Assessment of Regression Methods for Inference of Regulatory Networks Involved in Circadian Regulation
Year: 2013
Type: book-chapter
Abstract: We assess the accuracy of three established regression \nmethods for reconstructing gene and protein regulatory \nnetworks in the context of circadian regulation. Data are \nsimulated from a recently published regulatory network of \nthe circadian clock in Arabidopsis thaliana, in which protein and gene interactions are described by a Markov jump \nprocess based on Michaelis-Menten kinetics. We closely \nfollow recent experimental protocols, including the entrainment of seedlings to different light-dark cycles and \nthe knock-out of various key regulatory genes. Our study \nprovides relative assessment scores for the comparison of \nstate-of-the art regression methods, investigates the influence of systematically missing values related to unknown \nprotein concentrations and mRNA transcription rates, and \nquantifies the dependence of the performance on the degree of recurrency.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G2080879326, G4370143863, G6426415226
OpenAlex ID: W1914190875