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

> mathematical-oncology.org

From the editor:

Today we feature glioblastoma network complexity, deep learning, adaptive therapy, cancer drivers, cancer screening, and more...

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

PS. Have you considered submitting a blog post to The Mathematical Oncology Blog? Here’s your inspiration:

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"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."
-D. Robinson

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1. Algorithmic reconstruction of glioblastoma network complexity
   Abicumaran Uthamacumaran, Morgan Craig

2. 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

3. 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

4. In Silico Investigations of Multi-Drug Adaptive Therapy Protocols
   Daniel S. Thomas, Luis H. Cisneros, Alexander R. A. Anderson, Carlo C. Maley

5. 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

6. Designing optimal allocations for cancer screening using queuing network models
   Justin Dean, Evan Goldberg, Franziska Michor

1. 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

2. 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

3. Universality of evolutionary trajectories under arbitrary competition dynamics
   Andrea Mazzolini, Jacopo Grilli

4. Nothing makes sense in deep learning, except in the light of evolution
   Artem Kaznatcheev, Konrad Paul Kording

5. 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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