bluuebunny/crossref_metadata_2025 huggingface dataset:
Grouped graphical Granger modeling for gene expression regulatory networks discovery
Authors: Lozano Aurélie C. , Abe Naoki , Liu Yan , Rosset Saharon
Published: May 2009

We consider the problem of discovering gene regulatory networks from time-series microarray data. Recently, graphical Granger modeling has gained considerable attention as a promising direction for addressing this problem. These methods apply graphical modeling methods on time-series data and invoke the notion of ‘Granger causality’ to make assertions on causality through inference on time-lagged effects. Existing algorithms, however, have neglected an important aspect of the problem—the group structure among the lagged temporal variables naturally imposed by the time series they belong to. Specifically, existing methods in computational biology share this shortcoming, as well as additional computational limitations, prohibiting their effective applications to the large datasets including a large number of genes and many data points. In the present article, we propose a novel methodology which we term ‘grouped graphical Granger modeling method’, which overcomes the limitations mentioned above by applying a regression method suited for high-dimensional and large data, and by leveraging the group structure among the lagged temporal variables according to the time series they belong to. We demonstrate the effectiveness of the proposed methodology on both simulated and actual gene expression data, specifically the human cancer cell (HeLa S3) cycle data. The simulation results show that the proposed methodology generally exhibits higher accuracy in recovering the underlying causal structure. Those on the gene expression data demonstrate that it leads to improved accuracy with respect to prediction of known links, and also uncovers additional causal relationships uncaptured by earlier works.

Contact:  aclozano@us.ibm.com
Assessing phylogenetic motif models for predicting transcription factor binding sites
Authors: Hawkins John , Grant Charles , Noble William Stafford , Bailey Timothy L.
Published: May 2009

Motivation: A variety of algorithms have been developed to predict transcription factor binding sites (TFBSs) within the genome by exploiting the evolutionary information implicit in multiple alignments of the genomes of related species. One such approach uses an extension of the standard position-specific motif model that incorporates phylogenetic information via a phylogenetic tree and a model of evolution. However, these phylogenetic motif models (PMMs) have never been rigorously benchmarked in order to determine whether they lead to better prediction of TFBSs than obtained using simple position weight matrix scanning.

Results: We evaluate three PMM-based prediction algorithms, each of which uses a different treatment of gapped alignments, and we compare their prediction accuracy with that of a non-phylogenetic motif scanning approach. Surprisingly, all of these algorithms appear to be inferior to simple motif scanning, when accuracy is measured using a gold standard of validated yeast TFBSs. However, the PMM scanners perform much better than simple motif scanning when we abandon the gold standard and consider the number of statistically significant sites predicted, using column-shuffled ‘random’ motifs to measure significance. These results suggest that the common practice of measuring the accuracy of binding site predictors using collections of known sites may be dangerously misleading since such collections may be missing ‘weak’ sites, which are exactly the type of sites needed to discriminate among predictors. We then extend our previous theoretical model of the statistical power of PMM-based prediction algorithms to allow for loss of binding sites during evolution, and show that it gives a more accurate upper bound on scanner accuracy. Finally, utilizing our theoretical model, we introduce a new method for predicting the number of real binding sites in a genome. The results suggest that the number of true sites for a yeast TF is in general several times greater than the number of known sites listed in the Saccharomyces cerevisiae Database (SCPD). Among the three scanning algorithms that we test, the MONKEY algorithm has the highest accuracy for predicting yeast TFBSs.

Contact:  j.hawkins@imb.uq.edu.au
Domain-oriented edge-based alignment of protein interaction networks
Authors: Guo Xin , Hartemink Alexander J.
Published: May 2009

Motivation: Recent advances in high-throughput experimental techniques have yielded a large amount of data on protein–protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment.

Results: Most conventional network alignment algorithms adopt a node-then-edge-alignment paradigm: they first identify homologous proteins across networks and then consider interactions among them to construct network alignments. In this study, we propose an alternative direct-edge-alignment paradigm. Specifically, instead of explicit identification of homologous proteins, we directly infer plausibly alignable PPIs across species by comparing conservation of their constituent domain interactions. We apply our approach to detect conserved protein complexes in yeast–fly and yeast–worm PPI networks, and show that our approach outperforms two recent approaches in most alignment performance metrics.

Availability: Supplementary material and source code can be found at http://www.cs.duke.edu/∼amink/.

