This week in MathOnco 214

Agent-based models, spatial structure, cellular hierarchy, evolutionary unpredictability

“This week in Mathematical Oncology” — June 16, 2022

> mathematical-oncology.org

From the editor:

Today we feature articles on agent-based models (and an accompanying blog post on The Mathematical Oncology blog), spatial structure, cellular hierarchy, evolutionary unpredictability. Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

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"These are men with bold ideas, but highly critical of their own ideas; they try to find whether their ideas are right by trying first to find whether they are not perhaps wrong. They work with bold conjectures and severe attempts at refuting their own conjectures."
- K. Popper

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1. A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues
   Daniel Bergman, Randy F. Sweis, Alexander T. Pearson, Fereshteh Nazari, Trachette L. Jackson

2. Quantitative models for the inference of intratumor heterogeneity
   Tom van den Bosch, Louis Vermeulen, Daniël M. Miedema

3. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia
   Andy G. X. Zeng, Suraj Bansal, Liqing Jin, Amanda Mitchell, …, Mark D. Minden, James A. Kennedy, Jean C. Y. Wang, John E. Dick

4. Clonal dynamics of haematopoiesis across the human lifespan
   Emily Mitchell, Michael Spencer Chapman, Nicholas Williams, Kevin J. Dawson, …, Anthony R. Green, Jyoti Nangalia, Elisa Laurenti, Peter J. Campbell

5. Spatial dynamics of feedback and feedforward regulation in cell lineages
   Peter Uhl, John Lowengrub, Natalia Komarova, Dominik Wodarz

1. Spatial structure alters the site frequency spectrum produced by hitchhiking
   Jiseon Min, Misha Gupta, Michael M. Desai, Daniel Weissman

2. Deep Reinforcement Learning for Optimal Experimental Design in Biology
   Neythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes

3. Inherent evolutionary unpredictability in cancer model system
   Subhayan Chattopadhyay, Jenny Karlsson, Adriana Mañas, Ryu Kanzaki, …, Sofie Mohlin, Kristian Pietras, Daniel Bexell, David Gisselsson

1. Fast simulations of molecular dynamics: multiscale agent-based models of biological tissues

   The Mathematical Oncology Blog
   Daniel Bergman: “In the world of mathematical oncology, agent-based models (ABMs) continue to prove themselves a useful tool for a wide range of applications. They can be used to understand questions in diverse areas of oncology such as tumorigenesis, angiogenesis, progression, phenotypic plasticity, and therapeutic design. As the field continues to develop and more complex models are built to address more specific, involved questions, the molecular scale will become more and more ubiquitous as a natural pairing with the cellular scale the agents inhabit. In fact, most software tailored to building ABMs in this field assume that the typical end user will include one or more molecules in their ABM.”

2. Moffitt’s Robert Gillies, “father of radiomics,” dies at 69
   The Cancer Letter
   Kim Polacek: ”Moffitt Cancer Center and the global research community have lost a great leader, scientist, and collaborator. Dr. Robert J. Gillies died June 7 after an extended illness. His recruitment in 2008 and the contributions to science he made over the ensuing 14 years elevated Moffitt’s scientific stature. He was 69.”

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: “Agent-based models of biological tissue are computationally expensive. Many models that include molecular dynamics, and intracellular signaling in particular, spend much of their simulation time solving these dynamics. We developed and analyzed an approach to simulating ABMs that aims to reduce this time by coarse-graining these dynamics. This global method for simulating molecular dynamics is 1-2 orders of magnitude faster and produces very similar results to the fine-grained, local method. Learn more about the method, how well it performs, and some future directions in our recent publication here. Check out our blog post for a bit more on how we came up with the method and where we're going.“

Created by: Daniel Bergman (@dbergman78)

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