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Biological Interpretation (biological + interpretation)
Selected AbstractsComparative analysis of gene expression on mRNA and protein level during development of Streptomyces cultures by using singular value decompositionPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 21 2007Jiri Vohradsky Dr. Abstract This paper describes a comparative systems level analysis of the developmental proteome and transcriptome in the model antibiotic-producing eubacterium Streptomyces coelicolor, cultured on different media. The analysis formulates expression as the superposition of effects of regulatory networks and biological processes which can be identified using singular value decomposition (SVD) of a data matrix formed by time series measurements of expression of individual genes throughout the cell cycle of the bacterium. SVD produces linearly orthogonal factors, each of which can represent an independent system behavior defined by a linear combination of the genes/proteins highly correlated with the corresponding factor. By using SVD of the developmental time series of gene expression, as measured by both protein and RNA levels, we show that on the highest level of control (representing the basic kinetic behavior of the population), the results are identical, regardless of the type of experiment or cultivation method. The results show that this approach is capable of identifying basic regulatory processes independent of the environment in which the organism lives. It also shows that these processes are manifested equally on protein and RNA levels. Biological interpretation of the correlation of the genes and proteins with significant eigenprofiles (representing the highest level kinetic behavior of protein and/or RNA synthesis) revealed their association with metabolic processes, stress responses, starvation, and secondary metabolite production. [source] Linking dispersal, immigration and scale in the neutral theory of biodiversityECOLOGY LETTERS, Issue 12 2009Ryan A. Chisholm Abstract In the classic spatially implicit formulation of Hubbell's neutral theory of biodiversity a local community receives immigrants from a metacommunity operating on a relatively slow timescale, and dispersal into the local community is governed by an immigration parameter m. A current problem with neutral theory is that m lacks a clear biological interpretation. Here, we derive analytical expressions that relate the immigration parameter m to the geometry of the plot defining the local community and the parameters of a dispersal kernel. Our results facilitate more rigorous and extensive tests of the neutral theory: we conduct a test of neutral theory by comparing estimates of m derived from fits to empirical species abundance distributions to those derived from dispersal kernels and find acceptable correspondence; and we generate a new prediction of neutral theory by investigating how the shapes of species abundance distributions change theoretically as the spatial scale of observation changes. We also discuss how our main analytical results can be used to assess the error in the mean-field approximations associated with spatially implicit formulations of neutral theory. Ecology Letters (2009) 12: 1385,1393 [source] A simple persistence condition for structured populationsECOLOGY LETTERS, Issue 7 2006Alan Hastings Abstract The fundamental question in both basic and applied population biology of whether a species will increase in numbers is often investigated by finding the population growth rate as the largest eigenvalue of a deterministic matrix model. For a population classified only by age, and not stage or size, a simpler biologically interpretable condition can be used, namely whether R0, the mean number of offspring per newborn, is greater than one. However, for the many populations not easily described using only age classes, stage-structured models must be used for which there is currently no quantity like R0. We determine analogous quantities that must be greater than one for persistence of a general structured population model that have a similar useful biological interpretation. Our approach can be used immediately to determine the magnitude of changes and interactions that would either allow population persistence or would ensure control of an undesirable species. [source] INTERPRETATION OF THE RESULTS OF COMMON PRINCIPAL COMPONENTS ANALYSESEVOLUTION, Issue 3 2002David Houle Abstract Common principal components (CPC) analysis is a new tool for the comparison of phenotypic and genetic variance-covariance matrices. CPC was developed as a method of data summarization, but frequently biologists would like to use the method to detect analogous patterns of trait correlation in multiple populations or species. To investigate the properties of CPC, we simulated data that reflect a set of causal factors. The CPC method performs as expected from a statistical point of view, but often gives results that are contrary to biological intuition. In general, CPC tends to underestimate the degree of structure that matrices share. Differences of trait variances and covariances due to a difference in a single causal factor in two otherwise identically structured datasets often cause CPC to declare the two datasets unrelated. Conversely, CPC could identify datasets as having the same structure when causal factors are different. Reordering of vectors before analysis can aid in the detection of patterns. We urge caution in the biological interpretation of CPC analysis results. [source] Minimizing errors in identifying Lévy flight behaviour of organismsJOURNAL OF ANIMAL ECOLOGY, Issue 2 2007DAVID W. SIMS Summary 1Lévy flights are specialized random walks with fundamental properties such as superdiffusivity and scale invariance that have recently been applied in optimal foraging theory. Lévy flights have movement lengths chosen from a probability distribution with a power-law tail, which theoretically increases the chances of a forager encountering new prey patches and may represent an optimal solution for foraging across complex, natural habitats. 