Heterogeneous Variances in Repeated Measures

A general approach to repeated measures analysis described in MCGILCHRIST and CULLIS (1991) is extended to general linear models in which there is a homogeneous correlation structure such as a stationary process but the variances are heterogeneous over time. Models for the variance heterogeneity are suggested and results are applied to data examining the effect of enzootic pneumonia on young pigs.     

The role of mRNA structure in the regulation of protein synthesis: Implications for studies of development

A method is described for the experimental determination of the secondary structure of RNA using enzymatic cleavage data coupled with computer analysis. The structure-specific enzymes S1 nuclease and cobra venom ribonuclease are used to locate nonpaired and basepaired nucleotides, respectively. Computer techniques that utilize the enzymatic susceptibility information to generate a minimum free-energy structure are used to obtain secondary structure models. A second method, using acrylamide-agarose gel electrophoresis, is described for the determination of the relative protein synthesis initiation rates of endlabeled eukaryotic mRNAs. These methods are applied to the rabbit globin mRNAs as an example of a general approach for relating mRNA structure and function. A discussion of the role of messenger RNA structure in the regulation of translation is included with an emphasis on studies of development.     

Phylogenetic meta-analysis

Meta-analysis is a powerful statistical technique that combines the results of independent studies to identify general trends. When the species under examination are not independent however, it is also necessary to incorporate phylogenetic information into the analysis. Unfortunately, current meta-analytic approaches cannot account for lack of independence resulting from shared evolutionary history, so a general solution to this problem is lacking. In this article, I derive a model for phylogenetic meta-analysis, so that data across studies may be summarized with evolutionary history explicitly incorporated. The approach takes advantage of common aspects of linear statistical models used by both meta-analysis and the phylogenetic comparative method, thereby allowing them to be analytically combined. In this manner, the correlation structure generated by phylogenetic history can be incorporated directly into the meta-analytic procedure. I illustrate the approach by examining the prevalence of body size clines in mammals. The approach is general, and can also be used to incorporate correlation structure among studies generated by other factors, such as spatial or temporal proximity, or environmental similarity. Therefore, this procedure provides a general statistical template for meta-analytic techniques that can account for attributes that generate nonindependence among studies. Implications of the phylogenetic meta-analysis are discussed.     

Control of neuromuscular blockade with Gaussian process models

This paper presents Nonlinear Model Predictive Control (NMPC) of neuromuscular blockade induced by atracurium on patients subject to general anesthesia. In order to tackle the high levels of uncertainty in the process behavior, probabilistic and non-parametric Gaussian process models are used in the NMPC approach. The proposed control structure was tested in a bank of models that represent patients subject to general anesthesia under elective surgery. All patients models were stabilized and yield a satisfactory performance.     

A group-theoretic approach to rings of coupled biological oscillators

In this paper, a general approach for studying rings of coupled biological oscillators is presented. This approach, which is group-theoretic in nature, is based on the finding that symmetric ring networks of coupled non-linear oscillators possess generic patterns of phaselocked oscillations. The associated analysis is independent of the mathematical details of the oscillators' intrinsic dynamics and the nature of the coupling between them. The present approach thus provides a framework for distinguishing universal dynamic behaviour from that which depends upon further structure. In this study, the typical oscillation patterns for the general case of a symmetric ring of n coupled non-linear oscillators and the specific cases of three- and five-membered rings are considered. Transitions between different patterns of activity are modelled as symmetry-breaking bifurcations. The effects of one-way coupling in a ring network and the differences between discrete and continuous systems are discussed. The theoretical predictions for symmetric ring networks are compared with physiological observations and numerical simulations. This comparison is limited to two examples: neuronal networks and mammalian intestinal activity. The implications of the present approach for the development of physiologically meaningful oscillator models are discussed.     

Protein evolution with dependence among codons due to tertiary structure

Markovian models of protein evolution that relax the assumption of independent change among codons are considered. With this comparatively realistic framework, an evolutionary rate at a site can depend both on the state of the site and on the states of surrounding sites. By allowing a relatively general dependence structure among sites, models of evolution can reflect attributes of tertiary structure. To quantify the impact of protein structure on protein evolution, we analyze protein-coding DNA sequence pairs with an evolutionary model that incorporates effects of solvent accessibility and pairwise interactions among amino acid residues. By explicitly considering the relationship between nonsynonymous substitution rates and protein structure, this approach can lead to refined detection and characterization of positive selection. Analyses of simulated sequence pairs indicate that parameters in this evolutionary model can be well estimated. Analyses of lysozyme c and annexin V sequence pairs yield the biologically reasonable result that amino acid replacement rates are higher when the replacements lead to energetically favorable proteins than when they destabilize the proteins. Although the focus here is evolutionary dependence among codons that is associated with protein structure, the statistical approach is quite general and could be applied to diverse cases of evolutionary dependence where surrogates for sequence fitness can be measured or modeled.     

