IEEE Transactions on Signal Processing
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
The evolution of higher-level biochemical reaction models
Genetic Programming and Evolvable Machines
Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inference of hidden variables in systems of differential equations with genetic programming
Genetic Programming and Evolvable Machines
Using gene expression programming to infer gene regulatory networks from time-series data
Computational Biology and Chemistry
Hi-index | 0.00 |
A key issue in genomic signal processing is the inference of gene regulatory networks. These are used both to understand the role of biological regulation in phenotypic determination and to derive therapeutic strategies for genetic-based diseases. In this paper, gene regulatory networks are inferred via evolutionary modeling based on time-series microarray measurements. A nonlinear differential equation model is adopted. It includes random noise parameters for intrinsic noise arising from stochasticity in transcription and translation and for external noise arising from factors such as the amount of RNA polymerase, levels of regulatory proteins, and the effects of mRNA and protein degradation. An iterative algorithm is proposed for model identification. Genetic programming is applied to identify the structure of the model and Kalman filtering is used to estimate the parameters in each iteration. Both standard and robust Kalman filtering are considered. The effectiveness of the proposed scheme is demonstrated by using synthetic data and by using microarray measurements pertaining to yeast protein synthesis.