Challenging age-structured and first order transition cell cycle models of cell proliferation
Ubezio, P.
Show abstract
Uncontrolled cell proliferation is the key feature of tumours. Because experimental measures provide only a partial view to the underlying proliferative processes, such as cell cycling, cell quiescence and cell death, mathematical modelling aims to provide a unifying view of the data with a quantitative description of the contributing basic processes. Modelling approaches to proliferation of cell populations can be divided in two main categories: those based on first order transitions between successive compartments and those including a structure of the cells life cycle. Here we challenge basic models belonging to the two categories to fit time course data sets, from our laboratory experience, obtained observing the proliferative phenomenon with different experimental techniques in a cancer cell line. We disclose the limitations of too simple models. At the minimal complexity level accounting for all available data the two approaches converge and suggest similar scenarios for the underlying proliferation process, in both untreated conditions and after treatment.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy 96%
- Chemotherapy-Induced Cachexia and Model-Informed Dosing to Preserve Lean Mass in Cancer Treatment 95%
- Combining Hypoxia-Activated Prodrugs and Radiotherapy in silico: Impact of Treatment Scheduling and the Intra-Tumoural Oxygen Landscape 95%
Similar papers in this journal
Similar papers in this journal
- A DNA-structured mathematical model of cell-cycle progression in cyclic hypoxia 96%
- A novel 3D atomistic-continuum cancer invasion model: In silico simulations of an in vitro organotypic invasion assay 95%
- Interplay of p53 and XIAP protein dynamics orchestrates cell fate in response to chemotherapy 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.