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At least 19 recordsLinked to original sources

PubMed1991

Two models of brief strategic therapy: the MRI model and the de Shazer model.

This paper presents and compares two models of brief strategic therapy: the MRI model, and the de Shazer model. Both models were implemented simultaneously in a community mental health child clinic. Each model is presented, and then illustrated by a case study. Apparent are the benefits that can be gained through exposure to each model, and the point that premature integration of the models might detract from the processes. The staff members' experience with the two models, and the differences in the therapist attitude are discussed.

Child↗

PubMed1992

[Mucus models for investigation of intestinal absorption mechanisms. 4. Comparison of mucus models with absorption models in vivo and in situ for prediction of intestinal drug absorption].

Intestinal absorption with an in vitro model using pig intestinal mucus was examined by means of in vivo and in situ experiments in the rat. With 10 compounds of different structure, in vitro, in situ, and in vivo models were tested. The in vitro model in the present form can only simulate the first step of intestinal absorption, namely diffusion through the mucus layer. Indeed, we found that one function of the intestinal mucus can be described being a molecular sieve with a molecular mass (MM) cut off within the range of about 600 to 700 [g/mol]. Absorption of substances with higher molecular mass remains at a low level. With the mucus model prediction of intestinal absorption of hydrophilic substances with MM < 600 to 700 [g/mol] will be possible, if the mass transport in the mucus layer is rate limiting. Independent of polarity, it is also valid for substances of MM > 600 to 700 [g/mol]. Estimation however, is not valid for lipophilic substances and MM < 600 to 700 [g/mol], when mass transport from the mucus to the adjacent compartments is rate limiting. Further optimization of the mucus model for a more extensive application seems possible and reasonable with respect to saving in vivo experiments with animals.

Animals↗

PubMed1977

Model of protein folding: incorporation of a one-dimensional short-range (Ising) model into a three-dimensional model.

In this paper, we have incorporated a one-dimensional short-range model into a three-dimensional model for protein folding. It has been applied, by extending the concept of the three-step mechanism for protein folding proposed in our previous paper, to simulate the folding of bovine pancreatic trypsin inhibitor, using a Monte Carlo procedure in all three steps, A, B, and C. The statistical mechanical ensemble treatment of the short-range model serves as a constraint on the Monte Carlo procedure, in which conformational transitions are introduced. The preliminary results of 10 independent Monte Carlo trials indicate that, while folding is achieved, improvements are required in order to account for the correct three-dimensional structure of a globular protein.

Methods↗

PubMed1990

Tertiary structure of RNase Pch1 predicted from the model structure of RNase Ms and the crystal structure of RNase T1. Comparison among the model structures--testing the limits of modelling by homology.

In this paper we predict the structure of RNase Pch1 as modelled from the previously predicted structure of RNase Ms and the crystal structure of RNase T1 in the complex with 2'GMP. The predicted structures and their initial energy minimized structural RNase T1 template are compared. The predicted structures of RNase Pch1 show, independent of their prediction form RNase Ms or T1, a higher structural similarity to RNase T1 than to RNase Ms, in agreement with higher sequence similarity and specificity - RNases T1 and Pch1 are specific for guanine whereas RNase Ms is base-unspecific with preference for guanine.

Amino Acid Sequence↗

PubMed1992

Numerical comparisons of two formulations of the logistic regressive models with the mixed model in segregation analysis of discrete traits.

Segregation analysis of discrete traits can be conducted by the classical mixed model and the recently introduced regressive models. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affected persons have a liability exceeding a threshold. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype effects, the phenotypes of older relatives, and other covariates. A formulation of the regressive models, based on an underlying liability model, has been recently proposed. The regression coefficients on antecedents are expressed in terms of the relevant familial correlations and a one-to-one correspondence with the parameters of the mixed model can thus be established. Computer simulations are conducted to evaluate the fit of the two formulations of the regressive models to the mixed model on nuclear families. The two forms of the class D regressive model provide a good fit to a generated mixed model, in terms of both hypothesis testing and parameter estimation. The simpler class A regressive model, which assumes that the outcomes of children depend solely on the outcomes of parents, is not robust against a sib-sib correlation exceeding that specified by the model, emphasizing testing class A against class D. The studies reported here show that if the true state of nature is that described by the mixed model, then a regressive model will do just as well. Moreover, the regressive models, allowing for more patterns of family dependence, provide a flexible framework to understand gene-environment interactions in complex diseases.

Computer Simulation↗

PubMed1991

The Hodgkin-Huxley Na+ channel model versus the five-state Markovian model.

