Combining gene expression QTL mapping and phenotypic spectrum analysis to uncover gene regulatory relationships

Gene expression QTL (eQTL) mapping can suggest candidate regulatory relationships between genes. Recent advances in mammalian phenotype annotation such as mammalian phenotype ontology (MPO) enable systematic analysis of the phenotypic spectrum subserved by many genes. In this study we combined eQTL mapping and phenotypic spectrum analysis to predict gene regulatory relationships. Five pairs of genes with similar phenotypic effects and potential regulatory relationships suggested by eQTL mapping were identified. Lines of evidence supporting some of the predicted regulatory relationships were obtained from biological literature. A particularly notable example is that promoter sequence analysis and real-time PCR assays support the predicted regulation of protein kinase C epsilon (Prkce) by cAMP responsive element binding protein 1 (Creb1). Our results show that the combination of gene eQTL mapping and phenotypic spectrum analysis may provide a valuable approach to uncovering gene regulatory relations underlying mammalian phenotypes.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.     

Proteomic approaches to uncover MMP function

Proteomics has revolutionized protease research and particularly contributed to the identification of novel substrates and their sites of cleavage as key determinants of protease function. New technologies and rapid advancements in development of powerful mass spectrometers allowed unprecedented insights into activities of matrix metalloproteinases (MMPs) within their complex extracellular environments. Mass spectrometry-based proteomics extended our knowledge on MMP cleavage specificities and will help to develop more specific inhibitors as new therapeutics. Quantitative proteomics and N-terminal enrichment strategies have revealed numerous novel MMP substrates and shed light on their modes of action in vitro and in vivo. In this review, we provide an overview of current proteomic technologies in protease research and their application to the functional characterization of MMPs.     

An integrated approach to uncover drivers of cancer

Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma data set. Our analysis correctly identified known drivers of melanoma and predicted multiple tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer.     

Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

ABSTRACT: 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 complexity 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.     

Quantitative approaches to uncover physical mechanisms of tissue morphogenesis

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Dynamical approaches to modeling developmental gene regulatory networks

The network of interacting regulatory signals within a cell comprises one of the most complex and powerful computational systems in biology. Gene regulatory networks (GRNs) play a key role in transforming the information encoded in a genome into morphological form. To achieve this feat, GRNs must respond to and integrate environmental signals with their internal dynamics in a robust and coordinated fashion. The highly dynamic nature of this process lends itself to interpretation and analysis in the language of dynamical models. Modeling provides a means of systematically untangling the complicated structure of GRNs, a framework within which to simulate the behavior of reconstructed systems and, in some cases, suites of analytic tools for exploring that behavior and its implications. This review provides a general background to the idea of treating a regulatory network as a dynamical system, and describes a variety of different approaches that have been taken to the dynamical modeling of GRNs. Birth Defects Research (Part C) 87:131–142, 2009. © 2009 Wiley‐Liss, Inc.     

Combining many interaction networks to predict gene function and analyze gene lists

In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems.     

From gene regulation to gene function: regulatory networks in bacillus subtilis

Bacillus subtilis is a sporulating Gram-positive bacterium that lives primarily in the soil and associated water sources. The publication of the B. subtilis genome sequence and subsequent systematic functional analysis and gene regulation programmes, together with an extensive understanding of its biochemistry and physiology, makes this micro-organism a prime candidate in which to model regulatory networks in silico. In this paper we discuss combined molecular biological and bioinformatical approaches that are being developed to model this organism's responses to changes in its environment.     

Vertebrate segmentation: from cyclic gene networks to scoliosis

One of the most striking features of the human vertebral column is its periodic organization along the anterior-posterior axis. This pattern is established when segments of vertebrates, called somites, bud off at a defined pace from the anterior tip of the embryo's presomitic mesoderm (PSM). To trigger this rhythmic production of somites, three major signaling pathways--Notch, Wnt/β-catenin, and fibroblast growth factor (FGF)--integrate into a molecular network that generates a traveling wave of gene expression along the embryonic axis, called the "segmentation clock." Recent systems approaches have begun identifying specific signaling circuits within the network that set the pace of the oscillations, synchronize gene expression cycles in neighboring cells, and contribute to the robustness and bilateral symmetry of somite formation. These findings establish a new model for vertebrate segmentation and provide a conceptual framework to explain human diseases of the spine, such as congenital scoliosis.     

Inferring gene networks from steady-state response to single-gene perturbations

Inferring gene networks from gene expression data is an important step in understanding the molecular machinery of life. Three methods for establishing and quantifying causal relationships between genes based on steady-state measurements in single-gene perturbation experiments have recently been proposed: the regulatory strength method, the local regulatory strength method, and Gardner's method. The theoretical basis of these methods is presented here in a thorough and consistent fashion. In principle, for the same data set all three methods would generate identical networks, but they would quantify the strengths of connections in different ways. The regulatory strength method is shown here to be topology-dependent. It adopts the format of the data collected in gene expression microarray experiments and therefore can be immediately used with this technology. The regulatory strengths obtained by this method can also be used to compute local regulatory strengths. In contrast, Gardner's method requires both measurements of mRNA concentrations and measurements of the applied rate perturbations, which is not usually part of a standard microarray experimental protocol. The results generated by Gardner's method and by the two regulatory strengths methods differ only by scaling constants, but Gardner's method requires more measurements. On the other hand, the explicit use of rate perturbations in Gardner's approach allows one to address new questions with this method, like what perturbations caused given responses of the system. Results of the application of the three techniques to real experimental data are presented and discussed. The comparative analysis presented in this paper can be helpful for identifying an appropriate technique for inferring genetic networks and for interpreting the results of its application to experimental data.