Topological defects in spinor condensates

We investigate the structure of topological defects in the ground states of spinor Bose-Einstein condensates with spin F=1 or F=2. The type and number of defects are determined by calculating the first and second homotopy groups of the order-parameter space. The order-parameter space is identified with a set of degenerate ground state spinors. Because the structure of the ground state depends on whether or not there is an external magnetic field applied to the system, defects are sensitive to the magnetic field. We study both cases and find that the defects in zero and non-zero field are different.Comment: 10 pages, 1 figure. Published versio

TRANSFAC(®) and its module TRANSCompel(®): transcriptional gene regulation in eukaryotes

The TRANSFAC(®) database on transcription factors, their binding sites, nucleotide distribution matrices and regulated genes as well as the complementing database TRANSCompel(®) on composite elements have been further enhanced on various levels. A new web interface with different search options and integrated versions of Match™ and Patch™ provides increased functionality for TRANSFAC(®). The list of databases which are linked to the common GENE table of TRANSFAC(®) and TRANSCompel(®) has been extended by: Ensembl, UniGene, EntrezGene, HumanPSD™ and TRANSPRO™. Standard gene names from HGNC, MGI and RGD, are included for human, mouse and rat genes, respectively. With the help of InterProScan, Pfam, SMART and PROSITE domains are assigned automatically to the protein sequences of the transcription factors. TRANSCompel(®) contains now, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based. Finally, for TRANSFAC(®), in respect of data growth, in particular the gain of Drosophila transcription factor binding sites (by courtesy of the Drosophila DNase I footprint database) and of Arabidopsis factors (by courtesy of DATF, Database of Arabidopsis Transcription Factors) has to be stressed. The here described public releases, TRANSFAC(®) 7.0 and TRANSCompel(®) 7.0, are accessible under

Advanced Computational Biology Methods Identify Molecular Switches for Malignancy in an EGF Mouse Model of Liver Cancer

The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs' transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regulated transcription factors to display specificity to either the pre-tumor or the tumor state. Subsequent search for signal transduction key nodes upstream of the identified transcription factors and their targets suggested the insulin-like growth factor pathway to render the tumor cells independent of EGF receptor activity. Notably, expression of IGF2 in addition to many components of this pathway was highly upregulated in tumors. Together, we propose a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF and demonstrate the knowledge gain form promoter analysis combined with upstream key node identification

Leading-effect vs. Risk-taking in Dynamic Tournaments: Evidence from a Real-life Randomized Experiment

Two 'order effects' may emerge in dynamic tournaments with information feedback. First, participants adjust effort across stages, which could advantage the leading participant who faces a larger 'effective prize' after an initial victory (leading-effect). Second, participants lagging behind may increase risk at the final stage as they have 'nothing to lose' (risk-taking). We use a randomized natural experiment in professional two-game soccer tournaments where the treatment (order of a stage-specific advantage) and team characteristics, e.g. ability, are independent. We develop an identification strategy to test for leading-effects controlling for risk-taking. We find no evidence of leading-effects and negligible risk-taking effects

A Bayesian Search for Transcriptional Motifs

Identifying transcription factor (TF) binding sites (TFBSs) is an important step towards understanding transcriptional regulation. A common approach is to use gaplessly aligned, experimentally supported TFBSs for a particular TF, and algorithmically search for more occurrences of the same TFBSs. The largest publicly available databases of TF binding specificities contain models which are represented as position weight matrices (PWM). There are other methods using more sophisticated representations, but these have more limited databases, or aren't publicly available. Therefore, this paper focuses on methods that search using one PWM per TF. An algorithm, MATCHTM, for identifying TFBSs corresponding to a particular PWM is available, but is not based on a rigorous statistical model of TF binding, making it difficult to interpret or adjust the parameters and output of the algorithm. Furthermore, there is no public description of the algorithm sufficient to exactly reproduce it. Another algorithm, MAST, computes a p-value for the presence of a TFBS using true probabilities of finding each base at each offset from that position. We developed a statistical model, BaSeTraM, for the binding of TFs to TFBSs, taking into account random variation in the base present at each position within a TFBS. Treating the counts in the matrices and the sequences of sites as random variables, we combine this TFBS composition model with a background model to obtain a Bayesian classifier. We implemented our classifier in a package (SBaSeTraM). We tested SBaSeTraM against a MATCHTM implementation by searching all probes used in an experimental Saccharomyces cerevisiae TF binding dataset, and comparing our predictions to the data. We found no statistically significant differences in sensitivity between the algorithms (at fixed selectivity), indicating that SBaSeTraM's performance is at least comparable to the leading currently available algorithm. Our software is freely available at: http://wiki.github.com/A1kmm/sbasetram/building-the-tools

