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PubMed2005

QSPR modeling of pseudoternary microemulsions formulated employing lecithin surfactants: application of data mining, molecular and statistical modeling.

Data mining, computer aided molecular modeling, descriptor calculation, genetic algorithm and multiple linear regression analysis techniques were combined together to generate predictive quantitative structure property relationship (QSPR) models explaining the formation of lecithin-based W/O microemulsions. Ninety-four microemulsion phase diagrams were collected from five different references published over the past few years. Computer-based molecular modeling techniques were then applied on the components of the collected microemulsion systems to generate corresponding plausible three-dimensional (3D) structures. The resulting 3D models were utilized to calculate a group of molecular physicochemical descriptors. Thereafter, genetic algorithm and backward stepwise regression analysis were separately assessed as means for selecting optimal descriptor sets for statistical modeling. The selected descriptors were correlated with microemulsion existence areas employing multiple linear regression analysis. The resulting W/O models were statistically validated and found to be of significant predictive power. The models allowed better understanding of the process of microemulsion formation. Unfortunately, all QSPR modeling efforts directed towards O/W microemulsions failed completely.

Emulsions↗

PubMed2006

Environmental sub models for a macroeconomic model: agricultural contribution to climate change and acidification in Denmark.

Integrated modelling of the interaction between environmental pressure and economic development is a useful tool to evaluate environmental consequences of policy initiatives. However, the usefulness of such models is often restricted by the fact that these models only include a limited set of environmental impacts, which are often energy-related emissions. In order to evaluate the development in the overall environmental pressure correctly, these model systems must be extended. In this article an integrated macroeconomic model system of the Danish economy with environmental modules of energy related emissions is extended to include the agricultural contribution to climate change and acidification. Next to the energy sector, the agricultural sector is the most important contributor to these environmental themes and subsequently the extended model complex calculates more than 99% of the contribution to both climate change and acidification. Environmental sub-models are developed for agriculture-related emissions of CH(4), N(2)O and NH(3). Agricultural emission sources related to the production specific activity variables are mapped and emission dependent parameters are identified in order to calculate emission coefficients. The emission coefficients are linked to the economic activity variables of the Danish agricultural production. The model system is demonstrated by projections of agriculture-related emissions in Denmark under two alternative sets of assumptions: a baseline projection of the general economic development and a policy scenario for changes in the husbandry sector within the agricultural sector.

Agriculture↗

PubMed2004

The coupled dipole model: an integrated model for multiple MEG/EEG data sets.

Often MEG/EEG is measured in a few slightly different conditions to investigate the functionality of the human brain. This kind of data sets show similarities, though are different for each condition. When solving the inverse problem (IP), performing the source localization, one encounters the problem that this IP is ill-posed: constraints are necessary to solve and stabilize the solution to the IP. Moreover, a substantial amount of data is needed to avoid a signal to noise ratio (SNR) that is too poor for source localizations. In the case of similar conditions, this common information can be exploited by analyzing the data sets simultaneously. The here proposed coupled dipole model (CDM) provides an integrated method in which these similarities between conditions are used to solve and stabilize the inverse problem. The coupled dipole model is applicable when data sets contain common sources or common source time functions. The coupled dipole model uses a set of common sources and a set of common source time functions (STFs) to model all conditions in one single model. The data of each condition are mathematically described as a linear combination of these common spatial and common temporal components. This linear combination is specified in a coupling matrix for each data set. The coupled dipole model was applied in two simulation studies and in one experimental study. The simulations show that the errors in the estimated spatial and temporal parameters decrease compared to the standard separate analyses. A decrease in position error of a factor of 10 was shown for the localization of two nearby sources. In the experimental application, the coupled dipole model was shown to be necessary to obtain a plausible solution in at least 3 of 15 conditions investigated. Moreover, using the CDM, a direct comparison between parameters in different conditions is possible, whereas in separate models, the scaling of the amplitude parameters varies in general from data set to data set.

Algorithms↗

PubMed1998

Mixed graphical models for simultaneous model identification and control applied to the glucose-insulin metabolism.