Contact:  xinguo@cs.duke.edu
Prediction of sub-cavity binding preferences using an adaptive physicochemical structure representation
Authors: Wallach Izhar , Lilien Ryan H.
Published: May 2009

Motivation: The ability to predict binding profiles for an arbitrary protein can significantly improve the areas of drug discovery, lead optimization and protein function prediction. At present, there are no successful algorithms capable of predicting binding profiles for novel proteins. Existing methods typically rely on manually curated templates or entire active site comparison. Consequently, they perform best when analyzing proteins sharing significant structural similarity with known proteins (i.e. proteins resulting from divergent evolution). These methods fall short when used to characterize the binding profile of a novel active site or one for which a template is not available. In contrast to previous approaches, our method characterizes the binding preferences of sub-cavities within the active site by exploiting a large set of known protein–ligand complexes. The uniqueness of our approach lies not only in the consideration of sub-cavities, but also in the more complete structural representation of these sub-cavities, their parametrization and the method by which they are compared. By only requiring local structural similarity, we are able to leverage previously unused structural information and perform binding inference for proteins that do not share significant structural similarity with known systems.

Results: Our algorithm demonstrates the ability to accurately cluster similar sub-cavities and to predict binding patterns across a diverse set of protein–ligand complexes. When applied to two high-profile drug targets, our algorithm successfully generates a binding profile that is consistent with known inhibitors. The results suggest that our algorithm should be useful in structure-based drug discovery and lead optimization.

Contact:  izharw@cs.toronto.edu; lilien@cs.toronto.edu
Clustered alignments of gene-expression time series data
Authors: Smith Adam A. , Vollrath Aaron , Bradfield Christopher A. , Craven Mark
Published: May 2009

Motivation: Characterizing and comparing temporal gene-expression responses is an important computational task for answering a variety of questions in biological studies. Algorithms for aligning time series represent a valuable approach for such analyses. However, previous approaches to aligning gene-expression time series have assumed that all genes should share the same alignment. Our work is motivated by the need for methods that identify sets of genes that differ in similar ways between two time series, even when their expression profiles are quite different.

Results: We present a novel algorithm that calculates clustered alignments; the method finds clusters of genes such that the genes within a cluster share a common alignment, but each cluster is aligned independently of the others. We also present an efficient new segment-based alignment algorithm for time series called SCOW (shorting correlation-optimized warping). We evaluate our methods by assessing the accuracy of alignments computed with sparse time series from a toxicogenomics dataset. The results of our evaluation indicate that our clustered alignment approach and SCOW provide more accurate alignments than previous approaches. Additionally, we apply our clustered alignment approach to characterize the effects of a conditional Mop3 knockout in mouse liver.

Availability: Source code is available at http://www.biostat.wisc.edu/∼aasmith/catcode.

Contact:  aasmith@cs.wisc.edu
Fewer permutations, more accurate P-values
Authors: Knijnenburg Theo A. , Wessels Lodewyk F. A. , Reinders Marcel J. T. , Shmulevich Ilya
Published: May 2009

Motivation: Permutation tests have become a standard tool to assess the statistical significance of an event under investigation. The statistical significance, as expressed in a P-value, is calculated as the fraction of permutation values that are at least as extreme as the original statistic, which was derived from non-permuted data. This empirical method directly couples both the minimal obtainable P-value and the resolution of the P-value to the number of permutations. Thereby, it imposes upon itself the need for a very large number of permutations when small P-values are to be accurately estimated. This is computationally expensive and often infeasible.

Results: A method of computing P-values based on tail approximation is presented. The tail of the distribution of permutation values is approximated by a generalized Pareto distribution. A good fit and thus accurate P-value estimates can be obtained with a drastically reduced number of permutations when compared with the standard empirical way of computing P-values.

Availability: The Matlab code can be obtained from the corresponding author on request.

Contact:  tknijnenburg@systemsbiology.org

Supplementary information:  Supplementary data are available at Bioinformatics online.
A partition function algorithm for interacting nucleic acid strands
Authors: Chitsaz Hamidreza , Salari Raheleh , Sahinalp S. Cenk , Backofen Rolf
Published: May 2009

Recent interests, such as RNA interference and antisense RNA regulation, strongly motivate the problem of predicting whether two nucleic acid strands interact.

Motivation: Regulatory non-coding RNAs (ncRNAs) such as microRNAs play an important role in gene regulation. Studies on both prokaryotic and eukaryotic cells show that such ncRNAs usually bind to their target mRNA to regulate the translation of corresponding genes. The specificity of these interactions depends on the stability of intermolecular and intramolecular base pairing. While methods like deep sequencing allow to discover an ever increasing set of ncRNAs, there are no high-throughput methods available to detect their associated targets. Hence, there is an increasing need for precise computational target prediction. In order to predict base-pairing probability of any two bases in interacting nucleic acids, it is necessary to compute the interaction partition function over the whole ensemble. The partition function is a scalar value from which various thermodynamic quantities can be derived. For example, the equilibrium concentration of each complex nucleic acid species and also the melting temperature of interacting nucleic acids can be calculated based on the partition function of the complex.

