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Many-attractor signaling dynamics in complex biological networks

From Q-bio

Although the statistical properties and complex topology of gene regulatory networks has been the subject of intense study in recent years, relatively little is known about how regulatory signals propagate through the networks. An effective mathematical model of gene regulation signaling would allow researchers to search for synergistic drug targets in silico to reduce the number of combinations of drugs to be tested in vitro for combating genetic diseases such as cancer. We present two nonlinear models of gene expression regulation. The first is based on a spin-1/2 Ising ferromagnet with nonuniform interaction strengths and Boolean values for the expression of each node (discrete model). The second model represents interacting genes as overdamped masses coupled by springs with multiple rest lengths in one dimension, and the expression of a gene is represented as the corresponding mass’s position (continuous model). Both models encode experimental gene expression data as state-space attractors, or configurations toward which the system naturally evolves. We compare the models’ predictions to our collaborators’ experimental data on acute myeloid leukemia cells and normal control cells.