Inferring Time-Delayed Causal Gene Network Using Time-Series Expression Data

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 12; no. 5; pp. 1169 - 1182
• Main Authors: Lo, Leung-Yau, Leung, Kwong-Sak, Lee, Kin-Hong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Bioinformatics; causality; Computational Biology; Computer Simulation; Correlation; Delay effects; Delays; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation - physiology; Genes; Genetic algorithms; Government regulations; Humans; Inference algorithms; Links; Models, Biological; Mutual information; Networks; Oligonucleotide Array Sequence Analysis - methods; Protein Interaction Mapping - methods; Proteome - metabolism; Signal Transduction - physiology; Time delay; time delays; Time Factors; Time series analysis; transcriptional gene regulatory networks; time delays; transcriptional gene regulatory networks; Computational Biology; causality
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2015.2394442

Inferring gene regulatory network (GRN) from the microarray expression data is an important problem in Bioinformatics, because knowing the GRN is an essential first step in understanding the inner workings of the cell and the related diseases. Time delays exist in the regulatory effects from one gene to another due to the time needed for transcription, translation, and to accumulate a sufficient number of needed proteins. Also, it is known that the delays are important for oscillatory phenomenon. Therefore, it is crucial to develop a causal gene network model, preferably as a function of time. In this paper, we propose an algorithm CLINDE to infer causal directed links in GRN with time delays and regulatory effects in the links from time-series microarray gene expression data. It is one of the most comprehensive in terms of features compared to the state-of-the-art discrete gene network models. We have tested CLINDE on synthetic data, the in vivo IRMA (On and Off) datasets and the [1] yeast expression data validated using KEGG pathways. Results show that CLINDE can effectively recover the links, the time delays and the regulatory effects in the synthetic data, and outperforms other algorithms in the IRMA in vivo datasets.