1 code implementation • 23 May 2024 • Even Moa Myklebust, Arnoldo Frigessi, Fredrik Schjesvold, Jasmine Foo, Kevin Leder, Alvaro Köhn-Luque
In this work, we develop a hierarchical Bayesian model of subpopulation dynamics that uses baseline covariate information to predict cancer dynamics under treatment, inspired by cancer dynamics in multiple myeloma (MM), where serum M protein is a well-known proxy of tumor burden.
no code implementations • 12 Dec 2023 • John Metzcar, Catherine R. Jutzeler, Paul Macklin, Alvaro Köhn-Luque, Sarah C. Brüningk
This review aims to capture the current state of the field and provide a perspective on how mechanistic learning may further progress in mathematical oncology.
no code implementations • 2 May 2021 • Xiaoran Lai, Håkon A. Taskén, Torgeir Mo, Simon W. Funke, Arnoldo Frigessi, Marie E. Rognes, Alvaro Köhn-Luque
Coupling discrete cell-based models with continuous models using hybrid cellular automata is a powerful approach for mimicking biological complexity and describing the dynamical exchange of information across different scales.
no code implementations • 31 Jul 2020 • Alvaro Köhn-Luque, Xiaoran Lai, Arnoldo Frigessi
Cancer pathology is unique to a given individual, and developing personalized diagnostic and treatment protocols are a primary concern.