This week in MathOnco 242
“This week in Mathematical Oncology” — Feb. 9, 2023
From the editor:
Today we feature articles on cell fate, spatial transcriptomics, glucose metabolism, model identifiability, and more.
“If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is."
- John von Neumann
A cell fate reprogramming strategy reverses epithelial-to-mesenchymal transition of lung cancer cells while avoiding hybrid states
Namhee Kim, Chae Young Hwang, Taeyoung Kim, Hyunjin Kim, Kwang-Hyun Cho
Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST
Wei Liu, Xu Liao, Ziye Luo, Yi Yang, Mai Chan Lau, Yuling Jiao, Xingjie Shi, Weiwei Zhai, Hongkai Ji, Joe Yeong, Jin Liu
Hybrid computational models of multicellular tumour growth considering glucose metabolism
Inês G. Gonçalves, Jose Manuel García-Aznar
Practical Understanding of Cancer Model Identifiability in Clinical Applications
Tin Phan, Justin Bennett, Taylor Patten
A reduced model of cell metabolism to revisit the glycolysis-OXPHOS relationship in the deregulated tumor microenvironment
Pierre Jacquet, Angélique Stéphanou
Computational modeling of DLBCL predicts response to BH3-mimetics
Ielyaas Cloete, Victoria M Smith, Ross A Jackson, Andrea Pepper, Chris Pepper, Meike Vogler, Martin JS Dyer, Simon Mitchell
Modeling of mouse experiments suggests that optimal anti-hormonal treatment for breast cancer is diet-dependent
Tuğba Akman, Lisa M. Arendt, Jürgen Geisler, Vessela N. Kristensen, Arnoldo Frigessi, Alvaro Köhn-Luque
Theoretical understanding of evolutionary dynamics on inhomogeneous networks
Hamid Teimouri, Dorsa Sattari Khavas, Cade Spaulding, Christopher Li, Anatoly B. Kolomeisky
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. This week’s artwork:
Based on the paper: “Spatially aware dimension reduction for spatial transcriptomics” in Nature Communications
Artist: Lulu Shang (@shang_lulu)
Caption: "Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. We developed a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. Our results demonstrate the benefits of SpatialPCA for detecting spatial domains and inferring tissue trajectories, and constructing high-resolution spatial maps."
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Postdoctoral Fellow - Computational Biology, Computational Chemistry, Bioinformatics (Nagarajan Vaidehi, Andrei S. Rodin, and Sergio Branciamore, City of Hope)
Postdoctoral Fellow (Ilya Shmulevich, Institute for Systems Biology, Seattle)
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H/T @_Alex_Zeilmann_ - thanks for the quote