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

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 …

Making decisions on how best to treat cancer patients requires the integration of different
data sets, including genomic profiles, tumour histopathology, radiological images, proteomic …

Recent progress in our understanding of cancer mostly relies on the systematic profiling of
patient samples with high-throughput techniques like transcriptomics. With this approach …

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 …

Cancer is a complex disease that involves multiple types of biological interactions across
diverse physical, temporal, and biological scales. This complexity presents substantial …

A systems-biology approach to complex disease (such as cancer) is now complementing
traditional experience-based approaches, which have typically been invasive and …

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 …

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 …

An important challenge facing the field is how better to translate in vitro discoveries to the
clinic. Computational systems biology approaches that use omic data to predict biology …