A database and tool, IM Browser, for exploring and integrating emerging gene and protein interaction data forDrosophila

Background  

Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets.     

Statistical techniques for detection of major genes in animal breeding data

Summary Statistical techniques for detection of major loci and for making inferences about major locus parameters such as genotypic frequencies, effects and gene action from field-collected data are presented. In field data, major genotypic effects are likely to be masked by a large number of environmental differences in addition to additive and nonadditive polygenic effects. A graphical technique and a procedure for discriminating among genetic hypotheses based on a mixed model accounting for all these factors are proposed. The methods are illustrated by using simulated data.Journal Paper No. J-12733 of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa. Project No. 1901     

Beyond rest and quiescence (endodormancy and ecodormancy): A novel model for quantifying plant–environment interaction in bud dormancy release

Bud dormancy of plants has traditionally been explained either by physiological growth arresting conditions in the bud or by unfavourable environmental conditions, such as non-growth-promoting low air temperatures. This conceptual dichotomy has provided the framework also for developing process-based plant phenology models. Here, we propose a novel model that in addition to covering the classical dichotomy as a special case also allows the quantification of an interaction of physiological and environmental factors. According to this plant–environment interaction suggested conceptually decades ago, rather than being unambiguous, the concept of “non-growth-promoting low air temperature” depends on the dormancy status of the plant. We parameterized the model with experimental results of growth onset for seven boreal plant species and found that based on the strength of the interaction, the species can be classified into three dormancy types, only one of which represents the traditional dichotomy. We also tested the model with four species in an independent experiment. Our study suggests that interaction of environmental and physiological factors may be involved in many such phenomena that have until now been considered simply as plant traits without any considerations of effects of the environmental factors.     

Ontogenetic differences of herbivory on woody and herbaceous plants: a meta-analysis demonstrating unique effects of herbivory on the young and the old, the slow and the fast

Defensive modifications in prey traits that reduce predation risk can also have negative effects on prey fitness. Such nonconsumptive effects (NCEs) of predators are common, often quite strong, and can even dominate the net effect of predators. We develop an intuitive graphical model to identify and explore the conditions promoting strong NCEs. The model illustrates two conditions necessary and sufficient for large NCEs: (1) trait change has a large cost, and (2) the benefit of reduced predation outweighs the costs, such as reduced growth rate. A corollary condition is that potential predation in the absence of trait change must be large. In fact, the sum total of the consumptive effects (CEs) and NCEs may be any value bounded by the magnitude of the predation rate in the absence of the trait change. The model further illustrates how, depending on the effect of increased trait change on resulting costs and benefits, any combination of strong and weak NCEs and CEs is possible. The model can also be used to examine how changes in environmental factors (e.g., refuge safety) or variation among predator–prey systems (e.g., different benefits of a prey trait change) affect NCEs. Results indicate that simple rules of thumb may not apply; factors that increase the cost of trait change or that increase the degree to which an animal changes a trait, can actually cause smaller (rather than larger) NCEs. We provide examples of how this graphical model can provide important insights for empirical studies from two natural systems. Implementation of this approach will improve our understanding of how and when NCEs are expected to dominate the total effect of predators. Further, application of the models will likely promote a better linkage between experimental and theoretical studies of NCEs, and foster synthesis across systems.     

Modeling, design, chiral aspects and role of para-substituents in aryloxypropranolamine based beta-blockers.

The conformation of the beta-blockers viz. metoprolol, atenolol, bisoprolol, betaxolol and celiprolol has been investigated using Perturbative Configuration Interaction of Localized Orbitals (PCILO) method. The conformational energy maps have been constructed for both the enantiomers (R and S) by rotating the molecule from the para-substituent end. The aryloxypropranolamine moiety adopts the same conformation for all antagonists. The graphical view of R- and S- form of these antagonists in the lowest energy conformation reveals that it is only in the S- form of beta-blockers, all the three functionalities--aromatic moiety, amino and beta-hydroxyl groups are available for interaction with beta-adrenoceptors. The para-substituents of the beta-blockers adopt a conformation which is perpendicular to the aryloxy moiety resulting in an L-shaped structure. The beta-antagonists possibly partition into the lipid bilayer through the para-substituents and the aryloxypropranolamine moiety containing the functionalities, thus, lies parallel to the plane of lipid bilayer for interaction with beta-adrenoceptors. Superimposition of S-bisoprolol in lowest energy conformation with the 3rd putative transmembranous segment of the beta-adrenoceptors reveals that the aromatic moiety, amino and beta-hydroxyl groups of antagonists are involved in interaction with the side chains of Trp-109, Asp-113 and Thr-110 respectively. This has been further substantiated by the interaction studies on the model systems.     

