Simple Summary Omics technologies and targeted drugs are revolutionizing the clinical
oncology landscape, portending the promise of precision oncology. However, our abilities to …

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate
individual cell interactions and microenvironmental dynamics. Unfortunately, the high …

In the last decades, mathematical and computational models have become ubiquitous to the
field of systems biology. Specifically, the multiscale nature of biological processes makes the …

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

Despite a growing body of knowledge on the mechanisms underlying the onset and
progression of cancer, treatment success rates in oncology are at best modest. Current …

Machine learning has become a powerful tool for systems biologists, from diagnosing
cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a …

The usefulness of mechanistic models to disentangle complex multiscale cancer processes,
such as treatment response, has been widely acknowledged. However, a major barrier for …

Human cancer is a complex and heterogeneous collection of diseases that kills more than
18 million people every year worldwide. Despite advances in detection, diagnosis, and …

Computational models that can explain and predict complex sub-cellular, cellular, and tissue-
level drug response mechanisms could speed drug discovery and prioritize patient-specific …

Highlights•Choice of modeling framework should account for experimental context and
data.•Accurate models offer biological insight and explain non-intuitive observations.•Multi …