How many membrane proteins are there?

One of the basic issues that arises in functional genomics is the ability to predict the subcellular location of proteins that are deduced from gene and genome sequencing. In particular, one would like to be able to readily specify those proteins that are soluble and those that are inserted in a membrane. Traditional methods of distinguishing between these two locations have relied on extensive, time-consuming biochemical studies. The alternative approach has been to make inferences based on a visual search of the amino acid sequences of presumed gene products for stretches of hydrophobic amino acids. This numerical, sequence-based approach is usually seen as a first approximation pending more reliable biochemical data. The recent availability of large and complete sequence data sets for several organisms allows us to determine just how accurate such a numerical approach could be, and to attempt to minimize and quantify the error involved. We have optimized a statistical approach to protein location determination. Using our approach, we have determined that surprisingly few proteins are misallocated using the numerical method. We also examine the biological implications of the success of this technique.     

Identification of tyrosine phosphatases that dephosphorylate the insulin receptor. A brute force approach based on "substrate-trapping" mutants

Many pharmacologically important receptors, including all cytokine receptors, signal via tyrosine (auto)phosphorylation, followed by resetting to their original state through the action of protein tyrosine phosphatases (PTPs). Establishing the specificity of PTPs for receptor substrates is critical both for understanding how signaling is regulated and for the development of specific PTP inhibitors that act as ligand mimetics. We have set up a systematic approach for finding PTPs that are specific for a receptor and have validated this approach with the insulin receptor kinase. We have tested nearly all known human PTPs (45) in a membrane binding assay, using "substrate-trapping" PTP mutants. These results, combined with secondary dephosphorylation tests, confirm and extend earlier findings that PTP-1b and T-cell PTP are physiological enzymes for the insulin receptor kinase. We demonstrate that this approach can rapidly reduce the number of PTPs that have a particular receptor or other phosphoprotein as their substrate.     

Predicted fitness consequences of threat-sensitive hiding behavior

In studies of refuge use as a form of antipredator behavior,where prey hide in response to a predator's approach, factorssuch as foraging costs and the perceived risk in a predator'sapproach have been shown to influence the hiding behavior ofprey. Because few studies of waiting games have focused on mammals,we studied the hiding behavior of the yellow-bellied marmot(Marmota flaviventris), a ground-dwelling rodent. We testedthe prediction that marmots vary hiding time as a function ofpredator approach speed and presence and absence of food outsidetheir refuge and that marmots hide differently depending ontheir relative condition. We conducted "fast approaches" and"slow approaches" in the presence and absence of extra foodand evaluated hiding times. Multiple regression analyses demonstratedthat the interaction between the approach speed and the presenceand absence of food influenced hiding behavior; body conditionhad a smaller, but nonsignificant effect. We then developeda state-dependent dynamic model to explore potential fitnessconsequences of these decisions. The model suggested that theoverall survival of a population is substantially reduced whenindividuals make suboptimal decisions. Our research builds onprevious studies, indicating that animals integrate both costsand benefits of hiding when determining their hiding times.     

Oligonucleotide fingerprint identification for microarray-based pathogen diagnostic assays

MOTIVATION: Advances in DNA microarray technology and computational methods have unlocked new opportunities to identify 'DNA fingerprints', i.e. oligonucleotide sequences that uniquely identify a specific genome. We present an integrated approach for the computational identification of DNA fingerprints for design of microarray-based pathogen diagnostic assays. We provide a quantifiable definition of a DNA fingerprint stated both from a computational as well as an experimental point of view, and the analytical proof that all in silico fingerprints satisfying the stated definition are found using our approach. RESULTS: The presented computational approach is implemented in an integrated high-performance computing (HPC) software tool for oligonucleotide fingerprint identification termed TOFI. We employed TOFI to identify in silico DNA fingerprints for several bacteria and plasmid sequences, which were then experimentally evaluated as potential probes for microarray-based diagnostic assays. Results and analysis of approximately 150 in silico DNA fingerprints for Yersinia pestis and 250 fingerprints for Francisella tularensis are presented. AVAILABILITY: The implemented algorithm is available upon request.     

Importance of hydrophobic cluster formation through long-range contacts in the folding transition state of two-state proteins

Understanding the folding pathways of proteins is a challenging task. The Phi value approach provides a detailed understanding of transition-state structures of folded proteins. In this work, we have computed the hydrophobicity associated with each residue in the folded state of 16 two-state proteins and compared the Phi values of each mutant residue. We found that most of the residues with high Phi value coincide with local maximum in surrounding hydrophobicity, or have nearby residues that show such maximum in hydrophobicity, indicating the importance of hydrophobic interactions in the transition state. We have tested our approach to different structural classes of proteins, such as alpha-helical, SH3 domains of all-beta proteins, beta-sandwich, and alpha/beta proteins, and we observed a good agreement with experimental results. Further, we have proposed a hydrophobic contact network pattern to relate the Phi values with long-range contacts, which will be helpful to understand the transition-state structures of folded proteins. The present approach could be used to identify potential hydrophobic clusters that may form through long-range contacts during the transition state.     

