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Background: Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs). As a logical model, probabilistic Boolean networks (PBNs) consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n) or O(nN2n) for a sparse matrix. Results: This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN). An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n), where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a network inferred from a T cell immune response dataset. An SBN can also implement the function of an asynchronous PBN and is potentially useful in a hybrid approach in combination with a continuous or single-molecule level stochastic model. Conclusions: Stochastic Boolean networks (SBNs) are proposed as an efficient approach to modelling gene regulatory networks (GRNs). The SBN approach is able to recover biologically-proven regulatory behaviours, such as the oscillatory dynamics of the p53-Mdm2 network and the dynamic attractors in a T cell immune response network. The proposed approach can further predict the network dynamics when the genes are under perturbation, thus providing biologically meaningful insights for a better understanding of the dynamics of GRNs. The algorithms and methods described in this paper have been implemented in Matlab packages, which are attached as Additional files. [ABSTRACT FROM AUTHOR]

Background: Gene expression is regulated at both the DNA sequence level and through modification of chromatin. However, the effect of chromatin on tissue/cell-type specific gene regulation (TCSR) is largely unknown. In this paper, we present a method to elucidate the relationship between histone modification/variation (HMV) and TCSR. Results: A classifier for differentiating CD4+ T cell-specific genes from housekeeping genes using HMV data was built. We found HMV in both promoter and gene body regions to be predictive of genes which are targets of TCSR. For example, the histone modification types H3K4me3 and H3K27ac were identified as the most predictive for CpG-related promoters, whereas H3K4me3 and H3K79me3 were the most predictive for nonCpG-related promoters. However, genes targeted by TCSR can be predicted using other type of HMVs as well. Such redundancy implies that multiple type of underlying regulatory elements, such as enhancers or intragenic alternative promoters, which can regulate gene expression in a tissue/cell-type specific fashion, may be marked by the HMVs. Finally, we show that the predictive power of HMV for TCSR is not limited to protein-coding genes in CD4+ T cells, as we successfully predicted TCSR targeted genes in muscle cells, as well as microRNA genes with expression specific to CD4+ T cells, by the same classifier which was trained on HMV data of protein-coding genes in CD4+ T cells. Conclusion: We have begun to understand the HMV patterns that guide gene expression in both tissue/cell-type specific and ubiquitous manner. [ABSTRACT FROM AUTHOR]

Background: Adaptative immune repertoire diversity in vertebrate species is generated by recombination of variable (V), diversity (D) and joining (J) genes in the immunoglobulin (IG) loci of B lymphocytes and in the T cell receptor (TR) loci of T lymphocytes. These V-J and V-D-J gene rearrangements at the DNA level involve recombination signal sequences (RSS). Whereas many data exist, they are scattered in non specialized resources with different nomenclatures (eg. flat files) and are difficult to extract. Description: IMGT/GeneInfo is an online information system that provides, through a user-friendly interface, exhaustive information resulting from the complex mechanisms of T cell receptor V-J and V-DJ recombinations. T cells comprise two populations which express the αβand γδ TR, respectively. The first version of the system dealt with the Homo sapiens and Mus musculus TRA and TRB loci whose gene rearrangements allow the synthesis of the αβ TR chains. In this paper, we present the second version of IMGT/GeneInfo where we complete the database for the Homo sapiens and Mus musculus TRG and TRD loci along with the introduction of a quality control procedure for existing and new data. We also include new functionalities to the four loci analysis, giving, to date, a very informative tool which allows to work on V(D)J genes of all TR loci in both human and mouse species. IMGT/GeneInfo provides more than 59,000 rearrangement combinations with a full gene description which is freely available at http://imgt.cines.fr/ GeneInfo. Conclusion: IMGT/GeneInfo allows all TR information sequences to be in the same spot, and are now available within two computer-mouse clicks. This is useful for biologists and bioinformaticians for the study of T lymphocyte V(D)J gene rearrangements and their applications in immune response analysis. [ABSTRACT FROM AUTHOR]

A conference paper on inferring gene regulatory networks from classified microarray data is presented. The network inference method is based on the optimal network search algorithm proposed by Ott. ROC analysis of networks recovered from synthetic data provides a measure of the performance of the method. The method shows that distinct sub-networks associated with AML and with T-cell responses emerge.