In this paper a method for model identification of biological systems described by stochastic linear differential equations using a new computational technique for statistical Bayesian inference, namely mixed graphical models in the sense of Lauritzen and Wermuth, is presented. The model is identified in terms of biological model parameters and noise parameters. This non-linear estimation problem is solved by means of an exact inference algorithm. The parameter estimates are given as a-posteriori distributions which can be interpreted as fuzzy possibility distributions. For model-based simulations of the underlying biological system the model parameters are represented as uncertain parameters with the distributions obtained from the estimation procedure. We apply the presented methods to a model for the glucose-insulin metabolism: the Karlsburg model for type I diabetes.

Bayes Theorem↗

PubMed1998

The Graz hemisphere splint: a new precise, non-invasive method of replacing the dental arch of 3D-models by plaster models.

Three-dimensional (3-D) anatomical models have proven their great value in the field of cranio-maxillofacial surgery. One major disadvantage is the limited representation of the teeth in milled and stereolithographic models. This is mainly caused by the limited resolution of the CT-scan, especially in the plane perpendicular to that of the scan. A new, precise, non-invasive and standardized method of replacing teeth of 3-D models by plaster models is introduced. The accuracy of tooth replacement is analysed. A plastic human skull is scanned with different interscan distances (scan feed), eight 3-D models are fabricated from this data and the positioning precision of the replaced plaster models in the three main axes is examined. Statistical analysis is carried out with a paired samples t-test. A mean positioning deviation of 0.44 and 0.52 mm in all directions is found using a CT feed of 2 and 3 mm. With 4 and 6 mm, the accuracy decreases showing 0.95 mm and 1.08 mm deviation. No significant difference is found between 2 mm and 3 mm scans, but significant differences between 2, 3 mm and 4, 6 mm are found. For the replacement of model teeth, at least three definitive fixed marks are required. With the aid of a hemisphere, used as a marker, the limited resolution in z-direction is overcome. The hemisphere is visible on several scans as semicircles of varying size. In the 3-D model, it allows precise positioning even on the z-axis enabling the exact replacement of teeth for the first time. A scan feed of 3 mm is sufficient for precise tooth replacement.

Calcium Sulfate↗

PubMed2006

Three-component competitive adsorption model for fixed-bed and moving-bed granular activated carbon adsorbers. Part I. Model development.

Heterogeneous natural organic matter (NOM) present in all natural waters impedes trace organic contaminant adsorption, and predictive modeling of granular activated carbon (GAC) adsorber performance is often compromised by inadequate accounting forthese competitive effects. Thus, a 3-component adsorption model, COMPSORB-GAC, is developed that separately tracks NOM adsorption and its competitive effects as a function of NOM surface loading. In this model, NOM is simplified into two fictive fractions with distinct competitive effects on trace compound adsorption: a smaller, strongly competing fraction that reduces equilibrium capacity and a larger pore-blocking fraction that reduces adsorption kinetics (both external film mass transfer and surface diffusion). COMPSORB-GAC tracks these two NOM fractions, along with the trace compound, and changes adsorption parameters according to the local surface loading of the two NOM fractions. Model parameters are allowed to vary both temporally and spatially to reflect differences in the NOM preloading conditions that occur in GAC columns. This dual-resistance model is based on homogeneous surface diffusion with external film mass-transfer limitations. The governing equations are expressed in a moving-grid finite-difference formulation to accommodate the modeling of spatially varying parameters and moving-bed reactors with counter-current adsorbent flow. A series of short-term adsorption tests with fresh and preloaded GAC is proposed to determine the necessary model input parameters. The accompanying manuscript demonstrates the parameterization procedure and verifies the model with experimental data.

Adsorption↗

PubMed2005

Evidence for the usefulness of in vitro dialyzability, Caco-2 cell models, animal models, and algorithms to predict zinc bioavailability in humans.

Low bioavailability of zinc in certain diet types may contribute to zinc deficiency and its consequences in populations. As a result, several experimental models including animal models, in vitro dialyzability models, and Caco-2 cells, have been used to study these factors and estimate their impact on human zinc absorption. For the most part, consistency has been observed between the latter models and human absorption studies to identify factors that enhance or inhibit zinc bioavailability. However, dialyzability methods are limited to modeling luminal interactions among the factors as they affect zinc availability while Caco-2 cells can model luminal effects and uptake by intestinal cells. Neither animal nor in vitro methods can predict the magnitude of zinc absorption at the level of the human organism. Caco-2 cells will be useful models for understanding the mechanisms of intestinal zinc absorption. The in vitro methods are also limited to modeling absorption and the interactions that occur. Algorithms to estimate zinc absorption, based on dietary content of zinc absorption modifiers, have been derived from human studies. An algorithm derived from studies of zinc retention from radioactive zinc-labeled test meals underestimates zinc absorption compared to that derived from measurement of true zinc absorption from total diets using isotopic tracer methods. Based on the latter, phytate appears to be the only major inhibitor of zinc absorption from typical diets. Ultimately, population-based studies are needed to determine the impact of dietary factors that modulate zinc absorption on the adequacy of zinc status.