In describing the Na+ channel-gating kinetics, it is generally believed the Hodgkin-Huxley model is inadequate and other types of Markovian models are more appropriate. In this paper, we perform detailed kinetic analyses to find out whether the Hodgkin-Huxley model is really unacceptable. Specifically, we consider two models for the analyses: A five-state Markovian model that allows inactivation to take place before opening and a Hodgkin-Huxley eight-state model. The criteria used to check the goodness of the two models are (a) Akaike's information criterion; (b) chi 2 tests on the waiting-time, open-time, and closed-time distributions, and the number of openings per record; and (c) comparison between all latency distributions and the probability of the open state predicted from the two models. In order to do this, we first develop a method of constructing probability density histograms of a specified event (e.g., waiting time, closed time, open time, number of openings per patch) from the multichannel patch-clamp recordings. The goodness of our method is checked by simulating multichannel patch recordings using a multinomial random number generator. Our kinetic analysis on the single Na+ channel recordings from the cardiac cells revealed that (a) on the basis of Akaike's information criterion, the Hodgkin-Huxley model is definitely a better model than the five-state model, but (b) on the basis of chi 2 tests on the probability density functions, the latter model is slightly better than the former. We find no evidence that the Hodgkin-Huxley model is inferior to the five-state model for this cell type.

Ion Channel Gating↗

PubMed1992

Structure and parameterization of pharmacokinetic models: their impact on model predictions.

There has been an increasing interest in physiologically based pharmacokinetic (PBPK) models in the area of risk assessment. The use of these models raises two important issues: (1) How good are PBPK models for predicting experimental kinetic data? (2) How is the variability in the model output affected by the number of parameters and the structure of the model? To examine these issues, we compared a five-compartment PBPK model, a three-compartment PBPK model, and nonphysiological compartmental models of benzene pharmacokinetics. Monte Carlo simulations were used to take into account the variability of the parameters. The models were fitted to three sets of experimental data and a hypothetical experiment was simulated with each model to provide a uniform basis for comparison. Two main results are presented: (1) the difference is larger between the predictions of the same model fitted to different data sets than between the predictions of different models fitted to the dame data; and (2) the type of data used to fit the model has a larger effect on the variability of the predictions than the type of model and the number of parameters.

Animals↗

PubMed2026

Statistical test to compare the linkage model and the admixture model based on central limit results.

In the Admixture Model, the probability that an individual carries a certain allele at a specific marker depends on the allele frequencies in K ancestral populations and the proportion of the individual's genome originating from these populations. The markers are assumed to be independent. The Linkage Model is a Hidden Markov Model that extends the Admixture Model by incorporating linkage between neighboring loci. We prove consistency and asymptotic normality of maximum likelihood estimators for the ancestry of individuals in the Linkage Model, complementing earlier results by (Pfaff et al., 2004; Pfaffelhuber and Rohde, 2022; Heinzel, 2025) for the Admixture Model. These results are used to prove that a statistical test that allows for model selection between the Admixture Model and the Linkage Model is an asymptotic level-α-test. Finally, we demonstrate the practical relevance of our results by applying the test to real-world data from The 1000 Genomes Project Consortium (2015).

Genetic Linkage↗

PubMed1992

Neural networks in pharmacodynamic modeling. Is current modeling practice of complex kinetic systems at a dead end?

Neural networks (NN) are computational systems implemented in software or hardware that attempt to simulate the neurological processing abilities of biological systems, in particular the brain. Computational NN are classified as parallel distributed processing systems that for many tasks are recognized to have superior processing capability to the classical sequential Von Neuman computer model. NN are recognized mainly in terms of their adaptive learning and self-organization features and their nonlinear processing capability and are considered most suitable to deal with complex multivariate systems that are poorly understood and difficult to model by classical inductive, logically structured modeling techniques. A NN is applied to demonstrate one of the potentially many applications of NN for modeling complex kinetic systems. The NN was used to predict the effect of alfentanil on the heart rate resulting from a complex infusion scheme applied to six rabbits. Drug input-drug effect data resulting from a repeated, triple infusion rate scheme lasting from 30 to 180 min was used to train the NN to recognize and emulate the input-effect behavior of the system. With the NN memory fixed from the 30- to 180-min learning phase the NN was then tested for its ability to predict the effect resulting from a multiple infusion rate scheme applied in the subsequent 180 to 300 min of the experiment. The NN's ability to emulate the system (30-180 min) was excellent and its predictive extrapolation capability (180-300 min) was very good (mean relative prediction accuracy of 78%). The NN was best in predicting the higher intensity effect and was able to identify and predict an overshoot phenomenon likely caused by a withdrawal effect from acute tolerance. Current modeling philosophy and practice is discussed on the basis of the alternative offered by NN in the modeling of complex kinetic systems. In modeling such systems it is questioned whether traditional modeling practice that insists on structure relevance and conceptually pleasing structures has any practical advantages over the empirical NN approach that largely ignores structure relevance but concentrates on the emulation of the behavior of the kinetic system. The traditional searching for appropriate models of complex kinetic systems is a painstakingly slow process. In contrast, the search for empirical models using NN will continue to improve, limited only by technological advances supporting the very promising NN developments.