G-Protein Coupled Receptor Signaling Architecture of Mammalian Immune Cells

A series of recent studies on large-scale networks of signaling and metabolic systems revealed that a certain network structure often called “bow-tie network” are observed. In signaling systems, bow-tie network takes a form with diverse and redundant inputs and outputs connected via a small numbers of core molecules. While arguments have been made that such network architecture enhances robustness and evolvability of biological systems, its functional role at a cellular level remains obscure. A hypothesis was proposed that such a network function as a stimuli-reaction classifier where dynamics of core molecules dictate downstream transcriptional activities, hence physiological responses against stimuli. In this study, we examined whether such hypothesis can be verified using experimental data from Alliance for Cellular Signaling (AfCS) that comprehensively measured GPCR related ligands response for B-cell and macrophage. In a GPCR signaling system, cAMP and Ca2+ act as core molecules. Stimuli-response for 32 ligands to B-Cells and 23 ligands to macrophages has been measured. We found that ligands with correlated changes of cAMP and Ca2+ tend to cluster closely together within the hyperspaces of both cell types and they induced genes involved in the same cellular processes. It was found that ligands inducing cAMP synthesis activate genes involved in cell growth and proliferation; cAMP and Ca2+ molecules that increased together form a feedback loop and induce immune cells to migrate and adhere together. In contrast, ligands without a core molecules response are scattered throughout the hyperspace and do not share clusters. G-protein coupling receptors together with immune response specific receptors were found in cAMP and Ca2+ activated clusters. Analyses have been done on the original software applicable for discovering ‘bow-tie’ network architectures within the complex network of intracellular signaling where ab initio clustering has been implemented as well. Groups of potential transcription factors for each specific group of genes were found to be partly conserved across B-Cell and macrophage. A series of findings support the hypothesis

Wide-Scale Analysis of Human Functional Transcription Factor Binding Reveals a Strong Bias towards the Transcription Start Site

We introduce a novel method to screen the promoters of a set of genes with shared biological function, against a precompiled library of motifs, and find those motifs which are statistically over-represented in the gene set. The gene sets were obtained from the functional Gene Ontology (GO) classification; for each set and motif we optimized the sequence similarity score threshold, independently for every location window (measured with respect to the TSS), taking into account the location dependent nucleotide heterogeneity along the promoters of the target genes. We performed a high throughput analysis, searching the promoters (from 200bp downstream to 1000bp upstream the TSS), of more than 8000 human and 23,000 mouse genes, for 134 functional Gene Ontology classes and for 412 known DNA motifs. When combined with binding site and location conservation between human and mouse, the method identifies with high probability functional binding sites that regulate groups of biologically related genes. We found many location-sensitive functional binding events and showed that they clustered close to the TSS. Our method and findings were put to several experimental tests. By allowing a "flexible" threshold and combining our functional class and location specific search method with conservation between human and mouse, we are able to identify reliably functional TF binding sites. This is an essential step towards constructing regulatory networks and elucidating the design principles that govern transcriptional regulation of expression. The promoter region proximal to the TSS appears to be of central importance for regulation of transcription in human and mouse, just as it is in bacteria and yeast.Comment: 31 pages, including Supplementary Information and figure

