pIPHULA--parallel inference of population parameters using a likelihood approach

pIPHULA is the parallel program to estimate the parameters of a realistic model of population growth.     

Kinetic parameters of enzymatic reactions in states of maximal activity; an evolutionary approach

A theoretical investigation is presented which allows the calculation of states of maximal reaction rates for single enzymes and for unbranched enzymatic chains. As an extension to previous papers (Heinrich & Holzhütter, 1985, Biomed. biochim. Acta 44, 959-969; Heinrich et al., 1987, Bull. math. Biol. 49, 539-595) a detailed enzymatic mechanism was taken into consideration. Conclusions are drawn for the optimal values of the microscopic rate constants as well as of the maximal activities and Michaelis constants. Ten solutions are found which depend on the equilibrium constant as well as on the concentrations of substrates and products. It is shown that for high equilibrium constants one of the solutions applies to a very large range of the concentrations of the outer reactants. This solution is characterized by maximal values of the rate constants of all forward reactions and by non-maximal values of the rate constants of all backward reactions. In contrast to previous assumptions (Albery & Knowles, 1976b, Biochemistry 15, 5631-5640; Burbaum et al., 1989, Biochemistry 28, 9293-9305) states of maximal reaction rate are not always characterized by the highest possible values of the second-order rate constants which are related to the diffusion of the substrate and the product to the active site of the enzyme. Predictions are made concerning the ratios of maximal activities in optimal states as well as for the adaptation of the Michaelis constants to the concentrations of the outer reactants. Using metabolic control analysis it is shown that the solutions obtained for single enzymes may also be applied in multi-enzyme systems.     

Estimating epidemiological parameters for bovine tuberculosis in British cattle using a Bayesian partial-likelihood approach

Fitting models with Bayesian likelihood-based parameter inference is becoming increasingly important in infectious disease epidemiology. Detailed datasets present the opportunity to identify subsets of these data that capture important characteristics of the underlying epidemiology. One such dataset describes the epidemic of bovine tuberculosis (bTB) in British cattle, which is also an important exemplar of a disease with a wildlife reservoir (the Eurasian badger). Here, we evaluate a set of nested dynamic models of bTB transmission, including individual- and herd-level transmission heterogeneity and assuming minimal prior knowledge of the transmission and diagnostic test parameters. We performed a likelihood-based bootstrapping operation on the model to infer parameters based only on the recorded numbers of cattle testing positive for bTB at the start of each herd outbreak considering high- and low-risk areas separately. Models without herd heterogeneity are preferred in both areas though there is some evidence for super-spreading cattle. Similar to previous studies, we found low test sensitivities and high within-herd basic reproduction numbers (R₀), suggesting that there may be many unobserved infections in cattle, even though the current testing regime is sufficient to control within-herd epidemics in most cases. Compared with other, more data-heavy approaches, the summary data used in our approach are easily collected, making our approach attractive for other systems.     

Spread of Q fever within dairy cattle herds: key parameters inferred using a Bayesian approach

Q fever is a worldwide zoonosis caused by Coxiella burnetii. Although ruminants are recognized as the most important source of human infection, no previous studies have focused on assessing the characteristics of the bacterial spread within a cattle herd and no epidemic model has been proposed in this context. We assess the key epidemiological parameters from field data in a Bayesian framework that takes into account the available knowledge, missing data and the uncertainty of the observation process owing to the imperfection of diagnostic tests. We propose an original individual-based Markovian model in discrete time describing the evolution of the infection for each animal. Markov chain Monte Carlo methodology is used to estimate parameters of interest from data consisting of individual health states of 217 cows of five chronically infected dairy herds sampled every week for a four-week period. Outputs are the posterior distributions of the probabilities of transition between health states and of the environmental bacterial load. Our findings show that some herds are characterized by a very low infection risk while others have a mild infection risk and a non-negligible intermittent shedding probability. Moreover, the antibody status seems to be a key point in the bacterial spread (shedders with antibodies shed for a longer period of time than shedders without antibodies). In addition to the biological insights, these estimates also provide information for calibrating simulation models to assess control strategies for C. burnetii infection.     

