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Learning delayed influences of biological systems.

ABSTRACT: Boolean networks are widely used model to represent gene interactions and global dynamical behavior of gene regulatory networks. To understand the memory effect involved in some interactions between biological components, it is necessary to include delayed influences in the model. In this paper, we present a logical method to learn such models from sequences of gene expression data. This method analyzes each sequence one by one to iteratively construct a Boolean network that captures the dynamics of these observations. To illustrate the merits of this approach, we apply it to learning real data from bioinformatic literature. Using data from the yeast cell cycle, we give experimental results and show the scalability of the method. We show empirically that using this method we can handle millions of observations and successfully capture delayed influences of Boolean networks.

SUBMITTER: Ribeiro T

PROVIDER: S-EPMC4296389 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Learning delayed influences of biological systems.

Ribeiro Tony T Magnin Morgan M Inoue Katsumi K Sakama Chiaki C

Frontiers in bioengineering and biotechnology 20150116

Boolean networks are widely used model to represent gene interactions and global dynamical behavior of gene regulatory networks. To understand the memory effect involved in some interactions between biological components, it is necessary to include delayed influences in the model. In this paper, we present a logical method to learn such models from sequences of gene expression data. This method analyzes each sequence one by one to iteratively construct a Boolean network that captures the dynamic ...[more]

PMID: 25642421

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Project description:Dengue virus (DENV) infects hundreds of millions of people annually, yet there is only a limited knowledge of the host immune response to dengue. Here, we used a systems biological approach to perform a detailed analysis of the innate immune response to DENV infection in the whole blood samples of acutely infected humans in Bangkok, Thailand. Transcriptomic analysis revealed that genes encoding pro-inflammatory mediators and type I IFN related proteins, were associated with high levels of virus during the first few days of infection. Individuals with low or negative viremia at the late stage of fever were enriched with genes associated with pathways involved in cell cycle, proliferation, cell metabolism and translational control. Meta-analysis showed significant enrichment in genes specific for innate cells (monocytes, macrophages and DCs) in the specimens with high VL and enrichment in genes specific for NK cells, CD4+ and CD8+ T cells as well as B cells in specimens with low VL. Furthermore, flow cytometric analysis revealed an expansion in the numbers of CD14+CD16+ monocytes and depletion of CD14dimCD16++ cells and BDCA-1+ myeloid DC in blood. Consistent with this, in a non-human primate model, infection with DENV boosted the numbers of CD14+CD16+ monocytes in the blood and in secondary lymphoid organs. In vitro, freshly isolated blood monocytes infected with DENV up regulated CD16 and mediated robust differentiation of resting B cells to CD27++CD38++ plasmablasts and IgG and IgM secretion. Taken together, these data provide a detailed picture of the innate response to dengue infection in humans, and highlight an unappreciated role for CD14+CD16+ monocytes in promoting the differentiation of plasmablasts and mediating antibody response to DENV. We analyzed whole blood samples from 28 dengue patients (DF n=18, DHF=10) hospitalized at the Siriraj Hospital in Bangkok, Thailand during the season of 2009 The specimens were acquired between days 2 and 9 after onset of symptoms (acute illness), and for 19 patients (DF n=13, DHF=6) also at the convalescence at 4 weeks or later after discharge. Additionally, blood was sampled from 9 healthy, non-infected donors to provide controls for transcriptomic and immunological analysis.

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Learning delayed influences of biological systems.

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Learning delayed influences of biological systems.

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