An integrative functional genomics approach for discovering biomarkers in schizophrenia

Schizophrenia (SZ) is a complex disorder resulting from both genetic and environmental causes with a lifetime prevalence world-wide of 1%; however, there are no specific, sensitive and validated biomarkers for SZ. A general unifying hypothesis has been put forward that disease-associated single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) are more likely to be associated with gene expression quantitative trait loci (eQTL). We will describe this hypothesis and review primary methodology with refinements for testing this paradigmatic approach in SZ. We will describe biomarker studies of SZ and testing enrichment of SNPs that are associated both with eQTLs and existing GWAS of SZ. SZ-associated SNPs that overlap with eQTLs can be placed into gene-gene expression, protein-protein and protein-DNA interaction networks. Further, those networks can be tested by reducing/silencing the gene expression levels of critical nodes. We present pilot data to support these methods of investigation such as the use of eQTLs to annotate GWASs of SZ, which could be applied to the field of biomarker discovery. Those networks that have association with SNP markers, especially cis-regulated expression, might lead to a more clear understanding of important candidate genes that predispose to disease and alter expression. This method has general application to many complex disorders.     

Inferring causal phenotype networks from segregating populations

A major goal in the study of complex traits is to decipher the causal interrelationships among correlated phenotypes. Current methods mostly yield undirected networks that connect phenotypes without causal orientation. Some of these connections may be spurious due to partial correlation that is not causal. We show how to build causal direction into an undirected network of phenotypes by including causal QTL for each phenotype. We evaluate causal direction for each edge connecting two phenotypes, using a LOD score. This new approach can be applied to many different population structures, including inbred and outbred crosses as well as natural populations, and can accommodate feedback loops. We assess its performance in simulation studies and show that our method recovers network edges and infers causal direction correctly at a high rate. Finally, we illustrate our method with an example involving gene expression and metabolite traits from experimental crosses.     

An integrative approach to understanding mechanosensation

The ability for a living organism to sense and respond to its external environment is crucial to its survival. Understanding mechanosensation, the mechanism by which organisms react in response to mechanical stimuli, presents many interesting and challenging problems for both experimental and computational biologists. A major difficulty in studying mechanosensors is their inherent multiscale nature. The systems involved in mechanosesnsing can span eight orders of magnitude in length scale and up to 10 orders of magnitude in time scale. Trying to ascertain information across these length and time scales simultaneously is challenging. This problem has led to the need to approach these types of problems using an integrative approach, combining both computational and experimental biology. This review classifies the major types of mechanosensors and explains methods that have been employed in understanding their behavior, both using modeling and experimental techniques. Multiscale modeling methods combined with experimental techniques in an integrative approach are suggested as ways of undertaking the study of such systems.     

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.     

An integrative approach to environmental engineering in zoos

Zoos have shifted recently from the historical tendency to display as many species as possible to an emphasis on improved education and captive propagation. Many enlightened zoo administrations attempt to achieve these goals through creation of exhibits that mimic animals' natural physical and social environments, as extrapolated from field studies. An adjunct is the increased use of technology as a tool in environmental engineering, the term used herein to refer to the design of habitats for captive animals. Recently there has been discussion about the degree of technological control that is appropriate in zoo displays. One progressive group has proposed that natural resources and technology should only be used to enhance displays passively. Their efforts are concentrated on simulation of natural physical and social environments. Another group has suggested that technology should play an active role in the design of captive environments. They attempt to modify displays so that captive animals participate actively to obtain food. This paper first reviews the literature of both groups and suggests an integration of their views. The second section illustrates how both methods may be used to solve problems zoos face with animals during reproduction, development, and adulthood.     