Results: We present a model for analyzing the thermodynamics of two interacting nucleic acid strands considering the most general type of interactions studied in the literature. We also present a corresponding dynamic programming algorithm that computes the partition function over (almost) all physically possible joint secondary structures formed by two interacting nucleic acids in O(n6) time. We verify the predictive power of our algorithm by computing (i) the melting temperature for interacting RNA pairs studied in the literature and (ii) the equilibrium concentration for several variants of the OxyS–fhlA complex. In both experiments, our algorithm shows high accuracy and outperforms competitors.

Availability: Software and web server is available at http://compbio.cs.sfu.ca/taverna/pirna/

Contact:  cenk@cs.sfu.ca; backofen@informatik.uni-freiburg.de

Supplementary information:  Supplementary data are avaliable at Bioinformatics online.
Predicting functionality of protein–DNA interactions by integrating diverse evidence
Authors: Ucar Duygu , Beyer Andreas , Parthasarathy Srinivasan , Workman Christopher T.
Published: May 2009

Chromatin immunoprecipitation (ChIP-chip) experiments enable capturing physical interactions between regulatory proteins and DNA in vivo. However, measurement of chromatin binding alone is not sufficient to detect regulatory interactions. A detected binding event may not be biologically relevant, or a known regulatory interaction might not be observed under the growth conditions tested so far. To correctly identify physical interactions between transcription factors (TFs) and genes and to determine their regulatory implications under various experimental conditions, we integrated ChIP-chip data with motif binding sites, nucleosome occupancy and mRNA expression datasets within a probabilistic framework. This framework was specifically tailored for the identification of functional and non-functional DNA binding events. Using this, we estimate that only 50% of condition-specific protein–DNA binding in budding yeast is functional. We further investigated the molecular factors determining the functionality of protein–DNA interactions under diverse growth conditions. Our analysis suggests that the functionality of binding is highly condition-specific and highly dependent on the presence of specific cofactors. Hence, the joint analysis of both, functional and non-functional DNA binding, may lend important new insights into transcriptional regulation.

Contact:  workman@cbs.dtu.dk
Modeling stochasticity and robustness in gene regulatory networks
Authors: Garg Abhishek , Mohanram Kartik , Di Cara Alessandro , De Micheli Giovanni , Xenarios Ioannis
Published: May 2009

Motivation: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations.

Results: In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs.

Availability: Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/∼garg/genysis.html.

Contact:  abhishek.garg@epfl.ch
Computing galled networks from real data
Authors: Huson Daniel H. , Rupp Regula , Berry Vincent , Gambette Philippe , Paul Christophe
Published: May 2009

Motivation: Developing methods for computing phylogenetic networks from biological data is an important problem posed by molecular evolution and much work is currently being undertaken in this area. Although promising approaches exist, there are no tools available that biologists could easily and routinely use to compute rooted phylogenetic networks on real datasets containing tens or hundreds of taxa. Biologists are interested in clades, i.e. groups of monophyletic taxa, and these are usually represented by clusters in a rooted phylogenetic tree. The problem of computing an optimal rooted phylogenetic network from a set of clusters, is hard, in general. Indeed, even the problem of just determining whether a given network contains a given cluster is hard. Hence, some researchers have focused on topologically restricted classes of networks, such as galled trees and level-k networks, that are more tractable, but have the practical draw-back that a given set of clusters will usually not possess such a representation.

Results: In this article, we argue that galled networks (a generalization of galled trees) provide a good trade-off between level of generality and tractability. Any set of clusters can be represented by some galled network and the question whether a cluster is contained in such a network is easy to solve. Although the computation of an optimal galled network involves successively solving instances of two different NP-complete problems, in practice our algorithm solves this problem exactly on large datasets containing hundreds of taxa and many reticulations in seconds, as illustrated by a dataset containing 279 prokaryotes.

Availability: We provide a fast, robust and easy-to-use implementation of this work in version 2.0 of our tree-handling software Dendroscope, freely available from http://www.dendroscope.org.

Contact:  huson@informatik.uni-tuebingen.de
A framework to refine particle clusters produced by EMAN
Authors: Fan Liya , Zhang Fa , Wang Gongming , Liu Zhiyong
Published: May 2009

Motivation: EMAN is one of the most popular software packages for single particle reconstruction. But the particle clusters produced during its model refining stage are of low qualities. We attempt to refine the particle clusters by more accurately determining orientations of particles, and thereby achieving higher resolutions of consequent 3D structures.

Results: A particle reclustering framework (PRF) is introduced, which consists of three components. Each of them is responsible for one of the basic tasks of PRF: normalization, threshold determination and reclustering. Our implementation is also described and proved to meet the constraints proposed by PRF. Experiments revealed that our implementation improved resolutions of consequent structures for most cases, but only a little extra execution time was incurred. Therefore, it is practical to incorporate PRF in EMAN to improve qualities of generated 3D structures.

Availability and Implementation: Implementation of our algorithm is available upon request from the authors.

