Gene-gene cooperativity in small networks

Abstract

We show how to construct a reduced description of interacting genes in noisy, small regulatory networks using coupled binary "spin" variables. Treating both the protein number and gene expression state variables stochastically and on equal footing we propose a mapping which connects the molecular level description of networks to the binary representation. We construct a phase diagram indicating when genes can be considered to be independent and when the coupling between them cannot be neglected leading to synchrony or correlations. We find that an appropriately mapped boolean description reproduces the probabilities of gene expression states of the full stochastic system very well and can be transfered to examples of self-regulatory systems with a larger number of gene copies.

22 pages, 10 figures

References in corpus (2)

• Exact results for noise power spectra in linear biochemical reaction networks
• Quantitative transcription factor binding kinetics at the single-molecule level

Cited by in corpus (4)

• Maximally informative pairwise interactions in networks
• Approximation scheme based on effective interactions for stochastic gene regulation
• Type-dependent irreversible stochastic spin models for genetic regulatory networks at the level of promotion-inhibition circuitry
• Counting statistics for genetic switches based on effective interaction approximation