Constructing logical models of gene regulatory networks by integrating transcription factor-DNA interactions with expression data: an entropy-based approach

Models of gene regulatory networks (GRNs) attempt to explain the complex processes that determine cells' behavior, such as differentiation, metabolism, and the cell cycle. The advent of high-throughput data generation technologies has allowed researchers to fit theoretical models to experimental data on gene-expression profiles. GRNs are often represented using logical models. These models require that real-valued measurements be converted to discrete levels, such as on/off, but the discretization often introduces inconsistencies into the data. Dimitrova et al. posed the problem of efficiently finding a parsimonious resolution of the introduced inconsistencies. We show that reconstruction of a logical GRN that minimizes the errors is NP-complete, so that an efficient exact algorithm for the problem is not likely to exist. We present a probabilistic formulation of the problem that circumvents discretization of expression data. We phrase the problem of error reduction as a minimum entropy problem, develop a heuristic algorithm for it, and evaluate its performance on mouse embryonic stem cell data. The constructed model displays high consistency with prior biological knowledge. Despite the oversimplification of a discrete model, we show that it is superior to raw experimental measurements and demonstrates a highly significant level of identical regulatory logic among co-regulated genes. A software implementing the method is freely available at: http://acgt.cs.tau.ac.il/modent.     

Factor analysis for gene regulatory networks and transcription factor activity profiles

Background

G Protein-Coupled Receptors (GPCRs) are a large and diverse family of membrane proteins whose members participate in the regulation of most cellular and physiological processes and therefore represent key pharmacological targets. Although several bioinformatics resources support research on GPCRs, most of these have been designed based on the traditional assumption that monomeric GPCRs constitute the functional receptor unit. The increase in the frequency and number of reports about GPCR dimerization/oligomerization and the implication of oligomerization in receptor function makes necessary the ability to store and access information about GPCR dimers/oligomers electronically.

Results

We present here the requirements and ontology (the information scheme to describe oligomers and associated concepts and their relationships) for an information system that can manage the elements of information needed to describe comprehensively the phenomena of both homo- and hetero-oligomerization of GPCRs. The comprehensive information management scheme that we plan to use for the development of an intuitive and user-friendly GPCR-Oligomerization Knowledge Base (GPCR-OKB) is the result of a community dialog involving experimental and computational colleagues working on GPCRs.

Conclusion

Our long term goal is to disseminate to the scientific community organized, curated, and detailed information about GPCR dimerization/oligomerization and its related structural context. This information will be reported as close to the data as possible so the user can make his own judgment on the conclusions drawn for a particular study. The requirements and ontology described here will facilitate the development of future information systems for GPCR oligomers that contain both computational and experimental information about GPCR oligomerization. This information is freely accessible at http://www.gpcr-okb.org.     

Leveraging three-dimensional chromatin architecture for effective reconstruction of enhancer–target gene regulatory interactions

A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer–target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the hierarchy of structural domains which define chromatin three-dimensional (3D) architecture. Here we introduce a change of perspective based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work (i) we develop a computational and statistical framework to reconstruct a comprehensive map of ETG pairs leveraging functional genomics data; (ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy and (iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research.     

A relative variation-based method to unraveling gene regulatory networks

Gene regulatory network (GRN) reconstruction is essential in understanding the functioning and pathology of a biological system. Extensive models and algorithms have been developed to unravel a GRN. The DREAM project aims to clarify both advantages and disadvantages of these methods from an application viewpoint. An interesting yet surprising observation is that compared with complicated methods like those based on nonlinear differential equations, etc., methods based on a simple statistics, such as the so-called Z-score, usually perform better. A fundamental problem with the Z-score, however, is that direct and indirect regulations can not be easily distinguished. To overcome this drawback, a relative expression level variation (RELV) based GRN inference algorithm is suggested in this paper, which consists of three major steps. Firstly, on the basis of wild type and single gene knockout/knockdown experimental data, the magnitude of RELV of a gene is estimated. Secondly, probability for the existence of a direct regulation from a perturbed gene to a measured gene is estimated, which is further utilized to estimate whether a gene can be regulated by other genes. Finally, the normalized RELVs are modified to make genes with an estimated zero in-degree have smaller RELVs in magnitude than the other genes, which is used afterwards in queuing possibilities of the existence of direct regulations among genes and therefore leads to an estimate on the GRN topology. This method can in principle avoid the so-called cascade errors under certain situations. Computational results with the Size 100 sub-challenges of DREAM3 and DREAM4 show that, compared with the Z-score based method, prediction performances can be substantially improved, especially the AUPR specification. Moreover, it can even outperform the best team of both DREAM3 and DREAM4. Furthermore, the high precision of the obtained most reliable predictions shows that the suggested algorithm may be very helpful in guiding biological experiment designs.     