Contact:  fanliya@ict.ac.cn; zf@ncic.ac.cn
A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery
Authors: Xie Lei , Xie Li , Bourne Philip E.
Published: May 2009

Functional relationships between proteins that do not share global structure similarity can be established by detecting their ligand-binding-site similarity. For a large-scale comparison, it is critical to accurately and efficiently assess the statistical significance of this similarity. Here, we report an efficient statistical model that supports local sequence order independent ligand–binding-site similarity searching. Most existing statistical models only take into account the matching vertices between two sites that are defined by a fixed number of points. In reality, the boundary of the binding site is not known or is dependent on the bound ligand making these approaches limited. To address these shortcomings and to perform binding-site mapping on a genome-wide scale, we developed a sequence-order independent profile–profile alignment (SOIPPA) algorithm that is able to detect local similarity between unknown binding sites a priori. The SOIPPA scoring integrates geometric, evolutionary and physical information into a unified framework. However, this imposes a significant challenge in assessing the statistical significance of the similarity because the conventional probability model that is based on fixed-point matching cannot be applied. Here we find that scores for binding-site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version. Efficient statistical analysis makes it possible to apply SOIPPA to genome-based drug discovery. Consequently, we have applied the approach to the structural genome of Mycobacterium tuberculosis to construct a protein–ligand interaction network. The network reveals highly connected proteins, which represent suitable targets for promiscuous drugs.

Contact:  lxie@sdsc.edu
Viruses selectively mutate their CD8+ T-cell epitopes—a large-scale immunomic analysis
Authors: Vider-Shalit Tal , Sarid Ronit , Maman Kobi , Tsaban Lea , Levi Ran , Louzoun Yoram
Published: May 2009

Motivation: Viruses employ various means to evade immune detection. One common evasion strategy is the removal of CD8+cytotoxic T-lymphocyte epitopes. We here use a combination of multiple bioinformatic tools and large amount of genomic data to compute the epitope repertoire presented by over 1300 viruses in many HLA alleles. We define the ‘Size of Immune Repertoire score’, which represents the ratio between the epitope density within a protein and the expected density. This score is used to study viral immune evasion.

Results: We show that viral proteins in general have a higher epitope density than human proteins. This difference is due to a good fit of the human MHC molecules to the typical amino-acid usage of viruses. Among different viruses, viruses infecting humans present less epitopes than non-human viruses. This selection is not at the amino-acid usage level, but through the removal of specific epitopes. Within a single virus, not all proteins express the same epitopes density. Proteins expressed early in the viral life cycle have a lower epitope density than late proteins. Such a difference is not observed in non-human viruses. The removal of early epitopes and the targeting of the cellular immune response to late viral proteins, allow the virus a time interval to propagate before its host cells are destroyed by T cells.

Contact:  louzouy@math.biu.ac.il
Constrained mixture estimation for analysis and robust classification of clinical time series
Authors: Costa Ivan G. , Schönhuth Alexander , Hafemeister Christoph , Schliep Alexander
Published: May 2009

Motivation: Personalized medicine based on molecular aspects of diseases, such as gene expression profiling, has become increasingly popular. However, one faces multiple challenges when analyzing clinical gene expression data; most of the well-known theoretical issues such as high dimension of feature spaces versus few examples, noise and missing data apply. Special care is needed when designing classification procedures that support personalized diagnosis and choice of treatment. Here, we particularly focus on classification of interferon-β (IFNβ) treatment response in Multiple Sclerosis (MS) patients which has attracted substantial attention in the recent past. Half of the patients remain unaffected by IFNβ treatment, which is still the standard. For them the treatment should be timely ceased to mitigate the side effects.

Results: We propose constrained estimation of mixtures of hidden Markov models as a methodology to classify patient response to IFNβ treatment. The advantages of our approach are that it takes the temporal nature of the data into account and its robustness with respect to noise, missing data and mislabeled samples. Moreover, mixture estimation enables to explore the presence of response sub-groups of patients on the transcriptional level. We clearly outperformed all prior approaches in terms of prediction accuracy, raising it, for the first time, >90%. Additionally, we were able to identify potentially mislabeled samples and to sub-divide the good responders into two sub-groups that exhibited different transcriptional response programs. This is supported by recent findings on MS pathology and therefore may raise interesting clinical follow-up questions.

Availability: The method is implemented in the GQL framework and is available at http://www.ghmm.org/gql. Datasets are available at http://www.cin.ufpe.br/∼igcf/MSConst

Contact:  igcf@cin.ufpe.br

Supplementary information:  Supplementary data are available at Bioinformatics online.
Speeding up HMM algorithms for genetic linkage analysis via chain reductions of the state space
Authors: Geiger Dan , Meek Christopher , Wexler Ydo
Published: May 2009

We develop an hidden Markov model (HMM)-based algorithm for computing exact parametric and non-parametric linkage scores in larger pedigrees than was possible before. The algorithm is applicable whenever there are chains of persons in the pedigree with no genetic measurements and with unknown affection status. The algorithm is based on shrinking the state space of the HMM considerably using such chains. In a two g-degree cousins pedigree the reduction drops the state space from being exponential in g to being linear in g. For a Finnish family in which two affected children suffer from a rare cold-inducing sweating syndrome, we were able to reduce the state space by more than five orders of magnitude from 250 to 232. In another pedigree of state-space size of 227, used for a study of pituitary adenoma, the state space reduced by a factor of 8.5 and consequently exact linkage scores can now be computed, rather than approximated.

