2022/12/26 by Niklas Bonacker, Bonacker, Niklas, Johannes Berg +1
Biochemistry, Genetics and Molecular Biology · #Advanced Fluorescence Microscopy Techniques #Bioinformatics and Genomic Networks #FOS: Biological sciences #Gene Regulatory Network Analysis #Molecular Networks (q-bio.MN)
paper · pdf · doi:10.48550/arxiv.2212.13168
openalex publication_date 2022/12/26 · openalex created_date 2023/01/06 · openalex updated_date 2026/07/28
Regulatory networks describe the interactions between molecular or cellular regulators, like transcription factors and genes in gene regulatory networks, kinases and their receptors in signalling networks, or neurons in neural networks. A long-standing aim of quantitative biology is to reconstruct such networks on the basis of large-scale data. Our aim is to leverage fluctuations around the non-equilibrium steady state for network inference. To this end, we use a stochastic model of gene regulation or neural dynamics and solve it approximately within a Gaussian mean-field theory. We develop a likelihood estimate based on this stochastic theory to infer regulatory interactions from perturbation data on the network nodes. We apply this approach to artificial perturbation data as well as to phospho-proteomic data from cell-line experiments and compare our results to inference schemes restricted to mean activities in the steady state.