Design and calibration of a simplified river-water quality model including heterotrophic bacteria and chemical parameters

Abstract The results obtained in the design and calibration of a deterministic water quality model from the Llobregat River (Spain) are presented. The water quality indicators studied are dissolved oxygen, biochemical oxygen demand, ammonia, nitrite, nitrate, phosphate, and heterotrophic bacteria. The factors which describe the rates of the transformation processes are of two types: first-order and Michaelis-Menten kinetics. A decomposition process is applied to the model for calibration. The inclusion in the model of heterotrophic bacteria shows good predictive capacity to describe the behaviour of the biological processes in the river. The proposed model gives a correct evaluation of the indicators studied, and may be used in water quality management.     

Coupling isoprene and monoterpene emissions from Amazonian tree species with physiological and environmental parameters using a neural network approach

The ability to predict isoprene emissions from plants is important for predicting atmospheric chemistry. To improve the basis for prediction capability, data obtained from continuous field measurements of isoprene and monoterpene emissions from three Amazonian tree species were related to observed environmental and leaf physiological parameters using a new neural network approach. The environmental parameters included leaf temperature, light, relative humidity, water vapour pressure deficit, and the history of ambient temperature and ozone concentration, whereas the physiological parameters included stomatal conductance, assimilation and intercellular CO₂ concentration. The neural approach with 24 different combinations of these parameters was applied to predict the emission variability observed during short time periods (2–3 d) with individual tree branches and, on a longer-term scale, in aggregated data sets from different seasons, leaf developmental stage, and light environment. The results were compared to the quasi standard emission algorithm for isoprene. On the short-term scale, good agreement (r²≈ 0.9) was obtained between observations and predictions of the standard algorithm as well as predictions of the neural network using the same input parameters (leaf temperature and light). When these predictors were used to model the long-term emission variability, r² was reduced to < 0.5 for both approaches. Remarkably, for the neural technique, more than 50% of the unexplained variance could be explained by the mean temperature of the preceding 36 h. An even better network performance was obtained with physiological parameter combinations (r² > 0.9) suggesting a strong and applicable link between isoprenoid emission and leaf primary metabolism.     

Quantitative approach using multiple single parameters versus visual assessment in dobutamine stress echocardiography

ABSTRACT: BACKGROUND: A number of myocardial Doppler-derived velocity, strain myocardial imaging parameters (DMI) and speckle tracking imaging (STI) have been proposed for the quantification of myocardial ischemia during stress echocardiography. The purpose of the study was to identify the best single ultrasound quantitative parameter for prediction of significant coronary stenosis and compare it with visual assessment during dobutamine stress echocardiography (DSE). METHODS: Prospective analysis included data of 151 patients (age 61.8 +/- 9.2) who underwent dobutamine stress echocardiography for known (n=35) or suspected CAD (n=36) or symptomatic chest pain (n=80), excluding patients with previous myocardial infarction. Systolic, post-systolic and diastolic velocities, strain and strain rate parameters were obtained at rest and at peak dobutamine challenge. Derivative markers as E'/A' ratio, post-systolic index and changes from rest to stress were calculated (98 parameters overall, predominantly longitudinal). Coronary angiography was chosen as reference method considering at least one stenosis greater than or equal to 70% per patient as significant CAD. The predictive value of quantitative parameters and wall motion score index (WMSI) was obtained using logistic regression and ROC analysis. RESULTS: The value of single parameters discriminated as independent predictors of CAD appeared to be modest (area under the curve [AUC] ranged from 0.63 to 0.72 for 16 PW-DMI, 12 CC-DMI and 12 STI markers), comparing to AUC of WMSI 0.88. Sensitivity, specificity and accuracy of visual DSE evaluation was 82.4% (95%CI 77.4%; 85.2%), 92.6% (95%CI 83.4%; 97.5%) and 86.0% (95%CI 79.5%; 89.6%), respectively, Youden index 0.75. Sensitivity, specificity and accuracy of single predictors ranged from 40.0% to 93.3% (95% CI 22.7%; 99.2%), from 34.2% to 88.7% (95% CI 25.6%; 94.1%) and from 45.8% to 80.0% (95% CI 37.5%; 87.2%) respectively, Youden index ranged from 0.20 to 0.52. CONCLUSIONS: Multiple single quantitative parameters showed limited predictive ability to identify significant coronary artery stenosis. Visual assessment of DSE appears to be more accurate than single velocity and strain/strain rate markers in the diagnosis of CAD.     