Analysis of periodic growth-disturbance models

In this paper, I present and discuss a potentially useful modeling approach for investigating population dynamics in the presence of disturbance. Using the motivating example of wildfire, I construct and analyze a deterministic model of population dynamics with periodic disturbances independent of spatial effects. Plant population growth is coupled to fire disturbance to create a growth-disturbance model for a fluctuating population. Changes in the disturbance frequency are shown to generate a period-bubbling bifurcation structure and population dynamics that are most variable at intermediate disturbance frequencies. Similar dynamics are observed when the model is extended to include a seed bank. Some general conditions necessary for a rich bifurcation structure in growth-disturbance models are discussed.     

RNA sequence analysis using covariance models.

We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models 'covariance models'. A covariance model of tRNA sequences is an extremely sensitive and discriminative tool for searching for additional tRNAs and tRNA-related sequences in sequence databases. A model can be built automatically from an existing sequence alignment. We also describe an algorithm for learning a model and hence a consensus secondary structure from initially unaligned example sequences and no prior structural information. Models trained on unaligned tRNA examples correctly predict tRNA secondary structure and produce high-quality multiple alignments. The approach may be applied to any family of small RNA sequences.     

Modeling of protein binary complexes using structural mass spectrometry data

In this article, we describe a general approach to modeling the structure of binary protein complexes using structural mass spectrometry data combined with molecular docking. In the first step, hydroxyl radical mediated oxidative protein footprinting is used to identify residues that experience conformational reorganization due to binding or participate in the binding interface. In the second step, a three-dimensional atomic structure of the complex is derived by computational modeling. Homology modeling approaches are used to define the structures of the individual proteins if footprinting detects significant conformational reorganization as a function of complex formation. A three-dimensional model of the complex is constructed from these binary partners using the ClusPro program, which is composed of docking, energy filtering, and clustering steps. Footprinting data are used to incorporate constraints-positive and/or negative-in the docking step and are also used to decide the type of energy filter-electrostatics or desolvation-in the successive energy-filtering step. By using this approach, we examine the structure of a number of binary complexes of monomeric actin and compare the results to crystallographic data. Based on docking alone, a number of competing models with widely varying structures are observed, one of which is likely to agree with crystallographic data. When the docking steps are guided by footprinting data, accurate models emerge as top scoring. We demonstrate this method with the actin/gelsolin segment-1 complex. We also provide a structural model for the actin/cofilin complex using this approach which does not have a crystal or NMR structure.     

SnugDock: Paratope Structural Optimization during Antibody-Antigen Docking Compensates for Errors in Antibody Homology Models

High resolution structures of antibody-antigen complexes are useful for analyzing the binding interface and to make rational choices for antibody engineering. When a crystallographic structure of a complex is unavailable, the structure must be predicted using computational tools. In this work, we illustrate a novel approach, named SnugDock, to predict high-resolution antibody-antigen complex structures by simultaneously structurally optimizing the antibody-antigen rigid-body positions, the relative orientation of the antibody light and heavy chains, and the conformations of the six complementarity determining region loops. This approach is especially useful when the crystal structure of the antibody is not available, requiring allowances for inaccuracies in an antibody homology model which would otherwise frustrate rigid-backbone docking predictions. Local docking using SnugDock with the lowest-energy RosettaAntibody homology model produced more accurate predictions than standard rigid-body docking. SnugDock can be combined with ensemble docking to mimic conformer selection and induced fit resulting in increased sampling of diverse antibody conformations. The combined algorithm produced four medium (Critical Assessment of PRediction of Interactions-CAPRI rating) and seven acceptable lowest-interface-energy predictions in a test set of fifteen complexes. Structural analysis shows that diverse paratope conformations are sampled, but docked paratope backbones are not necessarily closer to the crystal structure conformations than the starting homology models. The accuracy of SnugDock predictions suggests a new genre of general docking algorithms with flexible binding interfaces targeted towards making homology models useful for further high-resolution predictions.     