2An increasing number of studies are detecting Lévy behaviour in diverse organisms such as microbes, insects, birds, and mammals including humans. A principal method for detecting Lévy flight is whether the exponent (µ) of the power-law distribution of movement lengths falls within the range 1 < µ , 3. The exponent can be determined from the histogram of frequency vs. movement (step) lengths, but different plotting methods have been used to derive the Lévy exponent across different studies. 3Here we investigate using simulations how different plotting methods influence the µ-value and show that the power-law plotting method based on 2k (logarithmic) binning with normalization prior to log transformation of both axes yields low error (1·4%) in identifying Lévy flights. Furthermore, increasing sample size reduced variation about the recovered values of µ, for example by 83% as sample number increased from n = 50 up to 5000. 4Simple log transformation of the axes of the histogram of frequency vs. step length underestimated µ by c.40%, whereas two other methods, 2k (logarithmic) binning without normalization and calculation of a cumulative distribution function for the data, both estimate the regression slope as 1 , µ. Correction of the slope therefore yields an accurate Lévy exponent with estimation errors of 1·4 and 4·5%, respectively. 5Empirical reanalysis of data in published studies indicates that simple log transformation results in significant errors in estimating µ, which in turn affects reliability of the biological interpretation. The potential for detecting Lévy flight motion when it is not present is minimized by the approach described. We also show that using a large number of steps in movement analysis such as this will also increase the accuracy with which optimal Lévy flight behaviour can be detected. [source] A test of the metapopulation model of the species,area relationshipJOURNAL OF BIOGEOGRAPHY, Issue 8 2002Stephen F. Matter Abstract Aim The species,area relationship is a ubiquitous pattern. Previous methods describing the relationship have done little to elucidate mechanisms producing the pattern. Hanski & Gyllenberg (Science, 1997, 275, 397) have shown that a model of metapopulation dynamics yields predictable species,area relationships. We elaborate on the biological interpretation of this mechanistic model and test the prediction that communities of species with a higher risk of extinction caused by environmental stochasticity should have lower species,area slopes than communities experiencing less impact of environmental stochasticity. Methods We develop the mainland,island version of the metapopulation model and show that the slope of the species,area relationship resulting from this model is related to the ratio of population growth rate to variability in population growth of individual species. We fit the metapopulation model to five data sets, and compared the fit with the power function model and Williams's (Ecology, 1995, 76, 2607) extreme value function model. To test that communities consisting of species with a high risk of extinction should have lower slopes, we used the observation that small-bodied species of vertebrates are more susceptible to environmental stochasticity than large-bodied species. The data sets were divided into small and large bodied species and the model fit to both. Results and main conclusions The metapopulation model showed a good fit for all five data sets, and was comparable with the fits of the extreme value function and power function models. The slope of the metapopulation model of the species,area relationship was greater for larger than for smaller-bodied species for each of five data sets. The slope of the metapopulation model of the species,area relationship has a clear biological interpretation, and allows for interpretation that is rooted in ecology, rather than ad hoc explanation. [source] A tale of two matrices: multivariate approaches in evolutionary biologyJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 1 2007M. W. BLOWS Abstract Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (,) which describes the individual fitness surface. The second is the genetic variance,covariance matrix (G) that influences the multivariate response to selection. A common approach to the empirical analysis of these matrices is the element-by-element testing of significance, and subsequent biological interpretation of pattern based on these univariate and bivariate parameters. Here, I show why this approach is likely to misrepresent the genetic basis of