Contact:  dang@cs.technion.ac.il

Supplementary information:  Supplementary data are available at Bioinformatics online.
Multi-locus match probability in a finite population: a fundamental difference between the Moran and Wright–Fisher models
Authors: Bhaskar Anand , Song Yun S.
Published: May 2009

Motivation: A fundamental problem in population genetics, which being also of importance to forensic science, is to compute the match probability (MP) that two individuals randomly chosen from a population have identical alleles at a collection of loci. At present, 11–13 unlinked autosomal microsatellite loci are typed for forensic use. In a finite population, the genealogical relationships of individuals can create statistical non-independence of alleles at unlinked loci. However, the so-called product rule, which is used in courts in the USA, computes the MP for multiple unlinked loci by assuming statistical independence, multiplying the one-locus MPs at those loci. Analytically testing the accuracy of the product rule for more than five loci has hitherto remained an open problem.

Results: In this article, we adopt a flexible graphical framework to compute multi-locus MPs analytically. We consider two standard models of random mating, namely the Wright–Fisher (WF) and Moran models. We succeed in computing haplotypic MPs for up to 10 loci in the WF model, and up to 13 loci in the Moran model. For a finite population and a large number of loci, we show that the MPs predicted by the product rule are highly sensitive to mutation rates in the range of interest, while the true MPs computed using our graphical framework are not. Furthermore, we show that the WF and Moran models may produce drastically different MPs for a finite population, and that this difference grows with the number of loci and mutation rates. Although the two models converge to the same coalescent or diffusion limit, in which the population size approaches infinity, we demonstrate that, when multiple loci are considered, the rate of convergence in the Moran model is significantly slower than that in the WF model.

Availability: A C++ implementation of the algorithms discussed in this article is available at http://www.cs.berkeley.edu/∼yss/software.html.

Contact:  yss@eecs.berkeley.edu
Genotype–phenotype associations: substitution models to detect evolutionary associations between phenotypic variables and genotypic evolutionary rate
Authors: O'Connor Timothy D. , Mundy Nicholas I.
Published: May 2009

Motivation: Mapping between genotype and phenotype is one of the primary goals of evolutionary genetics but one that has received little attention at the interspecies level. Recent developments in phylogenetics and statistical modelling have typically been used to examine molecular and phenotypic evolution separately. We have used this background to develop phylogenetic substitution models to test for associations between evolutionary rate of genotype and phenotype. We do this by creating hybrid rate matrices between genotype and phenotype.

Results: Simulation results show our models to be accurate in detecting genotype–phenotype associations and robust for various factors that typically affect maximum likelihood methods, such as number of taxa, level of relevant signal, proportion of sites affected and length of evolutionary divergence. Further, simulations show that our method is robust to homogeneity assumptions. We apply the models to datasets of male reproductive system genes in relation to mating systems of primates. We show that evolution of semenogelin II is significantly associated with mating systems whereas two negative control genes (cytochrome b and peptidase inhibitor 3) show no significant association. This provides the first hybrid substitution model of which we are aware to directly test the association between genotype and phenotype using a phylogenetic framework.

Availability: Perl and HYPHY scripts are available upon request from the authors.

Contact:  to252@cam.ac.uk

Supplementary information:  Supplementary data are available at Bioinformatics online.
An introduction to the proceedings and a synthesis of the 2008 ICES Symposium on the Ecosystem Approach with Fisheries Acoustics and Complementary Technologies (SEAFACTS)
Authors: Demer David A. , Kloser Rudy J. , MacLennan David N. , Ona Egil
Published: May 2009

Demer, D. A., Kloser, R. J., MacLennan, D. N., and Ona, E. 2009. An introduction to the proceedings and a synthesis of the 2008 ICES Symposium on the Ecosystem Approach with Fisheries Acoustics and Complementary Technologies (SEAFACTS). – ICES Journal of Marine Science, 66: 961–965.
Body size differences do not arise from divergent mate preferences in a species pair of threespine stickleback
Authors: Head Megan L , Price Emily A. , Boughman Janette W.
Published: May 2009