Analysis of Gene Regulatory Networks in the Mammalian Circadian Rhythm

Circadian rhythm is fundamental in regulating a wide range of cellular, metabolic, physiological, and behavioral activities in mammals. Although a small number of key circadian genes have been identified through extensive molecular and genetic studies in the past, the existence of other key circadian genes and how they drive the genomewide circadian oscillation of gene expression in different tissues still remains unknown. Here we try to address these questions by integrating all available circadian microarray data in mammals. We identified 41 common circadian genes that showed circadian oscillation in a wide range of mouse tissues with a remarkable consistency of circadian phases across tissues. Comparisons across mouse, rat, rhesus macaque, and human showed that the circadian phases of known key circadian genes were delayed for 4–5 hours in rat compared to mouse and 8–12 hours in macaque and human compared to mouse. A systematic gene regulatory network for the mouse circadian rhythm was constructed after incorporating promoter analysis and transcription factor knockout or mutant microarray data. We observed the significant association of cis-regulatory elements: EBOX, DBOX, RRE, and HSE with the different phases of circadian oscillating genes. The analysis of the network structure revealed the paths through which light, food, and heat can entrain the circadian clock and identified that NR3C1 and FKBP/HSP90 complexes are central to the control of circadian genes through diverse environmental signals. Our study improves our understanding of the structure, design principle, and evolution of gene regulatory networks involved in the mammalian circadian rhythm

Combined In Silico and In Vivo Analyses Reveal Role of Hes1 in Taste Cell Differentiation

The sense of taste is of critical importance to animal survival. Although studies of taste signal transduction mechanisms have provided detailed information regarding taste receptor calcium signaling molecules (TRCSMs, required for sweet/bitter/umami taste signal transduction), the ontogeny of taste cells is still largely unknown. We used a novel approach to investigate the molecular regulation of taste system development in mice by combining in silico and in vivo analyses. After discovering that TRCSMs colocalized within developing circumvallate papillae (CVP), we used computational analysis of the upstream regulatory regions of TRCSMs to investigate the possibility of a common regulatory network for TRCSM transcription. Based on this analysis, we identified Hes1 as a likely common regulatory factor, and examined its function in vivo. Expression profile analyses revealed that decreased expression of nuclear HES1 correlated with expression of type II taste cell markers. After stage E18, the CVP of Hes1−/− mutants displayed over 5-fold more TRCSM-immunoreactive cells than did the CVP of their wild-type littermates. Thus, according to our composite analyses, Hes1 is likely to play a role in orchestrating taste cell differentiation in developing taste buds

Fast index based algorithms and software for matching position specific scoring matrices

BACKGROUND: In biological sequence analysis, position specific scoring matrices (PSSMs) are widely used to represent sequence motifs in nucleotide as well as amino acid sequences. Searching with PSSMs in complete genomes or large sequence databases is a common, but computationally expensive task. RESULTS: We present a new non-heuristic algorithm, called ESAsearch, to efficiently find matches of PSSMs in large databases. Our approach preprocesses the search space, e.g., a complete genome or a set of protein sequences, and builds an enhanced suffix array that is stored on file. This allows the searching of a database with a PSSM in sublinear expected time. Since ESAsearch benefits from small alphabets, we present a variant operating on sequences recoded according to a reduced alphabet. We also address the problem of non-comparable PSSM-scores by developing a method which allows the efficient computation of a matrix similarity threshold for a PSSM, given an E-value or a p-value. Our method is based on dynamic programming and, in contrast to other methods, it employs lazy evaluation of the dynamic programming matrix. We evaluated algorithm ESAsearch with nucleotide PSSMs and with amino acid PSSMs. Compared to the best previous methods, ESAsearch shows speedups of a factor between 17 and 275 for nucleotide PSSMs, and speedups up to factor 1.8 for amino acid PSSMs. Comparisons with the most widely used programs even show speedups by a factor of at least 3.8. Alphabet reduction yields an additional speedup factor of 2 on amino acid sequences compared to results achieved with the 20 symbol standard alphabet. The lazy evaluation method is also much faster than previous methods, with speedups of a factor between 3 and 330. CONCLUSION: Our analysis of ESAsearch reveals sublinear runtime in the expected case, and linear runtime in the worst case for sequences not shorter than | [Formula: see text] |(m )+ m - 1, where m is the length of the PSSM and [Formula: see text] a finite alphabet. In practice, ESAsearch shows superior performance over the most widely used programs, especially for DNA sequences. The new algorithm for accurate on-the-fly calculations of thresholds has the potential to replace formerly used approximation approaches. Beyond the algorithmic contributions, we provide a robust, well documented, and easy to use software package, implementing the ideas and algorithms presented in this manuscript