quantitative traits, and the selection acting on them in many cases. Diagonalization of square matrices is a fundamental aspect of many of the multivariate statistical techniques used by biologists. Applying this, and other related approaches, to the analysis of the structure of , and G matrices, gives greater insight into the form and strength of nonlinear selection, and the availability of genetic variance for multiple traits. [source] Modelling growth and body composition in fish nutrition: where have we been and where are we going?AQUACULTURE RESEARCH, Issue 2 2010André Dumas Abstract Mathematical models in fish nutrition have proven indispensable in estimating growth and feed requirements. Nowadays, reducing the environmental footprint and improving product quality of fish culture operations are of increasing interest. This review starts by examining simple models applied to describe/predict fish growth profiles and progresses towards more comprehensive concepts based on bioenergetics and nutrient metabolism. Simple growth models often lack biological interpretation and overlook fundamental properties of fish (e.g. ectothermy, indeterminate growth). In addition, these models disregard possible variations in growth trajectory across life stages. Bioenergetic models have served to predict not only fish growth but also feed requirements and waste outputs from fish culture operations. However, bioenergetics is a concept based on energy-yielding equivalence of chemicals and has significant limitations. Nutrient-based models have been introduced into the fish nutrition literature over the last two decades and stand as a more biologically sound alternative to bioenergetic models. More mechanistic models are required to expand current understanding about growth targets and nutrient utilization for biomass gain. Finally, existing models need to be adapted further to address effectively concerns regarding sustainability, product quality and body traits. [source] MolProbity: all-atom structure validation for macromolecular crystallographyACTA CRYSTALLOGRAPHICA SECTION D, Issue 1 2010Vincent B. Chen MolProbity is a structure-validation web service that provides broad-spectrum solidly based evaluation of model quality at both the global and local levels for both proteins and nucleic acids. It relies heavily on the power and sensitivity provided by optimized hydrogen placement and all-atom contact analysis, complemented by updated versions of covalent-geometry and torsion-angle criteria. Some of the local corrections can be performed automatically in MolProbity and all of the diagnostics are presented in chart and graphical forms that help guide manual rebuilding. X-ray crystallography provides a wealth of biologically important molecular data in the form of atomic three-dimensional structures of proteins, nucleic acids and increasingly large complexes in multiple forms and states. Advances in automation, in everything from crystallization to data collection to phasing to model building to refinement, have made solving a structure using crystallography easier than ever. However, despite these improvements, local errors that can affect biological interpretation are widespread at low resolution and even high-resolution structures nearly all contain at least a few local errors such as Ramachandran outliers, flipped branched protein side chains and incorrect sugar puckers. It is critical both for the crystallographer and for the end user that there are easy and reliable methods to diagnose and correct these sorts of errors in structures. MolProbity is the authors' contribution to helping solve this problem and this article reviews its general capabilities, reports on recent enhancements and usage, and presents evidence that the resulting improvements are now beneficially affecting the global database. [source] High-dimensional data analysis: Selection of variables, data compression and graphics , Application to gene expressionBIOMETRICAL JOURNAL, Issue 2 2009Jürgen Läuter Abstract The paper presents effective and mathematically exact procedures for selection of variables which are applicable in cases with a very high dimension as, for example, in gene expression analysis. Choosing sets of variables is an important method to increase the power of the statistical conclusions and to facilitate the biological interpretation. For the construction of sets, each single variable is considered as the centre of potential sets of variables. Testing for significance is carried out by means of the Westfall-Young principle based on resampling or by the parametric method of spherical tests. The particular requirements for statistical stability are taken into account; each kind of overfitting is avoided. Thus, high power is attained and the familywise type I error can be kept in spite of the large dimension. To obtain graphical representations by heat maps and curves, a specific data compression technique is applied. Gene expression data from B-cell lymphoma patients serve for the demonstration of the procedures. [source] A unified model of sigmoid tumour growth based on cell proliferation and quiescenceCELL PROLIFERATION, Issue 6 2007F. Kozusko Objectives: A class of sigmoid functions designated generalized von Bertalanffy, Gompertzian and generalized Logistic has been used to fit tumour growth data. Various models