Parametric approach to genomic imprinting analysis with applications to Angelman's syndrome

Genomic imprinting is a mechanism by which only one copy of a gene pair is expressed, and this expression is determined by the parental origin of the copy. The deregulation of imprinted genes has been implicated in a number of human diseases. The Imprinted Gene Catalogue now has more than 200 genes listed, and estimates based on mouse models suggest many more may exist in humans. Therefore, the development of methods to identify such genes is important. In this communication, we present a parametric model-based approach to analyzing arbitrary-sized pedigree data for genomic imprinting. We have modified widely used LINKAGE program to incorporate our proposed approach. In addition, our approach allows for the use of sex-specific recombinations in the analysis, which is of particular importance in a genome-wide analysis for imprinted genes. We compared our imprinting analysis approach to that implemented in the GENEHUNTER-IMPRINT program using simulation studies as well as by analyzing causal genes in Angelman's syndrome families, which are known to be imprinted. These analyses showed that the proposed approach is very powerful for detecting imprinted genes in large pedigrees.     

Petri net modeling of high-order genetic systems using grammatical evolution

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.     

Nonlinear biological oscillators: Action-angle variable approach with an application to Cowan's model of neuroelectric activity

We obtain within the action-angle variable approach new expressions, involving the Dirac delta function, for time periods and time averages of dynamical variables which are useful for nonlinear biological oscillator problems. We combine these with Laplace transformation techniques for evaluating the required perturbation expansions. The radii of convergence of these series are determined through a complex variable approach. The method is powerful enough to yield explicit results for such systems as the two species Volterra model, Goodwin's model of protein synthesis etc. and as an illustration, is applied here to Cowan's model of neuroelectric activity. We also point out the usefulness of the action integral in the case where parameters occurring in dynamics have slow time variations.     

Development of Approaches to Improve Cell Survival in Myoblast Transfer Therapy

Myoblast transplantation has been extensively studied as a gene complementation approach for genetic diseases such as Duchenne Muscular Dystrophy. This approach has been found capable of delivering dystrophin, the product missing in Duchenne Muscular Dystrophy muscle, and leading to an increase of strength in the dystrophic muscle. This approach, however, has been hindered by numerous limitations, including immunological problems, and low spread and poor survival of the injected myoblasts. We have investigated whether antiinflammatory treatment and use of different populations of skeletal muscle–derived cells may circumvent the poor survival of the injected myoblasts after implantation. We have observed that different populations of muscle-derived cells can be isolated from skeletal muscle based on their desmin immunoreactivity and differentiation capacity. Moreover, these cells acted differently when injected into muscle: 95% of the injected cells in some populations died within 48 h, while others richer in desmin-positive cells survived entirely. Since pure myoblasts obtained from isolated myofibers and myoblast cell lines also displayed a poor survival rate of the injected cells, we have concluded that the differential survival of the populations of muscle-derived cells is not only attributable to their content in desmin-positive cells. We have observed that the origin of the myogenic cells may influence their survival in the injected muscle. Finally, we have observed that myoblasts genetically engineered to express an inhibitor of the inflammatory cytokine, IL-1, can improve the survival rate of the injected myoblasts. Our results suggest that selection of specific muscle-derived cell populations or the control of inflammation can be used as an approach to improve cell survival after both myoblast transplantation and the myoblast-mediated ex vivo gene transfer approach.     

An integrative approach for the identification of quantitative trait loci

The genetic dissection of complex traits is one of the most difficult and most important challenges facing science today. We discuss here an integrative approach to quantitative trait loci (QTL) mapping in mice. This approach makes use of the wealth of genetic tools available in mice, as well as the recent advances in genome sequence data already available for a number of inbred mouse strains. We have developed mapping strategies that allow a stepwise narrowing of a QTL mapping interval, prioritizing candidate genes for further analysis with the potential of identifying the most probable candidate gene for the given trait. This approach integrates traditional mapping tools, fine mapping tools, sequence-based analysis, bioinformatics and gene expression.     

