This week in MathOnco 212
Glioblastoma network complexity, deep learning, adaptive therapy, cancer drivers, cancer screening, and more...
“This week in Mathematical Oncology” — June 2, 2022
From the editor:
Today we feature glioblastoma network complexity, deep learning, adaptive therapy, cancer drivers, cancer screening, and more...
PS. Have you considered submitting a blog post to The Mathematical Oncology Blog? Here’s your inspiration:
"When you’ve written the same code 3 times, write a function. When you’ve given the same in-person advice 3 times, write a blog post."
Algorithmic reconstruction of glioblastoma network complexity
Abicumaran Uthamacumaran, Morgan Craig
A 1D–0D–3D coupled model for simulating blood flow and transport processes in breast tissue
Marvin Fritz, Tobias Köppl, John Tinsley Oden, Andreas Wagner, Barbara Wohlmuth, Chengyue Wu
Cancer driver drug interaction explorer
Michael Hartung, Elisa Anastasi, Zeinab M Mamdouh, Cristian Nogales, Harald H H W Schmidt, Jan Baumbach, Olga Zolotareva, Markus List
In Silico Investigations of Multi-Drug Adaptive Therapy Protocols
Daniel S. Thomas, Luis H. Cisneros, Alexander R. A. Anderson, Carlo C. Maley
Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics
Grace Avecilla, Julie N. Chuong, Fangfei Li, Gavin Sherlock, David Gresham, Yoav Ram
Designing optimal allocations for cancer screening using queuing network models
Justin Dean, Evan Goldberg, Franziska Michor
Mechanism of treatment-free remission in patients with chronic myeloid leukemia revealed by a computational model of CML evolution
Xiulan Lai, Xiaopei Jiao, Haojian Zhang, Jinzhi Lei
An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors
Xinghua Lu, Xueer Chen, Lujia Chen, Cornelius H.L. Kürten, …, Robert Lafyatis, Gregory Cooper, Robert Ferris, Xinghua Lu
Universality of evolutionary trajectories under arbitrary competition dynamics
Andrea Mazzolini, Jacopo Grilli
Nothing makes sense in deep learning, except in the light of evolution
Artem Kaznatcheev, Konrad Paul Kording
Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer
Yitao Lu, Qian Chu, Zhen Li, Mengdi Wang, Qingpeng Zhang
The newsletter now has a dedicated homepage where we post the cover artwork for each issue. We encourage submissions that coincide with the release of a recent paper from your group.
Caption: “Glioblastoma is a complex disease that is difficult to treat. In our study, we applied a host of data theoretic techniques to gene expression patterns from pediatric and adult glioblastoma, and adult glioma-derived stem cells (GSCs) to identify the key molecular regulators of the networks driving glioblastoma/GSCs and predict their cell fate dynamics along differentiation landscapes illustrated here. Our results provide strong evidence of complex systems approaches for inferring complex dynamics by reverse-engineering gene networks. In the figure, we see a visualization of the Waddington landscape with more or less differentiated cells. Identifying the programs regulating this differentiation will bolster the search for new clinically relevant targets in glioblastoma and other cancers.“
Created by: Jesse Morris (www.jessemorrisart.com)
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