Computational models are not just appealing because they can simulate and predict the
development of biological phenomena across multiple spatial and temporal scales, but also …

In this opinion, we highlight agent-based modeling as a key tool for exploration of cell–cell
and cell–environment interactions that drive cancer progression, therapeutic resistance, and …

We provide an overview on the use of biological assays to calibrate and initialize
mechanism-based models of cancer phenomena. Although artificial intelligence methods …

There have been many techniques developed in recent years to in silico model a variety of
cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling …

Multiscale systems biology is having an increasingly powerful impact on our understanding
of the interconnected molecular, cellular, and microenvironmental drivers of tumor growth …

Every patient and every disease is different. Each patient therefore requires a personalized
treatment approach. For technical reasons, a personalized approach is feasible for treatment …

Quantitative predictive modeling of cancer growth, progression, and individual response to
therapy is a rapidly growing field. Researchers from mathematical modeling, systems …

Background Cancer is a complex, multiscale dynamical system, with interactions between
tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host …

Cancer biology involves complex, dynamic interactions between cancer cells and their
tissue microenvironments. Single-cell effects are critical drivers of clinical progression …

Simulating cancer behavior across multiple biological scales in space and time, ie,
multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine …