Structural Equation Modeling for Systems Biology

Structural Equation Modeling for Systems Biology

Sachiyo Aburatani (Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Japan) and Hiroyuki Toh (Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Japan)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch044
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Issues, Controversies, Problems

Investigations of gene regulatory systems or complex functional networks among DNA, RNA, proteins and other cellular components in a living cell conventionally follow a standard protocol. After a DNA sequence is completed, the mRNA level is measured by a cDNA microarray, to reveal the gene expression profiles under various conditions. From this information, various algorithms, including Boolean and Bayesian networks, have been developed to infer complex gene networks (Akutsu et al., 2000; Friedman, et al., 2000). In our previous investigation, we developed an approach based on graphical Gaussian modeling (GGM) combined with hierarchical clustering (Aburatani et al., 2003). We can infer the huge network among all of the genes by the GGM approach, since this approach is suitable for massive amounts of gene expression data (Aburatani & Horimoto, 2005). However, GGM infers only the undirected graph, whereas the Boolean and Bayesian models infer the directed graph, which shows causality. Although all of these approaches are feasible for establishing relationships among genes, it is difficult to reveal the critical interactions between genes and the other cellular components, owing to the insufficient information about the other cellular components in the gene expression profiles. Since estimation of regulatory network between genes and the other cellular components is absolutely essential to uncover the mechanism of gene expression control, an alternative approach is needed.

Key Terms in this Chapter

Gene Regulatory Network: A regulatory interactions between genes for expression of those genes.

Observable Variable: A variable that can be observed and directly measured.

Structural Equation Modeling (SEM): A statistical technique for testing and estimating causal relations from a combination of measured data and assumed causalities.

Gene Expression: Synthesis process of mRNA from a gene.

Transcription Factor: A protein that binds to specific DNA sequences and controls the process of genetic information from DNA to mRNA.

Systems Biology: A biology-based inter-disciplinary field of study that focuses on complex interactions within biological systems with holistic perspective.

Factor Analysis: A statistical method for describing variability among observed variables.

Cellular Component: Unique and highly organized substances which are composed of cell.

Latent Variable: A variable that is not directly measured but are rather inferred from observed variables.

Genetic Network: Same with “Gene regulatory network.”

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