Experimental guidance for discovering genetic networks through hypothesis reduction on time series
Year: 2022
Type: article
Abstract: Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small "core" network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.
Source: PLoS Computational Biology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,655
Funders: HDR Foundation, National Science Foundation, Simons Foundation, Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico, National Institutes of Health, Defense Advanced Research Projects Agency, National Institute of General Medical Sciences, Defense Sciences Office, DARPA
Awards: Applications of Computational and Topological Methods to Dynamical Systems, G2405702604, G2515541506, G295657101, G3064008058, Tripods+X:Res: Collaborative Research: Identification of Gene Regulatory Network Function from Data, G4713059963, G4941908167, G5270150132, G5591258211, G6397249863, G6517621824, G668241585, HDR TRIPODS: Data Science Principles of the Human-Machine Convergence, Needle in a haystack: effective search for dynamics across the space of networks, Tripods+X:Res: Collaborative Research: Identification of Gene Regulatory Network Function from Data
OpenAlex ID: W4304091700