Algorithms↗

PubMed1990

Learning-forgetting independence, unidimensional memory models, and feature models: comment on Bogartz (1990).

In his recent articles, Bogartz offered a definition of what it means for forgetting rate to be independent of degree of original learning. He showed that, given this definition, independence is confirmed by extant data. Bogartz also criticized Loftus's (1985b) proposed method for testing independence. In this commentary, we counter Bogartz's criticisms and then offer two observations. First, we show that Loftus's horizontal-parallelism test distinguishes between two interesting class of memory models: unidimensional models wherein the memory system's state can be specified by a single number and multidimensional models wherein at least two numbers are required to specify the memory system's state. Independence by Loftus's definition is implied by a unidimensional model. Bogartz's definition, in contrast, is consistent with either model. Second, to better understand the constraints on memory mechanisms dictated by the mathematics of the models under consideration, we develop a simple but general feature model of learning and forgetting. We demonstrate what constraints must be placed on this model to make learning and forgetting rate independent by Loftus's and by Bogartz's definitions.

Humans↗

PubMed2005

Beam modeling and verification of a photon beam multisource model.

Dose calculations for treatment planning of photon beam radiotherapy require a model of the beam to drive the dose calculation models. The beam shaping process involves scattering and filtering that yield radiation components which vary with collimator settings. The necessity to model these components has motivated the development of multisource beam models. We describe and evaluate clinical photon beam modeling based on multisource models, including lateral beam quality variations. The evaluation is based on user data for a pencil kernel algorithm and a point kernel algorithm (collapsed cone) used in the clinical treatment planning systems Helax-TMS and Nucletron-Oncentra. The pencil kernel implementations treat the beam spectrum as lateral invariant while the collapsed cone involves off axis softening of the spectrum. Both algorithms include modeling of head scatter components. The parameters of the beam model are derived from measured beam data in a semiautomatic process called RDH (radiation data handling) that, in sequential steps, minimizes the deviations in calculated dose versus the measured data. The RDH procedure is reviewed and the results of processing data from a large number of treatment units are analyzed for the two dose calculation algorithms. The results for both algorithms are similar, with slightly better results for the collapsed cone implementations. For open beams, 87% of the machines have maximum errors less than 2.5%. For wedged beams the errors were found to increase with increasing wedge angle. Internal, motorized wedges did yield slightly larger errors than external wedges. These results reflect the increased complexity, both experimentally and computationally, when wedges are used compared to open beams.

Algorithms↗

PubMed2026

A trainable language model with potential to modulate translation rates in non-model organisms by generating upstream untranslated region sequence libraries.

Tuning protein expression in non-model organisms is often constrained by the lack of validated genetic parts and predictive design tools. Translational tuning through the modulation of upstream untranslated regions (5'-UTRs) offers a potentially organism-agnostic route, but existing methods typically rely on mechanistic assumptions, prior knowledge that may not be available in non-model contexts, or the screening of sequence libraries. Here, we present a simple generative approach for creating synthetic 5'-UTR libraries based solely on the genomic sequence statistics of any desired organism. The method uses a sliding-window n-gram language model applied to native 5'-UTR sequences to produce novel sequences that preserve organism-specific base distributions and motifs without hard-coding specific motifs or mechanistic rules into inflexible statistical templates. We have applied this approach to the model bacterium Escherichia coli and the non-model probiotic Limosilactobacillus reuteri. Libraries of approximately 1,000 sequences were generated for each organism, from which about 100 unique sequences were experimentally tested for translation of a fluorescent reporter protein. In both organisms, the synthetic libraries yielded a broad range of translation levels from this relatively small number of tested variants. Sequences derived from an organism's own genomic statistics provided a more uniformly distributed range of translation rates in that organism than sequences derived from the other species. Correlations of individual sequence performance across the two species were weak, and thermodynamic predictions of ribosome binding strength showed very little predictive power, especially in the non-model L. reuteri. The results demonstrate that simple statistical language model approaches applied to genomic data can generate functional translational regulatory sequence libraries without detailed mechanistic knowledge or explicit reference to consensus motifs. The approach requires minimal computational resources, avoids reproducing native sequences, and can be readily applied to any organism with a sequenced genome. This strategy may lower technical barriers to expression tuning in non-model organisms.