have been proposed to explain the biological significance and foundations of these functions. However, no model has been found to fully explain all three or the relationships between them. Materials and Methods: We propose a simple cancer cell population dynamics model that provides a biological interpretation for these sigmoids' ability to represent tumour growth. Results and Conclusions: We show that the three sigmoids can be derived from the model and are in fact a single solution subject to the continuous variation of parameters describing the decay of the proliferation fraction and/or cell quiescence. We use the model to generate proliferation fraction profiles for each sigmoid and comment on the significance of the differences relative to cell cycle-specific and non-cell cycle-specific therapies. [source] Statistical hypothesis testing in intraspecific phylogeography: nested clade phylogeographical analysis vs. approximate Bayesian computationMOLECULAR ECOLOGY, Issue 2 2009ALAN R. TEMPLETON Abstract Nested clade phylogeographical analysis (NCPA) and approximate Bayesian computation (ABC) have been used to test phylogeographical hypotheses. Multilocus NCPA tests null hypotheses, whereas ABC discriminates among a finite set of alternatives. The interpretive criteria of NCPA are explicit and allow complex models to be built from simple components. The interpretive criteria of ABC are ad hoc and require the specification of a complete phylogeographical model. The conclusions from ABC are often influenced by implicit assumptions arising from the many parameters needed to specify a complex model. These complex models confound many assumptions so that biological interpretations are difficult. Sampling error is accounted for in NCPA, but ABC ignores important sources of sampling error that creates pseudo-statistical power. NCPA generates the full sampling distribution of its statistics, but ABC only yields local probabilities, which in turn make it impossible to distinguish between a good fitting model, a non-informative model, and an over-determined model. Both NCPA and ABC use approximations, but convergences of the approximations used in NCPA are well defined whereas those in ABC are not. NCPA can analyse a large number of locations, but ABC cannot. Finally, the dimensionality of tested hypothesis is known in NCPA, but not for ABC. As a consequence, the ,probabilities' generated by ABC are not true probabilities and are statistically non-interpretable. Accordingly, ABC should not be used for hypothesis testing, but simulation approaches are valuable when used in conjunction with NCPA or other methods that do not rely on highly parameterized models. [source] Radiation damage in macromolecular crystallography: what is it and why should we care?ACTA CRYSTALLOGRAPHICA SECTION D, Issue 4 2010Elspeth F. Garman Radiation damage inflicted during diffraction data collection in macromolecular crystallography has re-emerged in the last decade as a major experimental and computational challenge, as even for crystals held at 100,K it can result in severe data-quality degradation and the appearance in solved structures of artefacts which affect biological interpretations. Here, the observable symptoms and basic physical processes involved in radiation damage are described and the concept of absorbed dose as the basic metric against which to monitor the experimentally observed changes is outlined. Investigations into radiation damage in macromolecular crystallography are ongoing and the number of studies is rapidly increasing. The current literature on the subject is compiled as a resource for the interested researcher. [source] Determining best complete subsets of specimens and characters for multivariate morphometric studies in the presence of large amounts of missing dataBIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY, Issue 2 2006RICHARD E. STRAUSS Missing data are frequent in morphometric studies of both fossil and recent material. A common method of addressing the problem of missing data is to omit combinations of characters and specimens from subsequent analyses; however, omitting different subsets of characters and specimens can affect both the statistical robustness of the analyses and the resulting biological interpretations. We describe a method of examining all possible subsets of complete data and of scoring each subset by the ,condition' (ratio of first eigenvalue to second, or of second to first, depending on context) of the corresponding covariance or correlation matrix, and subsequently choosing the submatrix that either optimizes one of these criteria or matches the estimated condition of the original data matrix. We then describe an extension of this method that can be used to choose the ,best' characters and specimens for which some specified proportion of missing data can be estimated using standard imputation techniques such as the expectation-maximization algorithm or multiple imputation. The methods are illustrated with published and unpublished data sets on fossil and extant vertebrates. Although these problems and methods are discussed in the context of conventional morphometric data, they are applicable to many other kinds of data matrices. © 2006 The Linnean Society of London, Biological Journal of the Linnean Society, 2006, 88, 309,328. [source] | |||||||||||||