Jane Synnergren
ABSTRACT
Systems biology aims to understand the behavior of and interaction between various components of the living cell, such as genes, proteins, and metabolites. A large number of components are involved in these complex systems and the diversity of relationships between the components can be overwhelming, and there is therefore a need for analysis methods incorporating data integration. We here present a method for exploring gene regulatory mechanisms which integrates various types of data to assist the identification of important components in gene regulation mechanisms. By first analyzing gene expression data, a set of differentially expressed genes is selected. These genes are then further investigated by combining various types of biological information, such as clustering results, promoter sequences, binding sites, transcription factors and other previously published information regarding the selected genes. Inspired by Information Fusion research, we also mapped functions of the proposed method to the well-known OODA-model to facilitate application of this data integration method in other research communities. We have successfully applied the method to genes identified as differentially expressed in human embryonic stem cells at different stages of differentiation towards cardiac cells. We identified 15 novel motifs that may represent important binding sites in the cardiac cell linage.
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Index Terms
- A data integration method for exploring gene regulatory mechanisms
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Reviews
Tommaso Mazza
The problem of understanding the behavior of the constituting components of living beings is of great interest. Many steps have been taken in this direction, but most "cellular intelligence" is still being ignored by our sciences. Experimental findings have confirmed many theoretical assumptions, and databases have been populated to permanently store and make information further available. Redundancy and overlapped bio-concepts are key factors for such repositories. Consequently, cross references have been established between them. From this perspective, the main challenge lies in mining knowledge from them. Synnergren et al. address this problem by proposing an integration method for exploring gene regulatory mechanisms. They give short descriptions of some types of data-and data sources-generally used in gene regulation studies. They discuss pure biological and "derived" information, without providing clear distinctions between these two classes. Then, they claim a methodology to explore gene regulatory mechanisms by integrating such data sources. Unfortunately, what it comes down to is a series of well-known tasks and tools that are individually presented; they are not described from a global cooperative perspective. Possible ways to combine them would have been of great interest. The biological description of the use case changes the tone of the paper, as it quickly becomes unnecessarily complex and detailed; then, the reader gets lost and his or her attention drops. Hence, even though the results seem interesting, it is hard to appreciate and evaluate them. Some grammatical errors and misspelled words also decrease the paper's readability. Online Computing Reviews Service
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