AP2/EREBP transcription factors are part of gene regulatory networks and integrate metabolic, hormonal and environmental signals in stress acclimation and retrograde signalling

Growth of five aeroterrestrial green algal strains (Trebouxiophyceae) in response to changing water availabilities—caused by osmotic (ionic) and matric (desiccation) stresses—was investigated in comparison with a freshwater and a marine strain. All investigated algae displayed good growth under brackish conditions while four out of the five aeroterrestrial strains even grew well under full marine conditions (28–40 psu). The comparison between growth responses in liquid medium, on solid agarose, and on glass fiber filters at 100% air humidity indicated a broad growth tolerance of aeroterrestrial algae towards diminished water availability. While two aeroterrestrial strains even grew better on solid medium which mimics natural biofilm conditions, the aquatic strains showed significant growth inhibition under matric stress. Except Stichococcus sp., which contained the C6-polyol sorbitol, all other aeroterrestrial green algae investigated synthesized and accumulated the C5-polyol ribitol in response to osmotic stress. Using ¹³C NMR spectroscopy and HPLC, it could be verified that ribitol functions as an osmotically regulated organic solute. This is the first proof of ribitol in free-living aeroterrestrial green algae. The biochemical capability to synthesize polyols under environmental stress conditions seems to support algal life outside aquatic habitats.     

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.     

Ecological meta‐networks integrate spatial and temporal dynamics of plant–bumble bee interactions

Bumble bees can forage on a large number of wild plants and crops. The survival of a colony depends on the availability of suitable food resources within foraging range and throughout their forage season. We studied the spatial and temporal use of floral resources by bumble bees in a set of 30 local plant communities and used these data to model colony survival under different combinations of patch size and bumble bee flight distance. Floral resources vary spatially and temporally at the landscape level, and bumble bees track these resources across the landscape during the season. The simulation model showed that different patterns of resources availability could affect the survival and distribution of bumble bee nests across the landscape. This model can be used to generate hypotheses explaining bumble bee richness and abundance that can be tested in real landscapes. Integrating the spatial and temporal dynamics of the flower resources used by bumble bees provides a new perspective that can be used to inform bumble bee conservation, particularly in the context of their widespread decline in recent decades.     

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.     

Negative-sense RNA viruses: An underexplored platform for examining virus–host lipid interactions

Viruses are pathogenic agents that can infect all varieties of organisms, including plants, animals, and humans. These microscopic particles are genetically simple as they encode a limited number of proteins that undertake a wide range of functions. While structurally distinct, viruses often share common characteristics that have evolved to aid in their infectious life cycles. A commonly underappreciated characteristic of many deadly viruses is a lipid envelope that surrounds their protein and genetic contents. Notably, the lipid envelope is formed from the host cell the virus infects. Lipid-enveloped viruses comprise a diverse range of pathogenic viruses, which often lead to high fatality rates and many lack effective therapeutics and/or vaccines. This perspective primarily focuses on the negative-sense RNA viruses from the order Mononegavirales, which obtain their lipid envelope from the host plasma membrane. Specifically, the perspective highlights the common themes of host cell lipid and membrane biology necessary for virus replication, assembly, and budding.     

Deconvoluting lung evolution: from phenotypes to gene regulatory networks

Speakers in this symposium presented examples of respiratoryregulation that broadly illustrate principles of evolution fromwhole organ to genes. The swim bladder and lungs of aquaticand terrestrial organisms arose independently from a commonprimordial "respiratory pharynx" but not from each other. Pathwaysof lung evolution are similar between crocodiles and birds buta low compliance of mammalian lung may have driven the developmentof the diaphragm to permit lung inflation during inspiration.To meet the high oxygen demands of flight, bird lungs have evolvedseparate gas exchange and pump components to achieve unidirectionalventilation and minimize dead space. The process of "screening"(removal of oxygen from inspired air prior to entering the terminalunits) reduces effective alveolar oxygen tension and potentiallyexplains why nonathletic large mammals possess greater pulmonarydiffusing capacities than required by their oxygen consumption.The "primitive" central admixture of oxygenated and deoxygenatedblood in the incompletely divided reptilian heart is actuallyco-regulated with other autonomic cardiopulmonary responsesto provide flexible control of arterial oxygen tension independentof ventilation as well as a unique mechanism for adjusting metabolicrate. Some of the most ancient oxygen-sensing molecules, i.e.,hypoxia-inducible factor-1alpha and erythropoietin, are up-regulatedduring mammalian lung development and growth under apparentlynormoxic conditions, suggesting functional evolution. Normalalveolarization requires pleiotropic growth factors acting viahighly conserved cell–cell signal transduction, e.g.,parathyroid hormone-related protein transducing at least partlythrough the Wingless/int pathway. The latter regulates morphogenesisfrom nematode to mammal. If there is commonality among thesediverse respiratory processes, it is that all levels of organization,from molecular signaling to structure to function, co-evolveprogressively, and optimize an existing gas-exchange framework.