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Using Systems Biology Approaches to Predict New Players in the Innate Immune System

Using Systems Biology Approaches to Predict New Players in the Innate Immune System

Bin Li
Copyright: © 2011 |Pages: 50
ISBN13: 9781609604912|ISBN10: 1609604911|EISBN13: 9781609604929
DOI: 10.4018/978-1-60960-491-2.ch020
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MLA

Li, Bin. "Using Systems Biology Approaches to Predict New Players in the Innate Immune System." Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., IGI Global, 2011, pp. 428-477. https://doi.org/10.4018/978-1-60960-491-2.ch020

APA

Li, B. (2011). Using Systems Biology Approaches to Predict New Players in the Innate Immune System. In L. Liu, D. Wei, Y. Li, & H. Lei (Eds.), Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications (pp. 428-477). IGI Global. https://doi.org/10.4018/978-1-60960-491-2.ch020

Chicago

Li, Bin. "Using Systems Biology Approaches to Predict New Players in the Innate Immune System." In Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., 428-477. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-491-2.ch020

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Abstract

Toll-like receptors (TLRs) are critical players in the innate immune response to pathogens. However, transcriptional regulatory mechanisms in the TLR activation pathways are still relatively poorly characterized. To address this question, the author of this chapter applied a systematic approach to predict transcription factors that temporally regulate differentially expressed genes under diverse TLR stimuli. Time-course microarray data were selected from mouse bone marrow-derived macrophages stimulated by six TLR agonists. Differentially regulated genes were clustered on the basis of their dynamic behavior. The author then developed a computational method to identify positional overlapping transcription factor (TF) binding sites in each cluster, so as to predict possible TFs that may regulate these genes. A second microarray dataset, on wild-type, Myd88-/- and Trif-/- macrophages stimulated by lipopolysaccharide (LPS), was used to provide supporting evidence on this combined approach. Overall, the author was able to identify known TLR TFs, as well as to predict new TFs that may be involved in TLR signaling.

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