Bridging the gap between the micro- and the macro-world of tumors

TL;DR

This paper presents a computational simulation model that links microscopic cellular processes to macroscopic tumor behavior, aiming to predict large tumor dynamics from basic biological knowledge.

Contribution

It introduces a detailed yet understandable simulation framework that bridges cellular-level processes with tumor-scale modeling, addressing a key gap in cancer research.

Findings

Developed a simulation program based on biochemical and biophysical processes.

Provides a structured approach to connect microscopic and macroscopic tumor models.

Facilitates prediction of tumor behavior from cellular-level data.

Abstract

At present it is still quite difficult to match the vast knowledge on the behavior of individual tumor cells with macroscopic measurements on clinical tumors. On the modeling side, we already know how to deal with many molecular pathways and cellular events, using systems of differential equations and other modeling tools, and ideally, we should be able to extend such a mathematical description up to the level of large tumor masses. An extended model should thus help us forecast the behavior of large tumors from our basic knowledge of microscopic processes. Unfortunately, the complexity of these processes makes it very difficult -- probably impossible -- to develop comprehensive analytical models. We try to bridge the gap with a simulation program which is based on basic biochemical and biophysical processes -- thereby building an effective computational model -- and in this paper we describe its structure, endeavoring to make the description sufficiently detailed and yet understandable.