Mechanistic models are built using knowledge as the primary information source, with well-
established biological and physical laws determining the causal relationships within the …

Mechanistic models of biological processes can explain observed phenomena and predict
responses to a perturbation. A mathematical model is typically constructed using expert …

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

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

To date, parameters defining biological properties in multiscale disease models are
commonly obtained from a variety of sources. It is thus important to examine the influence of …

We present a multiscale agent-based non-small cell lung cancer model that consists of a 3D
environment with which cancer cells interact while processing phenotypic changes. At the …

Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …

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 …

Cancer is a heterogeneous and complex disease and one of the leading causes of death
worldwide. The high tumor heterogeneity between individuals affected by the same cancer …

Quantitative dynamical models facilitate the understanding of biological processes and the
prediction of their dynamics. These models usually comprise unknown parameters, which …