Authors:
Ramouna Fouladi
1
;
Emad Fatemizadeh
1
and
S. Shahriar Arab
2
Affiliations:
1
Sharif University of Technology, Iran, Islamic Republic of
;
2
Faculty of Biological Sciences and Tarbiat Modares University, Iran, Islamic Republic of
Keyword(s):
Gene expression, Extended Kalman filtering, Gene regulatory network modelling.
Related
Ontology
Subjects/Areas/Topics:
Bioinformatics
;
Biomedical Engineering
;
Biostatistics and Stochastic Models
;
Genomics and Proteomics
;
Systems Biology
Abstract:
In this paper, the Extended Kalman filtering (EKF) approach has been used to infer gene regulatory networks using time-series gene expression data. Gene expression values are considered stochastic processes and the gene regulatory network, a dynamical nonlinear stochastic model. Using these values and a modified Kalman filtering approach, the model’s parameters and consequently the interactions amongst genes are predicted. In this paper, each gene-gene interaction is modeled using a linear term, a nonlinear one, and a constant term. The linear and nonlinear term coefficients are included in the state vector together with the gene expressions’ true values. Through the extended Kalman filtering process, these coefficients are updated in such a way that the predicted gene expressions follow the ones observed. Finally, connections between each two genes are inferred based on these coefficients.