Alfentanil↗

PubMed1992

The in vivo orthodontic banding model for vital teeth and the in situ orthodontic banding model for hard-tissue slabs.

This paper presents the orthodontic banding model for vital teeth and the orthodontic in situ model for slabs of enamel, root surface, dentin, or other mineralized tissues such as shark enamel. The model for vital teeth is an in vivo model, since a crevice for plaque accumulation is created behind orthodontic bands on the buccal enamel surfaces of teeth in situ. Visible white-spot lesions are usually seen after a four-week banding period in the absence of fluoride. The microbiological flora developed behind the bands shows a similarity to that of natural caries. Microradiographic data show that the initial lesion is a softening of the enamel surface. Later, a subsurface lesion develops. A modification of the model has been developed for the use of slabs of mineralized tissues. In this model, slabs are mounted on a removable appliance. The slabs are covered with orthodontic banding material for plaque accumulation. Lesion development in enamel in the two model systems is almost identical. The benefit of the in vivo model is that caries development can be studied on vital teeth in young individuals. The model is independent of the patient's cooperation. No special diet is required, e.g., no sucrose rinsing. In the in situ model, slabs could be examined after one study period and then replaced for another period.

Dental Caries↗

PubMed1991

Regressive logistic models for familial diseases: a formulation assuming an underlying liability model.

Statistical models have been developed to delineate the major-gene and non-major-gene factors accounting for the familial aggregation of complex diseases. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affection is defined by a threshold on the liability scale. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype, phenotypes of antecedents and other covariates. An equivalence between these two approaches cannot be derived analytically. I propose a formulation of the regressive logistic models on the supposition of an underlying liability model of disease. Relatives are assumed to have correlated liabilities to the disease; affected persons have liabilities exceeding an estimable threshold. Under the assumption that the correlation structure of the relatives' liabilities follows a regressive model, the regression coefficients on antecedents are expressed in terms of the relevant familial correlations. A parsimonious parameterization is a consequence of the assumed liability model, and a one-to-one correspondence with the parameters of the mixed model can be established. The logits, derived under the class A regressive model and under the class D regressive model, can be extended to include a large variety of patterns of family dependence, as well as gene-environment interactions.

Environment↗

PubMed1991

Model of kinetic behavior of deoxyglucose in heterogeneous tissues in brain: a reinterpretation of the significance of parameters fitted to homogeneous tissue models.

Effects of tissue heterogeneity on regional CMRglc (rCMRglc) calculated by use of the deoxyglucose (DG) method at 45 min following the pulse of DG were evaluated in simulation studies. A theoretical model was developed to describe the kinetics of DG uptake and metabolism in heterogeneous brain tissues. Rate constants were fitted to simulation data for mixed tissue and rCMRglc computed on the basis of this tissue heterogeneity model. The results were compared with those obtained by use of the original model of the DG method for homogeneous tissue, both without (3K model) and with (4K model) a term to describe an apparent loss of deoxyglucose-6-phosphate (DG-6-P). As a direct consequence of tissue heterogeneity, the effective rate constant for phosphorylation of DG, k3*, declined with time. To compensate for the time-changing k3*, estimates of the dephosphorylation rate constant, k4*, were artifactually high when the 4K model was used, even though no dephosphorylation of DG-6-P actually occurred. The present study demonstrates that the finding of a significant k4*, at least within 45 min following a pulse of DG, may not represent dephosphorylation at all, but rather the consequence of measuring radioactivity in a heterogeneous tissue and applying a model designed for a homogeneous tissue. Furthermore, the high estimates of k4* resulted in significant overestimation of rCMRglc. When rCMRglc was computed with the conventional single-scan or autoradiographic method at 45 min after a pulse of DG, the 3K and tissue heterogeneity models yielded values that were within 5% of the true weighted average value for the heterogeneous tissue as a whole. We conclude that the effects of tissue heterogeneity alone can give the appearance of product loss, even when none occurs, and that the use of the 4K model with the assumption of product loss in the 45-min experimental period recommended for the DG method may lead to overestimation of the rates of glucose utilization.