Ecological speciation can be driven by divergent natural and/or sexual selection. The relative contribution of these processes to species divergence, however, is unknown. Here, we investigate how sexual selection in the form of male and female mate preferences contributes to divergence of body size. This trait is known be under divergent natural selection and also contributes to sexual isolation in species pairs of threespine sticklebacks (Gasterosteus aculeatus). We show that neither female nor male size preferences contribute to body size divergence in this species pair, suggesting that size-based sexual isolation arises primarily through natural selection.
Modelling amorphous computations with transcription networks
Authors: Simpson Zack Booth , Tsai Timothy L. , Nguyen Nam , Chen Xi , Ellington Andrew D.
Published: May 2009

The power of electronic computation is due in part to the development of modular gate structures that can be coupled to carry out sophisticated logical operations and whose performance can be readily modelled. However, the equivalences between electronic and biochemical operations are far from obvious. In order to help cross between these disciplines, we develop an analogy between complementary metal oxide semiconductor and transcriptional logic gates. We surmise that these transcriptional logic gates might prove to be useful in amorphous computations and model the abilities of immobilized gates to form patterns. Finally, to begin to implement these computations, we design unique hairpin transcriptional gates and then characterize these gates in a binary latch similar to that already demonstrated by Kim
et al
. (Kim, White & Winfree 2006
Mol. Syst. Biol.
2
, 68 (doi:10.1038/msb4100099)). The hairpin transcriptional gates are uniquely suited to the design of a complementary NAND gate that can serve as an underlying basis of molecular computing that can output matter rather than electronic information.
Towards the engineering of
in vitro
systems
Authors: Hold Christoph , Panke Sven
Published: May 2009

Synthetic biology aims at rationally implementing biological systems from scratch. Given the complexity of living systems and our current lack of understanding of many aspects of living cells, this is a major undertaking. The design of
in vitro
systems can be considerably easier, because they can consist of fewer constituents, are quasi time invariant, their parameter space can be better accessed and they can be much more easily perturbed and then analysed chemically and mathematically. However, even for simplified
in vitro
systems, following a comprehensively rational design procedure is still difficult. When looking at a comparatively simple system, such as a medium-sized enzymatic reaction network as it is represented by glycolysis, major issues such as a lack of comprehensive enzyme kinetics and of suitable knowledge on crucial design parameters remain. Nevertheless,
in vitro
systems are very suitable to overcome these obstacles and therefore well placed to act as a stepping stone to engineering living systems.
A double-layer Boussinesq-type model for highly nonlinear and dispersive waves
Authors: Chazel F. , Benoit M. , Ern A. , Piperno S.
Published: May 2009

We derive and analyse, in the framework of the mild-slope approximation, a new double-layer Boussinesq-type model that is linearly and nonlinearly accurate up to deep water. Assuming the flow to be irrotational, we formulate the problem in terms of the velocity potential, thereby lowering the number of unknowns. The model derivation combines two approaches, namely the method proposed by Agnonet al.(Agnonet al.1999J. Fluid Mech.399, 319–333) and enhanced by Madsenet al.(Madsenet al.2003Proc. R. Soc. Lond. A459, 1075–1104), which consists of constructing infinite-series Taylor solutions to the Laplace equation, to truncate them at a finite order and to use Padé approximants, and the double-layer approach of Lynett & Liu (Lynett & Liu 2004aProc. R. Soc. Lond. A460, 2637–2669), which allows lowering the order of derivatives. We formulate the model in terms of a static Dirichlet–Neumann operator translated from the free surface to the still-water level, and we derive an approximate inverse of this operator that can be built once and for all. The final model consists of only four equations both in one and two horizontal dimensions, and includes only second-order derivatives, which is a major improvement in comparison with so-called high-order Boussinesq models. A linear analysis of the model is performed, and its properties are optimized using a free parameter determining the position of the interface between the two layers. Excellent dispersion and shoaling properties are obtained, allowing the model to be applied up to the deep-water valuekh=10. Finally, numerical simulations are performed to quantify the nonlinear behaviour of the model, and the results exhibit a nonlinear range of validity reaching at leastkh=3π.
Comparative evolution of flower and fruit morphology
Authors: Whitney Kenneth D.
Published: May 2009

Angiosperm diversification has resulted in a vast array of plant morphologies. Only recently has it been appreciated that diversification might have proceeded quite differently for the two key diagnostic structures of this clade, flowers and fruits. These structures are hypothesized to have experienced different selective pressures via their interactions with animals in dispersal mutualisms, resulting in a greater amount of morphological diversification in animal-pollinated flowers than in animal-dispersed fruits. I tested this idea using size and colour traits for the flowers and fruits of 472 species occurring in three floras (St John, Hawaii and the Great Plains). Phylogenetically controlled analyses of nearest-neighbour distances in multidimensional trait space matched the predicted pattern: in each of the three floras, flowers were more divergent from one another than were fruits. In addition, the spacing of species clusters differed for flowers versus fruits in the flora of St John, with clusters in flower space more divergent than those in fruit space. The results are consistent with the idea that a major driver of angiosperm diversification has been stronger selection for divergent floral morphology than for divergent fruit morphology, although genetic, physiological and ecological constraints may also play a role.
Bet-hedging as an evolutionary game: the trade-off between egg size and number
Authors: Olofsson Helen , Ripa Jörgen , Jonzén Niclas
Published: May 2009