Empirical perspectives on species borders: from traditional biogeography to global change

In this paper we will outline several empirical approaches to developing and testing hypotheses about the determinants of species borders. We highlight environmental change as an important opportunity – arguing that these unplanned, large-scale manipulations can be used to study mechanisms which limit species distributions. Our discussion will emphasize three main ideas. First, we review the traditional biogeographic approach. We show how modern analytical and computer techniques have improved this approach and generated important new hypotheses concerning species' range determinants. However, abilities to test those hypotheses continue to be limited. Next we look at how the additions of temporal data, field and lab experimentation, biological details and replication, when applied to systems that have been the subject of classical biogeographic studies, have been used to support or refute hypotheses on range determinants. Such a multi-faceted approach adds rigor, consistency and plausible mechanisms to the study of species ranges, and has been especially fruitful in the study of climate and species' ranges. Lastly, we present an alternative avenue for exploration of range-limiting mechanisms which has been under-utilized. We argue that carefully designed comparisons and contrasts between groups of species or systems provide a powerful tool for examining hypotheses on species' borders. The seasonality hypothesis as an explanation for Rapoport's rule serves as a model of this approach. A test is constructed by comparing patterns of seasonality and range size among marine and terrestrial systems. The seasonality hypothesis is not supported.     

Powerful Exact Unconditional Tests for Agreement between Two Raters with Binary Endpoints

Asymptotic and exact conditional approaches have often been used for testing agreement between two raters with binary outcomes. The exact conditional approach is guaranteed to respect the test size as compared to the traditionally used asymptotic approach based on the standardized Cohen''s kappa coefficient. An alternative to the conditional approach is an unconditional strategy which relaxes the restriction of fixed marginal totals as in the conditional approach. Three exact unconditional hypothesis testing procedures are considered in this article: an approach based on maximization, an approach based on the conditional p-value and maximization, and an approach based on estimation and maximization. We compared these testing procedures based on the commonly used Cohen''s kappa with regards to test size and power. We recommend the following two exact approaches for use in practice due to power advantages: the approach based on conditional p-value and maximization and the approach based on estimation and maximization.     

Simulating association studies: a data-based resampling method for candidate regions or whole genome scans

MOTIVATION: Reductions in genotyping costs have heightened interest in performing whole genome association scans and in the fine mapping of candidate regions. Improvements in study design and analytic techniques will require the simulation of datasets with realistic patterns of linkage disequilibrium and allele frequencies for typed SNPs. METHODS: We describe a general approach to simulate genotyped datasets for standard case-control or affected child trio data, by resampling from existing phased datasets. The approach allows for considerable flexibility in disease models, potentially involving a large number of interacting loci. The method is most applicable for diseases caused by common variants that have not been under strong selection, a class specifically targeted by the International HapMap project. RESULTS: Using the three population Phase I/II HapMap data as a testbed for our approach, we have implemented the approach in HAP-SAMPLE, a web-based simulation tool.     

Inference of S-system models of genetic networks by solving one-dimensional function optimization problems

Voit and Almeida have proposed the decoupling approach as a method for inferring the S-system models of genetic networks. The decoupling approach defines the inference of a genetic network as a problem requiring the solutions of sets of algebraic equations. The computation can be accomplished in a very short time, as the approach estimates S-system parameters without solving any of the differential equations. Yet the defined algebraic equations are non-linear, which sometimes prevents us from finding reasonable S-system parameters. In this study, we propose a new technique to overcome this drawback of the decoupling approach. This technique transforms the problem of solving each set of algebraic equations into a one-dimensional function optimization problem. The computation can still be accomplished in a relatively short time, as the problem is transformed by solving a linear programming problem. We confirm the effectiveness of the proposed approach through numerical experiments.     

Integrating the niche and neutral perspectives on community structure and dynamics

Elucidating the mechanisms underlying the assembly and dynamics of ecological communities is a fundamental goal of ecology. Two conceptual approaches have emerged in this respect: the niche-assembly view and the neutral perspective. The debate as to which approach best explains the biodiversity patterns observed in nature is becoming outdated, as ecologists increasingly agree on the existence of a niche-neutral continuum of community dynamical behaviors. However, attempts to make the continuum idea operational and measurable remain sparse. Here, we propose a model-based approach to achieving this. The proposed methodology consists of separating out fluctuations in species abundances into niche-mediated and stochastic factors, linking the niche configuration to community dynamics through competition, and adding demographic stochasticity. This results in a comprehensive framework including neutrality and strict niche segregation as extreme cases. We develop an index of departure from neutral drift as a surrogate for community position on the niche-neutral continuum. We evaluate the performance of our modeling approach with simulated data, and subsequently use the model to analyze rodent web-trapping data from a real-world system. The model fitting is carried out with a Bayesian approach using Markov chain Monte Carlo simulation methods.