Correlations of conformational parameters and equilibrium conformational states in a variety of beta-D-arabinonucleosides and their analogues.

H nuclear magnetic resonance spectroscopy has been applied to a study of the conformations of a variety of purine and pyrimidine beta-D-arabinofuranosyl nucleosides. The experimental results, together with data collected from the literature, demonstrated the existence of reasonably good correlations between the coupling constants made it possible to define more accurately, than hitherto possible, the conformational states between which equilibria exist in solution. The equilibrium for the arabinonucleosides differs from that previously established for ribonucleosides; in particular, structural modifications and solvent effects may appreciably modify the conformational states between which equilibria exist. Preliminary measurements on some arabinosides in the syn conformation about the glycosidic bond indicated that these do not conform to the foregoing correlations, and will require separate study. A correlation has also been established between the conformation of the arabinose ring and that of the exocyclic 5'-CH2OH group. For both purine and pyrimidine arabinonucleosides, the conformational state 3E of the arabinose ring coexists to some extent with a gauche-gauche conformation of the exocyclic 5'-CH2OH, as in the case of pyrimidine (but not purine) ribonucleosides. Application of the foregoing to some biological problems is described.     

Building a baseline for habitat-forming corals by a multi-source approach,including Web Ecological Knowledge

In the Mediterranean, habitat-forming corals often characterize essential fish habitats. While their distribution is sufficiently known for the western basin, few data are available from the Central-Eastern Mediterranean Sea (CEM). This study fills this gap supplying the largest dataset ever built on the geographical and bathymetric distribution of the most relevant habitat-forming corals (Eunicella cavolini, Eunicella verrucosa, Eunicella singularis, Leptogorgia sarmentosa, Paramuricea clavata, Corallium rubrum and Savalia savaglia) of the CEM. Information collected from different sources such as literature, citizen science, and from the World Wide Web (WWW) was combined. Videos published on the WWW provided additional information on the presence of fishing lines and signs of damage, as well as on the distribution of purple and yellow-purple colonies of Paramuricea clavata. The study highlighted the impressive amount of information that the WWW can offer to scientists, termed here as Web Ecological Knowledge (WEK). The WEK is constantly fuelled by internauts, representing a free, refreshable, long-term exploitable reservoir of information. A quick and easy method to retrieve data from the WWW was illustrated. In addition, the distribution of corals was overlapped to marine protected areas and to the distribution of environmental conditions suitable for coralligenous habitats, fragile biogenic Mediterranean structures hosting complex assemblages in need of strict protection. The collected data allowed identifying priority areas with high species diversity and sites that are impacted by fishing activities. Supplied data can correctly address conservation and restoration policies in the CEM, adding an important contribution to ecosystem-based marine spatial planning.     