Bet-hedging theory addresses how individuals should optimize fitness in varying and unpredictable environments by sacrificing mean fitness to decrease variation in fitness. So far, three main bet-hedging strategies have been described: conservative bet-hedging (play it safe), diversified bet-hedging (don’t put all eggs in one basket) and adaptive coin flipping (choose a strategy at random from a fixed distribution). Within this context, we analyse the trade-off between many small eggs (or seeds) and few large, given an unpredictable environment. Our model is an extension of previous models and allows for any combination of the bet-hedging strategies mentioned above. In our individual-based model (accounting for both ecological and evolutionary forces), the optimal bet-hedging strategy is a combination of conservative and diversified bet-hedging and adaptive coin flipping, which means a variation in egg size both within clutches and between years. Hence, we show how phenotypic variation within a population, often assumed to be due to non-adaptive variation, instead can be the result of females having this mixed strategy. Our results provide a new perspective on bet-hedging and stress the importance of extreme events in life history evolution.
Refining the Definition of Plant Mitochondrial Presequences through Analysis of Sorting Signals, N-Terminal Modifications, and Cleavage Motifs
Authors: Huang Shaobai , Taylor Nicolas L. , Whelan James , Millar A. Harvey
Published: May 2009

Mitochondrial protein import is a complex multistep process from synthesis of proteins in the cytosol, recognition by receptors on the organelle surface, to translocation across one or both mitochondrial membranes and assembly after removal of the targeting signal, referred to as a presequence. In plants, import has to further discriminate between mitochondria and chloroplasts. In this study, we determined the precise cleavage sites in the presequences for Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) mitochondrial proteins using mass spectrometry by comparing the precursor sequences with experimental evidence of the amino-terminal peptide from mature proteins. We validated this method by assessments of false-positive rates and comparisons with previous available data using Edman degradation. In total, the cleavable presequences of 62 proteins from Arabidopsis and 52 proteins from rice mitochondria were determined. None of these proteins contained amino-terminal acetylation, in contrast to recent findings for chloroplast stromal proteins. Furthermore, the classical matrix glutamate dehydrogenase was detected with intact and amino-terminal acetylated sequences, indicating that it is imported into mitochondria without a cleavable targeting signal. Arabidopsis and rice mitochondrial presequences had similar isoelectric points, hydrophobicity, and the predicted ability to form an amphiphilic α-helix at the amino-terminal region of the presequence, but variations in length, amino acid composition, and cleavage motifs for mitochondrial processing peptidase were observed. A combination of lower hydrophobicity and start point of the amino-terminal α-helix in mitochondrial presequences in both Arabidopsis and rice distinguished them (98%) from Arabidopsis chloroplast stroma transit peptides. Both Arabidopsis and rice mitochondrial cleavage sites could be grouped into three classes, with conserved −3R (class II) and −2R (class I) or without any conserved (class III) arginines. Class II was dominant in both Arabidopsis and rice (55%–58%), but in rice sequences there was much less frequently a phenylalanine (F) in the −1 position of the cleavage site than in Arabidopsis sequences. Our data also suggest a novel cleavage motif of (F/Y)↓(S/A) in plant class III sequences.
Primary dermal melanoma: A case report and molecular characterization
Authors: HIDA Yasutoshi , KUBO Yoshiaki , MIYAJIMA Osamu , ARASE Seiji
Published: May 2009

Swetter et al. proposed primary dermal melanoma (PDM) as a distinct entity based on an excellent prognosis. The histopathological features of PDM are extremely similar to those of metastatic melanoma or clear cell sarcoma (CCS). We describe a 38‐year‐old woman with a subcutaneous tumor in her left thigh. Physical and imaging examinations showed no evidence of metastatic melanoma. The lesion showed obvious strong expression of KIT by immunohistochemistry, but no EWS‐ATF1 fusion transcript specific for CCS was detected by reverse transcription polymerase chain reaction. In further analyses of KIT expression in other tumors, three of four primary melanomas (75%) and six of 12 metastatic melanomas (50%) were moderately or strongly positive, however, both the primary and metastatic lesions of CCS tested negative. We believe this to be a case of PDM, and emphasize the distinctiveness of PDM.
Antibody‐based proteomics for esophageal cancer: Identification of proteins in the nuclear factor‐κB pathway and mitotic checkpoint
Authors: Uemura Norihisa , Nakanishi Yukihiro , Kato Hoichi , Nagino Masato , Hirohashi Setsuo , Kondo Tadashi
Published: May 2009

