Strong regulatory graphs
This work addresses the scalability bottleneck in logical modeling of biological networks by reducing the need to specify update functions for every vertex.
The paper introduces 'strong regulation' for logical models in biology, where a vertex updates only if all predecessors agree, otherwise it becomes ambiguous. It explores attractors in such networks, showing that phenotype attractors exist where some variables are fixed and others arbitrary.
Logical modeling is a powerful tool in biology, offering a system-level understanding of the complex interactions that govern biological processes. A gap that hinders the scalability of logical models is the need to specify the update function of every vertex in the network depending on the status of its predecessors. To address this, we introduce in this paper the concept of strong regulation, where a vertex is only updated to active/inactive if all its predecessors agree in their influences; otherwise, it is set to ambiguous. We explore the interplay between active, inactive, and ambiguous influences in a network. We discuss the existence of phenotype attractors in such networks, where the status of some of the variables is fixed to active/inactive, while the others can have an arbitrary status, including ambiguous.