Jaatha: a fast composite-likelihood approach to estimate demographic parameters

While information about a species’ demography is interesting in its own right, it is an absolute necessity for certain types of population genetic analyses. The most widely used methods to infer a species’ demographic history do not take intralocus recombination or recent divergence into account, and some methods take several weeks to converge. Here, we present Jaatha, a new composite‐likelihood method that does incorporate recent divergence and is also applicable when intralocus recombination rates are high. This new method estimates four demographic parameters. The accuracy of Jaatha is comparable to that of other currently available methods, although it is superior under certain conditions, especially when divergence is very recent. As a proof of concept, we apply this new method to estimate demographic parameters for two closely related wild tomato species, Solanum chilense and S. peruvianum. Our results indicate that these species likely diverged 1.44·N generations ago, where N is the effective population size of S. chilense, and that some introgression between these species continued after the divergence process initiated. Furthermore, S. peruvianum likely experienced a population expansion following speciation.     

Molecular parameters involved in bee-plant relationships: a biological and chemical approach

Honeybee-plant relationships are based on a conditioning process in which olfactory (plant aroma) and gustatory cues (mainly nectars) are closely linked, leading to a selective foraging behaviour. Among crops dependent upon entomophilous cross-pollination, the sunflower has recently undergone extensive expansion due to hybrid variety selection. Sunflower hybrid seed production is strictly dependent upon pollinating insects, mainly the honeybees, but foragers may have preferences among the parental lines, leading to a lack of pollen carriage and consequently to a decrease of hybrid seed yield. In order to define the role of plant chemicals (aromas, nectars) involved in the pollination process, we set up a study combining behavioural and chemical assays. It appears that even though volatile chemical blends are much more complex compared to glucidic blends, for both kinds of cues only a 'limited chemical pattern' is responsible for foragers' choices. Therefore, it is henceforth possible to take these molecular criteria into account for plant improvement.     

Estimating ancestral distributions of lineages with uncertain sister groups： a statistical approach to Dispersal-Vicariance Analysis and a case using Aesculus L. （Sapindaceae） including fossils

We propose a simple statistical approach for using Dispersal-Vicariance Analysis （DIVA） software to infer biogeographic histories without fully bifurcating trees. In this approach, ancestral ranges are first optimized for a sample of Bayesian trees. The probability P of an ancestral range r at a node is then calculated as P（rY） = ∑t^n=1 F（rY）t Pt where Y is a node, and F（rY） is the frequency of range r among all the optimal solutions resulting from DIVA optimization at node Y, t is one of n topologies optimized, and Pt is the probability of topology t. Node Y is a hypothesized ancestor shared by a specific crown lineage and the sister of that lineage ＂x＂, where x may vary due to phylogenetic uncertainty （polytomies and nodes with posterior probability 〈 100%）. Using this method, the ancestral distribution at Y can be estimated to provide inference of the geographic origins of the specific crown group of interest. This approach takes into account phylogenetic uncertainty as well as uncertainty from DIVA optimization. It is an extension of the previously described method called Bayes-DIVA, which pairs Bayesian phylogenetic analysis with biogeographic analysis using DIVA. Further, we show that the probability P of an ancestral range at Y calculated using this method does not equate to pp＊F（rY） on the Bayesian consensus tree when both variables are 〈 100%, where pp is the posterior probability and F（rY） is the frequency of range r for the node containing the specific crown group. We tested our DIVA-Bayes approach using Aesculus L., which has major lineages unresolved as a polytomy. We inferred the most probable geographic origins of the five traditional sections of Aesculus and ofAesculus californica Nutt. and examined range subdivisions at parental nodes of these lineages. Additionally, we used the DIVA-Bayes data from Aesculus to quantify the effects on biogeographic inference of including two wildcard fossil taxa in phylogenetic analysis. Our analysis resolved the geographic     

Determination of Lennard-Jones interaction parameters using a new procedure

For theoretical and chemical engineering applications, accurate and, if possible, simple models of molecular interactions are needed. We have recently proposed a new procedure for determining Lennard-Jones interaction parameters for fluids, forcing agreement between the values of the pressure obtained from empirical equations of state and those obtained from computer simulations. In this work we obtain new intermolecular Lennard-Jones parameters for non-polar molecules, taking into account their deviation from the spherical shape by means of an acentric factor. Our procedure could help to connect the microscopic and macroscopic worlds and it will be progressively implemented in order to obtain a better representation of other substances and mixtures of chemical interest.     

