This week in MathOnco 306

Eco-evo dynamics, clonal hematopoiesis, complex ecosystems, and more..

“This week in Mathematical Oncology” — Sep 5, 2024

> mathematical-oncology.org

From the editor:

Greetings math oncologists — this week we have a list of publications in topics such as eco-evo dynamics, clonal hematopoiesis, complex ecosystems, and more..

Thanks,

Jeffrey West
jeffrey.west@moffitt.org

1. Accessing the pharmacokinetics of magnetic nanoparticles in cirrhosis-associated hepatocarcinogenesis by ordinary differential equation modeling and AC biosusceptometry

   Diego S. Rodrigues, Guilherme A. Soares, Verónica A. González-López, Anibal T. Bezerra, Mats Jirstrand & José R. A. Miranda

2. Ecological and evolutionary dynamics to design and improve ovarian cancer treatment
   Grace Y. Q. Han, Monica Alexander, Julia Gattozzi, Marilyn Day, Elayna Kirsch, Narges Tafreshi, Raafat Chalar, Soraya Rahni, Gabrielle Gossner, William Burke, Mehdi Damaghi

3. Clinical and Therapeutic Implications of Clonal Hematopoiesis
   Giulia Petrone, Isik Turker, Pradeep Natarajan, and Kelly L. Bolton

4. METI: deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomic
   Jiahui Jiang, Yunhe Liu, Jiangjiang Qin, Jianfeng Chen, … Xiangdong Cheng, Jaffer Ajani, Jianjun Gao, Jian Hu & Linghua Wang

5. A gene for all seasons: The evolutionary consequences of HIF-1 in carcinogenesis, tumor growth and metastasis
   Ranjini Bhattacharya, Joel S. Brown, Robert A. Gatenby, Arig Ibrahim-Hashim

6. Microenvironmental entropy dynamics analysis reveals novel insights in Notch-Delta-Jagged decision-making mechanism
   Aditi Ajith Pujar, Arnab Barua, Partha Sarathi Dey, Divyoj Singh, Ushasi Roy, Mohit Kumar Jolly, Haralampos Hatzikirou

7. Modeling tumors as complex ecosystems
   Guim Aguadé-Gorgorió, Alexander R.A. Anderson, Ricard Solé

8. Agent-Based Modeling of Virtual Tumors Reveals the Critical Influence of Microenvironmental Complexity on Immunotherapy Efficacy
   Yixuan Wang, Daniel R. Bergman, Erica Trujillo, Anthony A. Fernald, Lie Li, Alexander T. Pearson, Randy F. Sweis, Trachette L. Jackson

9. Impact of stroma remodeling on forces experienced by cancer cells and stromal cells within a pancreatic tumor tissue

   Morgan Connaughton, Mahsa Dabagh

1. Mechanistic Learning: The best of both worlds.
   The Mathematical Oncology Blog
   Jeffrey West: “As near I can tell, the phrase "mechanistic learning" was coined in 2020 CPT paper by Sebastien Benzekry. He makes the point that two separate fields (AI & mechanistic modeling) have progressed rapidly in their ability to describe and predict biology, but they unfortunately remain largely partitioned. However, there are promising synergies to be made: mechanistic models typically deal in low dimension data with time-series information and AI/deep learning methods are great with big data but lack interpretability. New approaches that leverage the advantage of both fields ought to have a new name: mechanistic learning.“

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 pre-print: The dynamic fitness landscape of ageing haematopoiesis through clonal competition posted on bioRxiv

Artist: Nathaniel V. Mon Père

Caption: Genetic heterogeneity – emerging from mutational processes and expanding clones – pervades somatic tissues at all stages of human life. This phenomenon has been well-documented in blood, where extensively shared mutations are common late in life. A newly occurring mutation must grant its carriers a significant fitness advantage over other cells for it to escape extinction from stochastic fluctuations and reach detectable size within a human lifetime. While such events are rare, they remain common enough that by the end of middle age some handful of highly fit clones compete for dominance, altering the fitness landscape in which they coexist and slowing each other down in the process. The image background portrays the expansion of various detectable clones in time (from left to right) as distinctly colored bands whose width represents clone size. In the foreground clonal expansions are depicted as logarithmic curves. The shaded area displays the “sub-detection” regime in which clones are still virtually invisible to sampling and run the risk of stochastic extinction.

Read the related post on The Mathematical Oncology Blog, here.

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