This week in MathOnco 240
Cell shape, optimal control, evolutionary tumor boards, and agent-based models
“This week in Mathematical Oncology” — Jan. 26, 2023
From the editor:
Today we feature articles on cell shape, optimal control, evolutionary tumor boards, and agent-based models.
“No one trusts a model except the man who wrote it; everyone trusts an observation, except the man who made it.”
— H. Shapley
Predicting progression-free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development
Paul R Barber, Rami Mustapha, Fabian Flores-Borja, Giovanna Alfano, …, Kevin J Harrington, Martin Forster, Anthony CC Coolen, Tony Ng
Understanding cellular growth strategies via optimal control
Tommi Mononen, Teemu Kuosmanen, Johannes Cairns and Ville Mustonen
On the origin of universal cell shape variability in confluent epithelial monolayers
Souvik Sadhukhan, Saroj Kumar Nandi
Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations
Stefano Giaimo, Arne Traulsen
Modeling interaction of Glioma cells and CAR T-cells considering multiple CAR T-cells bindings
Runpeng Li, Prativa Sahoo, Dongrui Wang, Qixuan Wang, Christine E. Brown, Russell C. Rockne, Heyrim Cho
Geometric analysis enables biological insight from complex non-identifiable models using simple surrogates
Alexander P. Browning, Matthew J. Simpson
Feasibility of an Evolutionary Tumor Board for Generating Novel Personalized Therapeutic Strategies
Mark Robertson-Tessi, Joel S. Brown, Maria Poole, Matthew Johnson, …, Robert A Gatenby, Damon Reed, Alexander R. A. Anderson, Christine H. Chung
Warlock: an automated computational workflow for simulating spatially structured tumour evolution
Maciej Bak, Blair Colyer, Veselin Manojlović, Robert Noble
Synchronization induced by directed higher-order interactions
Luca Gallo, Riccardo Muolo, Lucia Valentina Gambuzza, Vito Latora, Mattia Frasca, Timoteo Carletti
Formation and growth characteristics of co-culture tumour spheroids
Ryan J. Murphy, Gency Gunasingh, Nikolas K. Haass, Matthew J. Simpson
Fitting agent-based models to tumor images using representation learning
Colin G. Cess, Stacey D. Finley
She Finds Keys to Ecology in Cells That Steal From Others
Veronique Greenwood: “The ecologist Holly Moeller studies microorganisms that expand their range by absorbing organelles and gaining new metabolic talents from their prey.”
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: “Mutation divergence over space in tumour expansion” on the bioRxiv
Artist: Haiyang Li (@oceanlee2008), Weini Huang (@huang_weini)
Caption: "Spatial divergence is an important perspective of intra-tumour heterogeneity. Much interesting work has been done to measure how mutation accumulation diverges over space. Using an agent-based model, we apply the Jaccard index to compare the portion of shared mutations between samples while varying the sampling distance and tumour expansion modes. We infer mutation rates based on the distributions of variant allele frequencies under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred in any sampling size. However, the accuracy compared to the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. Such an inference becomes unreliable when the tumour expansion is slower e.g. in surface growth."
Visit the mathematical oncology page to view jobs, meetings, and special issues. We will post new additions here, but the full list can found at mathematical-oncology.org.
NEW: Postdoctoral positions in Rejniak Lab @Moffitt Cancer Center
We are looking for enthusiastic mathematical modelers to participate as postdoctoral fellows in the NIH/NCI-funded research project in modeling breast cancer DCIS microinvasions. These positions are available in the computational lab of Dr. Kasia Rejniak @Moffitt Integrated Mathematical Oncology (IMO) Department in collaboration with cancer biology lab of Dr. Mehdi Damaghi. The knowledge of agent-based models (ABMs) is desired; the knowledge of machine learning algorithms is a plus; this project will integrate computational models with in vitro, in vivo, and ex vivo data. For informal inquires email Kasia Rejniak (Kasia.Rejniak@moffitt.org) or click here. Interested applicants should send a single pdf file that includes (1) a cover letter summarizing their research training and accomplishments, (2) a personal statement of scientific interests and goals, (3) current CV with a list of research presentations and publications, and (4) contact information for three references to Dr. Kasia Rejniak and apply online (job number 62177): Applications will be accepted until the positions are filled.
NEW: Postdoctoral Fellow in predicting changes in tissue state from complex multi-modal data (Linus Schumacher, University of Edinburgh) – 10th February, 2023
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Thanks to Juan Jiménez Sánchez for the quote!