Search for novel neuraminidase inhibitors: Design, synthesis and interaction of oseltamivir derivatives with model membrane using docking, NMR and DSC methods

As a part of our ongoing program of developing novel influenza virus inhibitors, some new derivatives of oseltamivir were prepared by modifying the amino group with glycyl, acetyl, benzyl and prolyl moieties. The interactions of these derivatives with neuraminidase have been probed by molecular modeling techniques. Further, the interaction of these derivatives with model membranes prepared from DPPC and the effect on the thermotropic behavior and polymorphism of the bilayers have been investigated by multinuclear NMR and DSC methods. Results indicate that the glycyl derivative of oseltamivir has the most profound effects on the membrane, compared to other derivatives and seems to be the most promising derivative for further pharmacological evaluation as a neuraminidase inhibitor.     

A model checking method for the proportional hazards model with recurrent gap time data

Recurrent events are the natural outcome in many medical and epidemiology studies. To assess covariate effects on the gaps between consecutive recurrent events, the Cox proportional hazards model is frequently employed in data analysis. The validity of statistical inference, however, depends on the appropriateness of the Cox model. In this paper, we propose a class of graphical techniques and formal tests for checking the Cox model with recurrent gap time data. The building block of our model checking method is an averaged martingale-like process, based on which a class of multiparameter stochastic processes is proposed. This maneuver is very general and can be used to assess different aspects of model fit. Numerical simulations are conducted to examine finite-sample performance, and the proposed model checking techniques are illustrated with data from the Danish Psychiatric Central Register.     

A graphical notation for biochemical networks

A solid definition and comprehensive graphical representation of biological networks is essential for efficient and accurate dissemination of information on biological models. Several proposals have already been made toward this aim. The most well known representation of this kind is a molecular interaction map, or ‘Kohn Map’. However, although the molecular interaction map is a well-defined and compact notation, there are several drawbacks, such as difficulties in intuitive understanding of temporal changes of reactions and additional complexities arising from particular graphical representations. This article proposes several improvements to the molecular interaction map, as well as the use of the ‘process diagram’ to help understand temporal sequences of reactions.     

Effects of Metal Mixtures on Aquatic Biota: A Review of Observations and Methods

A brief review of the historical development of metal mixture interaction analyses is presented. The two major classifications of mixture models are outlined, the “Concentration Addition” and the “Response Addition” approaches. Within these two categories, a number of graphical, mathematical and statistical methods have been used, such as the toxic unit approach, relative potencies, toxicity equivalence factors, and dose-response relationships that have been described using several methods such as probit, logit, and regression analyses. A database was generated to evaluate the frequency of occurrence of less than additive, strictly additive, and more than additive responses to metal mixture effects reported in the literature. The three responses occurred at 43, 27, and 29%, respectively. The database is available electronically from the lead author. The research required to determine the most appropriate methods to quantify the effects of metal mixtures in an ecological risk assessment (ERA) framework is discussed. Until this research is completed, ERAs should use existing models such as the toxic unit or the effects addition approach. Bioaccumulation measurements by organisms for which the accumulation to response relationship is known would also be a useful complement.     

Goodness-of-fit methods for additive-risk models in tumorigenicity experiments

In tumorigenicity experiments, a complication is that the time to event is generally not observed, so that the time to tumor is subject to interval censoring. One of the goals in these studies is to properly model the effect of dose on risk. Thus, it is important to have goodness of fit procedures available for assessing the model fit. While several estimation procedures have been developed for current-status data, relatively little work has been done on model-checking techniques. In this article, we propose numerical and graphical methods for the analysis of current-status data using the additive-risk model, primarily focusing on the situation where the monitoring times are dependent. The finite-sample properties of the proposed methodology are examined through numerical studies. The methods are then illustrated with data from a tumorigenicity experiment.     

