Year: 2023
Type: preprint
Abstract: Identifying how proteins secreted by malignant cells reorganize the composition and functional orientation of different cells with a tissue during oncogenesis has important implications for developing therapies for cancer.Given the inherent dynamics and complexity associated with the interplay between secreted proteins and networks of cell-to-cell communication within tissues, mechanistic mathematical modeling and simulation is becoming an important tool in discovering and developing anti-cancer therapies.Currently, the topology or structure of the model, which includes the cell types (i.e., nodes) and their interactions (i.e., causal arcs), is created by hand.As hand-curated models can implicitly impose bias on how data is interpreted, we have prototyped a data-driven, rule-based modeling approach for identifying these cell-level networks in the context of immuno-oncology.While the initial effort focused on predicting the cell-level network based on human data and validating aspects of the predicted network using syngeneic mouse models, we can envision a project that can test this approach more rigorously.However, there are a number of questions that people have raised about this approach.Here, I'm going to address some of these frequently asked questions (FAQs).
DOI: 10.31224/2816
Author: David Klinke
Institution: West Virginia University
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4319790059