5' Untranslated Regions↗

PubMed2005

Modeling quantitative trait Loci and interpretation of models.

A quantitative genetic model relates the genotypic value of an individual to the alleles at the loci that contribute to the variation in a population in terms of additive, dominance, and epistatic effects. This partition of genetic effects is related to the partition of genetic variance. A number of models have been proposed to describe this relationship: some are based on the orthogonal partition of genetic variance in an equilibrium population. We compare a few representative models and discuss their utility and potential problems for analyzing quantitative trait loci (QTL) in a segregating population. An orthogonal model implies that estimates of the genetic effects are consistent in a full or reduced model in an equilibrium population and are directly related to the partition of the genetic variance in the population. Linkage disequilibrium does not affect the estimation of genetic effects in a full model, but would in a reduced model. Certainly linkage disequilibrium would complicate the detection of QTL and epistasis. Using different models does not influence the detection of QTL and epistasis. However, it does influence the estimation and interpretation of genetic effects.

Gene Frequency↗

PubMed1995

Are two mutations sufficient to cause cancer? Some generalizations of the two-mutation model of carcinogenesis of Moolgavkar, Venzon, and Knudson, and of the multistage model of Armitage and Doll.

Some generalizations of the two-mutation carcinogenesis model of Moolgavkar, Venzon, and Knudson (to allow for an arbitrary number of mutational stages) and of the multistage model of Armitage and Doll are shown to have the property that, at least in the case when the parameters of the model are eventually constant, the excess relative and absolute risks following changes in any of the parameters will eventually tend to zero. It is also shown that when the parameters governing the processes of cell division, death, or additional mutation at the penultimate stage are subject to perturbations, there are relatively large fluctuations in the hazard function for carcinogenesis for either model, which start almost as soon as the parameters are changed. For this reason it appears that without some extra stochastic "stage" appended (such as might be provided by consideration of the process of development of a malignant clone clone from a single malignant cell) the two-mutation model is not well able to describe the pattern of excess risk for solid cancers that is often seen after exposure to ionizing radiation, although leukemia may be better fitted by the two-mutation model in this respect. An examination of the results of perturbing various of the parameters for models that require three or more mutations provides indications that these models are easier to reconcile with the results from a body of epidemiological data relating to solid cancers.

Biometry↗

PubMed1997

Evaluation of comparative protein structure modeling by MODELLER-3.

We evaluate homology-derived 3D models of dihydrofolate reductase (DFR1), phosphotransferase enzyme IIA domain (PTE2A3), and mouse/human UBC9 protein (UBC9(24)) which were submitted to the second Meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP). The DFR1 and PTE2A3 models, based on alignments without large errors, were slightly closer to their corresponding X-ray structures than the closest template structures. By contrast, the UBC9(24) model was slightly worse than the best template due to a misalignment of the N-terminal helix. Although the current models appear to be more accurate than the models submitted to the CASP meeting in 1994, the four major types of errors in side chain packing, position and conformation of aligned segments, position and conformation of inserted segments, and in alignment still occur to almost the same degree. The modest improvement probably originates from the careful manual selection of the templates and editing of the alignment, as well as from the iterative realignment and model building guided by various model evaluation techniques. This iterative approach to comparative modeling is likely to overcome at least some initial alignment errors, as demonstrated by the correct final alignment of the C terminus of DFR.

Amino Acid Sequence↗

PubMed1996

Modelling lymphocytic leukaemia incidence in England and Wales using generalizations of the two-mutation model of carcinogenesis of Moolgavkar, Venzon and Knudson.