Brain↗

PubMed2023

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.

Preprint↗

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↗

PubMed2025

Refining sequence-to-activity models by increasing model resolution.

Decoding the cis-regulatory syntax that controls gene expression is essential for improving our understanding of cell differentiation and disease. To identify regulatory motifs and their regulatory syntax, deep learning based sequence-to-activity (S2A) models learn transcription factor binding motifs and their combinations from DNA sequence by modeling measured chromatin accessibility. Previously, we developed AI-TAC, a S2A model that predicts chromatin accessibility across various immune cell types in multi-task fashion, effectively decoding the regulatory syntax underlying immune cell differentiation. While ATAC-seq is commonly used to measure regional accessibility, it also provides high-resolution profiles, the distribution of Tn5 insertion sites, that offer additional insights into the precise location and strength of TF binding sites. Here we demonstrate that modeling ATAC-seq profiles alongside accessibility consistently improves predictions of differential chromatin accessibility across cell types. Moreover, we also find that multi-task learning across related immune cell types consistently outperforms single-task models. To understand what additional information bpAITAC learns from ATAC-seq profiles, we systematically compare sequence attributions from models trained with and without ATAC-seq profiles. We identify novel motifs with strong effect sizes that emerge only when profile data is included. Our findings suggest that modeling ATAC-seq at base-pair resolution enables the model to learn a more nuanced and sensitive representation of the cis-regulatory syntax driving immune cell-specific chromatin landscapes.

ATAC-seq↗

PubMed1992

Building population pharmacokinetic--pharmacodynamic models. I. Models for covariate effects.

One major task in clinical pharmacology is to determine the pharmacokinetic-pharmacodynamic (PK-PD) parameters of a drug in a patient population. NONMEM is a program commonly used to build population PK-PD models, that is, models that characterize the relationship between a patient's PK-PD parameters and other patient specific covariates such as the patient's (patho) physiological condition, concomitant drug therapy, etc. This paper extends a previously described approach to efficiently find the relationships between the PK-PD parameters and covariates. In a first step, individual estimates of the PK-PD parameters are obtained as empirical Bayes estimates, based on a prior NONMEN fit using no covariates. In a second step, the individual PK-PD parameter estimates are regressed on the covariates using a generalized additive model. In a third and final step, NONMEM is used to optimize and finalize the population model. Four real-data examples are used to demonstrate the effectiveness of the approach. The examples show that the generalized additive model for the individual parameter estimates is a good initial guess for the NONMEM population model. In all four examples, the approach successfully selects the most important covariates and their functional representation. The great advantage of this approach is speed. The time required to derive a population model is markedly reduced because the number of necessary NONMEM runs is reduced. Furthermore, the approach provides a nice graphical representation of the relationships between the PK-PD parameters and covariates.

Adult↗

PubMed1991

Domain of validity of classical models of leucine metabolism assessed by compartmental modeling.

Whole-body modeling of in vivo leucine (an essential amino acid) metabolism is fundamentally difficult due to the complexity of the system. This has favored the use of two simple kinetic models, the so-called primary and reciprocal pool models, to interpret tracer data, but their domain of validity is uncertain. We define here the error of these two approaches by using comprehensive compartmental models of leucine metabolism as true representations of the leucine system. Of particular interest is the comparison of the two simple models with an 11-compartment model characterized by a rich intracellular compartmentation that has recently been proposed as a sound physiological description of the system. Formulas are derived that define in structural terms the error of the primary and reciprocal pool models.

Humans↗

PubMed1992

Sexual mixing models: a comparison of analogue deterministic and stochastic models.

Models for sexual partner choice are discussed for the case of highly variable sexual activity in the population. It is demonstrated that the variances in the number of infected persons may be extremely large. For the random mixing model, higher order cumulants are also evaluated. On the basis of these results the applicability of deterministic models and models for expectations only are questioned. A general model is proposed for handling nonrandom, or correlated, mixing. The problem of inconsistency is overcome by considering the couples having sex as the natural unit in the model. In the case of s discrete homogeneous groups it is shown that only (s2) parameters defining the interaction between the groups can be chosen freely. Finally, the effect of correlation in partner choice is demonstrated by a bivariate lognormal model for partner choice.

Female↗