Correction: Identification of Potential Pathway Mediation Targets in Toll-like Receptor Signaling.

PLoS Computational Biology (Impact Factor: 4.83). 11/2009; 5(11). DOI: 10.1371/annotation/5cc0d918-83b8-44e4-9778-b96a249d4099
Source: PubMed

ABSTRACT [This corrects the article on p. e1000292 in vol. 5, PMID: 19229310.].

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    ABSTRACT: Mathematical models of biochemical networks, such as metabolic, signaling, and gene networks, have been studied extensively and have been shown to provide accurate descriptions of various cell processes. Nevertheless, their usage is restricted by the fact that they are usually studied in isolation, without feedback from the environment in which they evolve. Integrating these models in a global framework is a promising direction in order to increase both their accuracy and predictive capacity. In this paper, we describe the integration of large-scale metabolic and signaling networks with a regulatory gene network. We focus on the response to infection in mouse macrophage cells. Our computational framework allows to virtually simulate any type of infection and to follow its effect on the cell. The model comprises 3,507 chemical species involved in 4,630 reactions evolving at the fast time scale of metabolic and signaling processes. These interact with 20 genes evolving at the slow time scale of gene expression and regulation. We develop a simulator for this model and use it to study infections with Porphyromonas gingivalis.
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    ABSTRACT: We describe the reconstruction of a gene regulatory network involved with the Toll-like Receptor signaling pathways. By applying our recent identification algorithm to a time series gene expression dataset, we identify regulatory interactions between genes and construct discrete-time piece-wise affine regulatory functions. Our validation shows that our model predicts the expression levels of the genes involved in the network with good accuracy.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:2430-3.
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    ABSTRACT: The Toll-Like Receptors (TLRs) are proteins involved in the immune system that increase cytokine levels when triggered. While cytokines coordinate the response to infection, they appear to be detrimental to the host when reaching too high levels. Several studies have shown that the deletion of specific TLRs was beneficial for the host, as cytokine levels were decreased consequently. It is not clear, however, how targeting other components of the TLR pathways can improve the responses to infections. We applied the concept of Minimal Cut Sets (MCS) to the ihsTLR v1.0 model of the TLR pathways to determine sets of reactions whose knockouts disrupt these pathways. We decomposed the TLR network into 34 modules and determined signatures for each MCS, i.e. the list of targeted modules. We uncovered 2,669 MCS organized in 68 signatures. Very few MCS targeted directly the TLRs, indicating that they may not be efficient targets for controlling these pathways. We mapped the species of the TLR network to genes in human and mouse, and determined more than 10,000 Essential Gene Sets (EGS). Each EGS provides genes whose deletion suppresses the network's outputs.
    PLoS ONE 02/2012; 7(2):e31341. · 3.53 Impact Factor

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