valeriy Poroyko, Yingyu Wang, and Martin Sattler
Modeling triple-negative breast cancer heterogeneity: Effects of stromal macrophages, fibroblasts and tumor vasculature
Authors: Kerri-AnnNorton KideokJin Aleksander S.Popel
Construction of arbitrarily strong amplifiers of natural selection using evolutionary graph theory
Authors: Andreas Pavlogiannis, Josef Tkadlec, Krishnendu Chatterjee, Martin A. Nowak
#MathOnco Preprints
Cost of resistance: an unreasonably expensive concept
Authors: Thomas Lenormand, Noemie Harmand, Romain Gallet
Parameter estimation and treatment optimization in a stochastic model for immunotherapy of cancer
Authors: Modibo Diabate, Loren Coquille, Adeline Samson
Combination of direct methods and homotopy in numerical optimal control: application to the optimization of chemotherapy in cancer
Authors: Antoine Olivier, Camille Puchol
A mathematical formalism for natural selection with arbitrary spatial and genetic structure
Authors: Benjamin Allen, Alex McAvoy
The immune checkpoint kick start: Optimization of neoadjuvant combination therapy using game theory
Authors: Jeffrey West, Mark Robertson-Tessi, Kimberly Luddy, Derek S. Park, Drew F.K. Williamson, Cathal Harmon, Hung T. Khong, Joel Brown, Alexander R.A. Anderson
Quantifying Uncertainty and Robustness in a Biomathematical Model Based Patient-Specific Response Metric for Glioblastoma
Authors: Andrea Hawkins-Daarud, Sandra K. Johnston, Kristin R. Swanson
#MathOnco News
Her Key to Modeling Brains: Ignore the Right Details
Siobhan Roberts: "Beautiful models are simple to describe and can be mathematically analyzed. They contain important kernels of “truth” in them, even if they are not messy enough to be completely accurate."
#MathOnco - Book of the month
Adaptive Oncogenesis
James DeGregori's new cell biology book "corrects the fundamental attribution error that has focused cancer research on malignant cells and their genes. Adaptive oncogenesis, or ‘EcoOncogenesis,’ shows that the ecosystems surrounding cells are equally important, responsible for creating selection forces that speed or slow the evolution of cancer. "
#MathOnco - Best of last month
Most clicked links of May
Mathematical modeling predicts response to chemotherapy and drug combinations in ovarian cancer
A computational framework for the personalized clinical treatment of glioblastoma multiforme
Estimating intratumoral heterogeneity from spatiotemporal data
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