Application of docking methodologies to modeled proteins

Protein docking is essential for structural characterization of protein interactions. Besides providing the structure of protein complexes, modeling of proteins and their complexes is important for understanding the fundamental principles and specific aspects of protein interactions. The accuracy of protein modeling, in general, is still less than that of the experimental approaches. Thus, it is important to investigate the applicability of docking techniques to modeled proteins. We present new comprehensive benchmark sets of protein models for the development and validation of protein docking, as well as a systematic assessment of free and template-based docking techniques on these sets. As opposed to previous studies, the benchmark sets reflect the real case modeling/docking scenario where the accuracy of the models is assessed by the modeling procedure, without reference to the native structure (which would be unknown in practical applications). We also expanded the analysis to include docking of protein pairs where proteins have different structural accuracy. The results show that, in general, the template-based docking is less sensitive to the structural inaccuracies of the models than the free docking. The near-native docking poses generated by the template-based approach, typically, also have higher ranks than those produces by the free docking (although the free docking is indispensable in modeling the multiplicity of protein interactions in a crowded cellular environment). The results show that docking techniques are applicable to protein models in a broad range of modeling accuracy. The study provides clear guidelines for practical applications of docking to protein models.     

The intrinsic mean time to extinction: a unifying approach to analysing persistence and viability of populations

Analysing the persistence and viability of small populations is a key issue in extinction theory and population viability analysis. However, there is still no consensus on how to quantify persistence and viability. We present an approach to evaluate any simulation model concerned with extinction. The approach is devised from general Markov models of stochastic population dynamics. From these models, we distil insights into the general mathematical structure of the risk of extinction by time t, P₀(t). From this mathematical structure, we devise a simple but effective protocol – the ln(1−P₀)-plot – which is applicable for situations including environmental noise or catastrophes. This plot delivers two quantities which are fundamental to the assessment of persistence and viability: the intrinsic mean time to extinction, T_m, and the probability c₁ of the population reaching the established phase. The established phase is characterized by typical fluctuations of the population's state variable which can be described by quasi-stationary probability distributions. The risk of extinction in the established phase is constant and given by 1/T_m. We show that T_m is the basic currency for the assessment of persistence and viability because T_m is independent of initial conditions and allows the risk of extinction to be calculated for any time horizon. For situations where initial conditions are important, additionally c₁ has to be considered.     

Kinetic model of the action of oligomeric enzymes using phosphofructokinase as an example. II. General formulation of the hierarchical model

A general approach is suggested to describe the steady-state kinetics of the oligomeric enzymes on the base of the generalized statistical Ising model. Detailed analysis is given for the case of a oligomeric enzyme with a hierarchical supramolecular organization. A protomer of this enzyme composed of several equivalent subunits represents the quarternary level of structure. In their turn the finite or infinite number of protomers is associated into a oligomer thus creating a new "quinternary" level of the enzyme organization. The model accounts for the ligand-induced homotrophic cooperative interactions: firstly, between the neighbouring protomers and secondly, between the subunits of the same protomer. The influence of protomer conformation on the subunit state and the cooperativity induction caused by two-ligand binding are also taken into consideration. Monod-Wyman-Changeux's and Koshland's models are shown to be special limit cases of the suggested general theory.     

Prediction of protein structure from ideal forms

For many years it has been accepted that the sequence of a protein can specify its three-dimensional structure. However, there has been limited progress in explaining how the sequence dictates its fold and no attempt to do this computationally without the use of specific structural data has ever succeeded for any protein larger than 100 residues. We describe a method that can predict complex folds up to almost 200 residues using only basic principles that do not include any elements of sequence homology. The method does not simulate the folding chain but generates many thousands of models based on an idealized representation of structure. Each rough model is scored and the best are refined. On a set of five proteins, the correct fold score well and when tested on a set of larger proteins, the correct fold was ranked highest for some proteins more than 150 residues, with others being close topological variants. All other methods that approach this level of success rely on the use of templates or fragments of known structures. Our method is unique in using a database of ideal models based on general packing rules that, in spirit, is closer to an ab initio approach.     

Simulation of hyper-inverse Wishart distributions in graphical models

We introduce and exemplify an efficient method for direct samplingfrom hyper-inverse Wishart distributions. The method reliesvery naturally on the use of standard junction-tree representationof graphs, and couples these with matrix results for inverseWishart distributions. We describe the theory and resultingcomputational algorithms for both decomposable and nondecomposablegraphical models. An example drawn from financial time seriesdemonstrates application in a context where inferences on astructured covariance model are required. We discuss and investigatequestions of scalability of the simulation methods to higher-dimensionaldistributions. The paper concludes with general comments aboutthe approach, including its use in connection with existingMarkov chain Monte Carlo methods that deal with uncertaintyabout the graphical model structure.     

Background stratified Poisson regression analysis of cohort data

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as ‘nuisance’ variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this ‘conditional’ regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.