Understanding cancer invasion requires linking intracellular regulatory processes with multicellular dynamics. Boolean modelling provides a powerful framework to describe phenotypic transitions such as the mesenchymal–epithelial transition (MET), capturing how signalling networks and environmental cues regulate cell plasticity and state switching.
At a larger scale, intracellular logical...
Cell fate decisions are driven by gene regulatory networks (GRNs). While the mutually inhibitory toggle switch effectively models binary fate decisions, fully connected inhibitory networks with more than two nodes fail to capture multi-fate decisions due to the low prevalence of "single-high states", where only a single master regulator is highly expressed. In this study, we use monotone...
In Boolean networks, biological phenotypes are traditionally mapped to system attractors. However, attractors are often sensitive to the chosen update scheme and the granularity of the influence graph. Applying a more permissive update scheme or adding mediator nodes can easily disrupt plausible attractors, forcing modelers to carefully tune model properties to achieve biologically relevant...
Regulatory networks in cell biology specify monotonicity structure of interactions between genes and proteins, but do not specify the interactions between multiple inputs.
We describe several classes of models compatible with a given regulatory network:
$\mathbf{(A)}$ a collection of all monotone Boolean networks (MBF) whose influence graph matches the network;
$\mathbf{(B)}$ a...
Every year new experimental technologies in cell biology continue to generate more data about the cellular responses to different conditions and in different environments. As sophisticated as these data are, they still provide only partial snapshots of a behavior of a living cell in their natural environment.
For the data to yield understanding and, ultimately, control of cellular processes...