To identify the molecular background of esophageal cancer, we conducted a proteomics study using an antibody microarray consisting of 725 antibodies and surgical specimens from three cases. The microarray analysis identified 24 proteins with aberrant expression in esophageal cancer compared with the corresponding normal mucosa. The overexpression of 14 of the 24 proteins was validated by western blotting analysis of the same samples. These 14 proteins were examined by immunohistochemistry, in which nine proteins showed consistent results with those obtained by western blotting. Among the nine proteins, seven were localized in tumor cells, and two in infiltrating cells. The former included proteins associated with mitotic checkpoint control and the nuclear factor (NF)‐κB pathway. Although mitotic checkpoint gene products (budding uninhibited by benzidazoles 1 homolog beta (BubR1) and mitotic arrest deficient‐like 1 (Mad2)) have previously been reported to be involved in esophageal cancer, the association of NF‐κB‐activating kinase, caspase 10, and activator protein‐1 with esophageal cancer has not been previously reported. These proteins play a key role in the NF‐κB pathway, and NF‐κB is a signal transduction factor that has emerged as an important modulator of altered gene programs and malignant phenotype in the development of cancer. The association of these proteins with esophageal cancer may indicate that mitotic checkpoint gene products and NF‐κB play an important part in the carcinogenesis of esophageal cancer. (Cancer Sci 2009; 100: 1612–1622)
Clinical features of ileal pouch‐anal anastomosis in African American patients with underlying ulcerative colitis
Authors: MOORE L. , TANG L. , LOPEZ R. , SHEN B.
Published: May 2009

Background  The prevalence of inflammatory bowel disease in African Americans appears to be increasing. The data on differences in disease behavior and severity between the races have been conflicting.

Aim  To evaluate the effect of race on outcome and natural history of patients with ileal pouch‐anal anastomosis.

Methods  All African American patients with underlying ulcerative colitis and ileal pouch‐anal anastomosis who were seen in our subspecialty Pouchitis Clinic from 2002 to 2008 were included. The control group consisted of Caucasian patients with ulcerative colitis and ileal pouch‐anal anastomosis who were randomly selected from the same Pouch Registry at a ratio of 4:1. We compared pouch failure, Crohn’s disease of the pouch, and chronic pouchitis rates, as well as other 23 demographic and clinical variables between African American and Caucasian patients.

Results  A total of 12 African American patients and 48 Caucasian patients were evaluated in this case‐control study. There were no significant differences in the frequency of pouch failure, Crohn’s disease of the pouch, or chronic pouchitis between the African American and Caucasian groups. However, African American patients were found to have a significantly shorter duration of inflammatory bowel disease (11.5 years vs. 17.0 years, P = 0.024) as well as significantly shorter duration of pouch (1.5 years vs. 4 years, P = 0.02). African Americans were also less likely to have pancolitis at the time of colectomy (83% vs. 100%, P = 0.037).

Conclusions  While there were no significant differences in pouch outcomes between the races, African American patients appeared to have more left‐sided colitis at the time of colectomy, with a shorter duration of inflammatory bowel and ileal pouch. This finding suggests that the natural history of ulcerative colitis and disease course before and after restorative proctocolectomy may be different between these racial groups.
A new ‘total’ activin B enzyme‐linked immunosorbent assay (ELISA): development and validation for human samples
Authors: Ludlow Helen , Phillips David J. , Myers Michelle , McLachlan Robert I. , De Kretser David M. , Allan Carolyn A. , Anderson Richard A. , Groome Nigel P. , Hyvönen Marko , Colin Duncan W. , Muttukrishna Shanthi
Published: May 2009

Background and objective  There are currently no sensitive and specific assays for activin B that could be utilized to study human biological fluids. The aim of this project was to develop and validate a ‘total’ activin B ELISA for use with human biological fluids and establish concentrations of activin B in the circulation and fluids from the reproductive organs.

Design  The new ELISA was validated and then used to measure activin B levels in the circulation of healthy participants, IVF patients, pregnant women and in ovarian follicular fluid and seminal plasma.

Patients and measurements  Healthy adult subjects (n = 143), subjects from an IVF clinic (n = 27) and pregnancy groups (n = 29) were sampled.

Results  The sensitivity of the assay was 0·019 ng/ml. Validation of the activin B ELISA showed good recovery (90·7 ± 9·8%) and linearity in biological fluid and cell culture media and low cross‐reactivity with related analytes (inhibin B = 0·077% and activin A = 0·0034%). There was a negative correlation between activin B concentration (r = −0·281, P < 0·011) and females with increasing age. Patients attending IVF clinics had significantly lower levels of activin B compared with gender‐matched control subjects. Ovarian follicular fluid and seminal plasma had 50–80 fold higher levels of activin B (mean = 5·35 and 3·66 ng/ml respectively) than sera (mean = 0·071 ng/ml).

Conclusions  This fully validated ELISA for activin B offers a tremendous utility for measuring this protein in a variety of normal physiological processes and in various clinical pathologies.