Recurrent Neural Network model for gene regulatory networks Demo of an early GUI interface for building such models. This is a simulation of some decision making network in a single cell Each circle represents a DNA binding protein, the foundations of gene regulation. The growing/shrinking circle in the center of each is the concentration of that protein in the cell. Connections between different proteins represent the interaction one protein has on the rate of production of another DNA-binding protein (regulation of gene expression). Positive weights use an arrow, and negative weights use a flat arrow, and the thickness of the arrow represents the strength of the weight. The rate of production of a protein depends on the concentration of regulating proteins, along with a constant expression rate (constitutive expression) and some decay parameter that models protein degradation. This model will in the future be an extension of reaction diffusion systems, where each node in the network contains a regulatory network like the one shown here, and a subset of the regulatory proteins are allowed to diffuse between cells.
I’m working on a program to edit and simulate gene regulatory networks using recurrent neural networks as a model. Here’s a demo.