Generalizations of the two-mutation carcinogenesis model of Moolgavkar, Venzon and Knudson (MVK) are fitted to England and Wales lymphocytic leukaemia incidence data covering the period 1971-1988. Both acute lymphocytic leukaemia (ALL) and chronic lymphocytic leukaemia (CLL) can be fitted by a model with two mutations. These two-mutation models are such that the first (but not the second) mutation rate and the susceptible stem cell population vary rapidly with age. CLL is also adequately fitted by a model with three mutations, and the mutation rates and the variation in the stem cell population numbers implied by the three-mutation model are more plausible than those of the two-mutation model. For CLL there are no significant differences between the sexes in either of the optimal models fitted, but this is not the case for ALL. Thus the original MVK model adequately describes population rates of lymphocytic leukaemia in England and Wales.

Adolescent↗

PubMed2006

Thermodynamic modeling of activity coefficient and prediction of solubility: Part 2. Semipredictive or semiempirical models.

The solubility of stearic acid, ranitidine hydrochloride, and stavudine were predicted in selected organic solvents. The experimental solubility data of stearic acid and ranitidine hydrochloride were reported in previous work of the authors and stavudine's solubility was measured in this work. Equilibrium aqueous solubility of crystalline stauvudine was determined at controlled temperatures by stirring and filtration, with spectrophotometric quantification. The new model developed in Part 11 of this communication was modified as a semipredictive model with two adjustable parameters. Predicting the solubility data with the NRTL model using just one experimental point resulted in a big error while the modified new model and the UNIQUAC model showed much smaller errors. A new method was proposed in this work for predicting the solubility data of all polymorphs of a given compound using the experimental solubility data of one of the polymorphs of the same chemical compound. Although in general, the UNIQUAC model predictions were marginally superior, the new model is simpler and does not require the molecular parameters such as Van der Waals area and volume. The solubility prediction in a mixture of solvents using the NRTL and UNIQUAC models was also discussed.

2-Propanol↗

PubMed1984

Analysis of pharmacokinetic data using parametric models--1: Regression models.

This is the first in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article, the purposes of modelling are discussed; regression models for individuals and populations are defined; and structural and variance models are discussed as the two required submodels of the overall regression model. Topics of future articles are: point estimates of parameters; interval estimates of parameters; model criticism and choosing among contending models; population kinetic models and estimation; and elements of optimal design.

Humans↗

PubMed1995

Modeling household fertility decisions: estimation and testing of censored regression models for count data.

"This paper adds to the recent body of research on fertility by estimating and testing censored Poisson regression models and censored negative binomial regression models of household fertility decisions. A novel feature of this study is that in each case the censoring threshold varies from individual to individual. Also, a Lagrange multiplier or score test is used to investigate overdispersion. In these regression models the dependent variable is the number of children. In this situation, censored Poisson regression models and censored negative binomial regression models have statistical advantages over OLS, uncensored Poisson regression models, and uncensored negative binomial regression models. The censored models employed in this study are estimated using panel data collected from the Consumer Expenditure Survey compiled by the [U.S.] Bureau of Labor Statistics."

Americas↗

PubMed1996

A population pharmacokinetic model for docetaxel (Taxotere): model building and validation.

A sparse sampling strategy (3 samples per patient, 521 patients) was implemented in 22 Phase 2 studies of docetaxel (Taxotere) at the first treatment cycle for a prospective population pharmacokinetic evaluation. In addition to the 521 Phase 2 patients, 26 (data rich) patients from Phase I studies were included in the analysis. NONMEM analysis of an index set of 280 patients demonstrated that docetaxel clearance (CL) is related to alpha 1-acid glycoprotein (AAG) level, hepatic function (HEP), age (AGE), and body surface area (BSA). The index set population model prediction of CL was compared to that of a naive predictor (NP) using a validation set of 267 patients. Qualitatively, the dependence of CL on AAG, AGE, BSA, and HEP seen in the index set population model was supported in the validation set. Quantitatively, for the validation set patients overall, the performance (bias, precision) of the model was good (7 and 21%, respectively), although not better than that of the NP. However, in all the subpopulations with decreased CL, the model performed better than the NP; the more the CL differed from the population average, the better the performance. For example, in the subpopulation of patients with AAG levels > 2.27 g/L (n = 26), bias and precision of model predictions were 24 and 32% vs. 53 and 53%, respectively, for the NP. The prediction of CL using the model was better (than that of the NP) in 73% of the patients. The population model was redetermined using the whole population of 547 patients and a new covariate, albumin plasma level, was found to be a significant predictor in addition to those found previously. In the final model, HEP, AAG, and BSA are the main predictors of docetaxel CL.

Antineoplastic Agents, Phytogenic↗