Estimating developmental states of tumors and normal tissues using a linear time-ordered model

Background  

Tumor cells are considered to have an aberrant cell state, and some evidence indicates different development states appearing in the tumorigenesis. Embryonic development and stem cell differentiation are ordered processes in which the sequence of events over time is highly conserved. The "cancer attractor" concept integrates normal developmental processes and tumorigenesis into a high-dimensional "cell state space", and provides a reasonable explanation of the relationship between these two biological processes from theoretical viewpoint. However, it is hard to describe such relationship by using existed experimental data; moreover, the measurement of different development states is also difficult.     

SNP-based heritability estimation using a Bayesian approach

Heritability is a central element in quantitative genetics. New molecular markers to assess genetic variance and heritability are continually under development. The availability of molecular single nucleotide polymorphism (SNP) markers can be applied for estimation of variance components and heritability on population, where relationship information is unknown. In this study, we evaluated the capabilities of two Bayesian genomic models to estimate heritability in simulated populations. The populations comprised different family structures of either no or a limited number of relatives, a single quantitative trait, and with one of two densities of SNP markers. All individuals were both genotyped and phenotyped. Results illustrated that the two models were capable of estimating heritability, when true heritability was 0.15 or higher and populations had a sample size of 400 or higher. For heritabilities of 0.05, all models had difficulties in estimating the true heritability. The two Bayesian models were compared with a restricted maximum likelihood (REML) approach using a genomic relationship matrix. The comparison showed that the Bayesian approaches performed equally well as the REML approach. Differences in family structure were in general not found to influence the estimation of the heritability. For the sample sizes used in this study, a 10-fold increase of SNP density did not improve precision estimates compared with set-ups with a less dense distribution of SNPs. The methods used in this study showed that it was possible to estimate heritabilities on the basis of SNPs in animals with direct measurements. This conclusion is valuable in cases when quantitative traits are either difficult or expensive to measure.     

Predicting domain-domain interactions using a parsimony approach

We propose a novel approach to predict domain-domain interactions from a protein-protein interaction network. In our method we apply a parsimony-driven explanation of the network, where the domain interactions are inferred using linear programming optimization, and false positives in the protein network are handled by a probabilistic construction. This method outperforms previous approaches by a considerable margin. The results indicate that the parsimony principle provides a correct approach for detecting domain-domain contacts.     

Protein domain decomposition using a graph-theoretic approach

MOTIVATION: Automatic decomposition of a multi-domain protein into individual domains represents a highly interesting and unsolved problem. As the number of protein structures in PDB is growing at an exponential rate, there is clearly a need for more reliable and efficient methods for protein domain decomposition simply to keep the domain databases up-to-date. RESULTS: We present a new algorithm for solving the domain decomposition problem, using a graph-theoretic approach. We have formulated the problem as a network flow problem, in which each residue of a protein is represented as a node of the network and each residue--residue contact is represented as an edge with a particular capacity, depending on the type of the contact. A two-domain decomposition problem is solved by finding a bottleneck (or a minimum cut) of the network, which minimizes the total cross-edge capacity, using the classical Ford--Fulkerson algorithm. A multi-domain decomposition problem is solved through repeatedly solving a series of two-domain problems. The algorithm has been implemented as a computer program, called DomainParser. We have tested the program on a commonly used test set consisting of 55 proteins. The decomposition results are 78.2% in agreement with the literature on both the number of decomposed domains and the assignments of residues to each domain, which compares favorably to existing programs. On the subset of two-domain proteins (20 in number), the program assigned 96.7% of the residues correctly when we require that the number of decomposed domains is two.     

Sweat loss prediction using a multi-model approach

A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15–40°C, RH 25–75%), and exercise intensities (250–600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30–39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.