Computational Study of Stented and Wrapped Aortic Aneurysms

Aortic aneurysm is a pathology that involves the enlargement of the aortic diameter and has risk factors including aortic dissection. Aneurysm wrapping and stent placement has been used in the treatment of aneurysms. This study aimed to investigate the biomechanical effects of wrapping and stenting on aneurysm. The three-layered aortic aneurysm were created and fluid structure interaction were simulated in wrapped model and stented model. The results provide quantitative predictions of flow patterns and wall mechanics as well as the effects of wrapping and stenting.     

SAS applications for Tai's stability analysis and AMMI model in genotype x environmental interaction (GEI) effects

A user-friendly graphical data analysis to perform stability analysis of genotype x environmental interactions, using Tai's stability model and additive main effects and multiplicative interaction (AMMI) biplots, are presented here. This practical approach integrates statistical and graphical analysis tools available in SAS systems and provides user-friendly applications to perform complete stability analyses without writing SAS program statements or using pull-down menu interfaces by running the SAS macros in the background. By using this macro approach, the agronomists and plant breeders can effectively perform stability analysis and spend more time in data exploration, interpretation of graphs, and output, rather than debugging their program errors. The necessary MACRO-CALL files can be downloaded from the author's home page at http://www.ag.unr.edu/gf. The nature and the distinctive features of the graphics produced by these applications are illustrated by using published data.     

Multi-dimensional sensitivity analysis and ecological implications of a nutrient uptake model

Mechanistic models of nutrient uptake are essential to the study of plant-soil interactions. In these models, uptake rates depend on the supply of the nutrient through the soil and the uptake capacity of the roots. The behaviour of the models is complex, although only six to ten parameters are used. Our goal was to demonstrate a comprehensive and efficient method of exploring a steady-state uptake model with variation in parameters across a range of values described in the literature. We employed two analytical techniques: the first a statistical analysis of variance, and the second a graphical representation of the simulated response surface. The quantitative statistical technique allows objective comparison of parameter and interaction sensitivity. The graphical technique uses a judicious arrangement of figures to present the shape of the response surface in five dimensions. We found that the most important parameters controlling uptake per unit length of root are the average dissolved nutrient concentration and the maximal rate of nutrient uptake. Root radius is influential if rates are expressed per unit root length; on a surface area basis, this parameter is less important. The next most important parameter is the effective diffusion coefficient, especially in the uptake of phosphorus. The interactions of parameters were extremely important and included three and four dimensional effects. For example, limitation by maximal nutrient influx rate is approached more rapidly with increasing nutrient solution concentration when the effective diffusion coefficient is high. We also note the ecological implications of the response surface. For example, in nutrient-limited conditions, the rate of uptake is best augmented by extending root length; when nutrients are plentiful increasing uptake kinetics will have greater effect.     

Inference of Cross-Level Interaction between Genes and Contextual Factors in a Matched Case-Control Metabolic Syndrome Study: A Bayesian Approach

Genes, environment, and the interaction between them are each known to play an important role in the risk for developing complex diseases such as metabolic syndrome. For environmental factors, most studies focused on the measurements observed at the individual level, and therefore can only consider the gene-environment interaction at the same individual scale. Indeed the group-level (called contextual) environmental variables, such as community factors and the degree of local area development, may modify the genetic effect as well. To examine such cross-level interaction between genes and contextual factors, a flexible statistical model quantifying the variability of the genetic effects across different categories of the contextual variable is in need. With a Bayesian generalized linear mixed-effects model with an unconditional likelihood, we investigate whether the individual genetic effect is modified by the group-level residential environment factor in a matched case-control metabolic syndrome study. Such cross-level interaction is evaluated by examining the heterogeneity in allelic effects under various contextual categories, based on posterior samples from Markov chain Monte Carlo methods. The Bayesian analysis indicates that the effect of rs1801282 on metabolic syndrome development is modified by the contextual environmental factor. That is, even among individuals with the same genetic component of PPARG_Pro12Ala, living in a residential area with low availability of exercise facilities may result in higher risk. The modification of the group-level environment factors on the individual genetic attributes can be essential, and this Bayesian model is able to provide a quantitative assessment for such cross-level interaction. The Bayesian inference based on the full likelihood is flexible with any phenotype, and easy to implement computationally. This model has a wide applicability and may help unravel the complexity in development of complex diseases.