An empirical Bayes approach to inferring large-scale gene association networks

MOTIVATION: Genetic networks are often described statistically using graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standard algorithms for graphical models inapplicable, and inferring genetic networks an 'ill-posed' inverse problem. METHODS: We introduce a novel framework for small-sample inference of graphical models from gene expression data. Specifically, we focus on the so-called graphical Gaussian models (GGMs) that are now frequently used to describe gene association networks and to detect conditionally dependent genes. Our new approach is based on (1) improved (regularized) small-sample point estimates of partial correlation, (2) an exact test of edge inclusion with adaptive estimation of the degree of freedom and (3) a heuristic network search based on false discovery rate multiple testing. Steps (2) and (3) correspond to an empirical Bayes estimate of the network topology. RESULTS: Using computer simulations, we investigate the sensitivity (power) and specificity (true negative rate) of the proposed framework to estimate GGMs from microarray data. This shows that it is possible to recover the true network topology with high accuracy even for small-sample datasets. Subsequently, we analyze gene expression data from a breast cancer tumor study and illustrate our approach by inferring a corresponding large-scale gene association network for 3883 genes.     

Recent speciation and hybridization in Icelandic deep-sea isopods: An integrative approach using genomics and proteomics

The crustacean marine isopod species Haploniscus bicuspis (Sars, 1877) shows circum-Icelandic distribution in a wide range of environmental conditions and along well-known geographic barriers, such as the Greenland-Iceland-Faroe (GIF) Ridge. We wanted to explore population genetics, phylogeography and cryptic speciation as well as investigate whether previously described, but unaccepted subspecies have any merit. Using the same set of specimens, we combined mitochondrial COI sequences, thousands of nuclear loci (ddRAD), and proteomic profiles, plus selected morphological characters using confocal laser scanning microscopy (CLSM). Five divergent genetic lineages were identified by COI and ddRAD, two south and three north of the GIF Ridge. Assignment of populations to the three northern lineages varied and detailed analyses revealed hybridization and gene flow between them, suggesting a single northern species with a complex phylogeographic history. No apparent hybridization was observed among lineages south of the GIF Ridge, inferring the existence of two more species. Differences in proteomic profiles between the three putative species were minimal, implying an ongoing or recent speciation process. Population differentiation was high, even among closely associated populations, and higher in mitochondrial COI than nuclear ddRAD loci. Gene flow is apparently male-biased, leading to hybrid zones and instances of complete exchange of the local nuclear genome through immigrating males. This study did not confirm the existence of subspecies defined by male characters, which probably instead refer to different male developmental stages.     

A novel model-free approach for reconstruction of time-delayed gene regulatory networks

Many important biological processes (e.g. cellular differentiation during development, aging, disease etiology etc.) are very unlikely controlled by a single gene instead by the underlying complex regulatory interactions between thousands of genes within …     

An integrative phylogenetic and extrapolatory approach to the reconstruction of dromaeosaur (Theropoda: Eumaniraptora) shoulder musculature

Dromaeosauridae is the sister taxon of the Avialae; thus, an investigation of dromaeosaur shoulder girdle musculature and forelimb function provides substantial information regarding changes in the size and performance of the theropod shoulder girdle musculature leading to avian powered flight. Twenty-two shoulder girdle muscles were reconstructed for the dromaeosaurid shoulder apparatus, based on phylogenetic inference, which involves the comparison of lepidosaurian, crocodilian and avian musculature, and extrapolatory inference, which involves a secondary comparison with functional analogues of theropods. In addition to these comparative methodologies, osteological correlates of shoulder musculature preserved in eumaniraptorans are identified, and comparisons with those of extant archosaurs allow these muscles to be definitively inferred in dromaeosaurids. This muscle reconstruction provides a foundation for subsequent investigation of differences in muscular attachment and function, based on scapulocoracoid morphology, across the theropod lineage leading to birds.  © 2006 The Linnean Society of London, Zoological Journal of the Linnean Society , 2006, 146 , 301–344.     

On the sparse reconstruction of gene networks

We discuss a heuristic method for the sparse reconstruction of gene networks. The method is based on iterative greedy algorithms, and uses gene expression data from microarray experiments. Also, we show numerically that the greedy algorithms are able to give good approximative solutions to the sparse reconstruction problem even in the presence of significant levels of noise.