geneKeller

geneKeller is a conceptual framework used in computational biology and bioinformatics to understand and analyze gene regulation networks. It proposes that the expression levels of genes can be modeled as a system where each gene's state is influenced by a combination of its own previous state and the states of other genes in the network, often referred to as its "parents." This influence is typically represented by a set of parameters or "weights" that dictate the strength and nature of the interaction.

The term "geneKeller" draws inspiration from the concept of Keller's rule, a principle in automaton theory that describes the evolution of a cellular automaton state based on its neighbors. In the context of gene networks, the "cells" are genes, and their "states" represent their expression levels (e.g., high, low, or intermediate). The "neighbors" are the genes that directly influence a given gene's expression. The framework allows for the construction of mathematical models that simulate the dynamic behavior of these networks over time, offering insights into how genetic circuits function, how they respond to stimuli, and how disruptions can lead to disease.

Applications of the geneKeller framework include predicting gene expression patterns, identifying key regulatory genes, and designing synthetic gene circuits. Researchers can use these models to test hypotheses about gene interactions and to explore the emergent properties of complex biological systems. The underlying mathematical principles often involve Boolean networks, probabilistic graphical models, or differential equations, depending on the specific implementation and the desired level of detail.