[Analysis of indicator dilution in an experimental flow model using quantitative angiodensitometry with injection of the indicator by an automatic syringe].
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Absorptiometry, Photon↗
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The effect of orbital prefrontal cortex lesions on performance on a progressive ratio schedule: implications for models of inter-temporal choice.
In a previous experiment [Kheramin S, Body S, Mobini S, Ho M-Y, Velazquez-Martinez DN, Bradshaw CM, et al. Effects of quinolinic acid-induced lesions of the orbital prefrontal cortex on inter-temporal choice: a quantitative analysis. Psychopharmacology 2002;165: 9-17], destruction of the orbital prefrontal cortex (OPFC) in rats altered choice between two delayed food reinforcers, enhancing preference for the larger reinforcer. Theoretical analysis based on a quantitative model of inter-temporal choice [Ho M-Y, Mobini S, Chiang T-J, Bradshaw CM, Szabadi E. Theory and method in the quantitative analysis of 'impulsive choice' behaviour: implications for psychopharmacology. Psychopharmacology 1999;146:362-72] indicated that the lesion had increased the relative value of the larger of the two reinforcers due to a general reduction of absolute reinforcer value. The present experiment tested this hypothesis using a reinforcement schedule that did not entail either explicit choice or delayed reinforcement. Ten rats received quinolinic acid-induced lesions of the OPFC, and ten rats received sham lesions. The rats were trained under a progressive-ratio schedule of food reinforcement for 60 daily sessions. Response rates in successive ratios were a bitonic (inverted-U) function of ratio size. Analysis of the data using a three-parameter equation derived from a quantitative model of ratio schedule performance [Killeen PR. Mathematical principles of reinforcement. Behav. Brain Sci. 1994;17:105-72] revealed that the parameter specifying hypothetical reinforcer value was significantly lower in the OPFC-lesioned group than in the sham-lesioned group, consistent with the hypothesis that destruction of the OPFC resulted in devaluation of the food reinforcer.
Animals↗
Interaction of some peroxisome proliferators with the mouse liver peroxisome proliferator-activated receptor (PPAR): a molecular modelling and quantitative structure-activity relationship (QSAR) study.
1. The three-dimensional structure of a portion of the ligand-binding domain of the mouse liver peroxisome proliferator-activated receptor (PPAR) described by Issemann and Green (1990) has been modelled from amino acid sequence data. 2. By inspection of the three-dimensional structure of the portion of the PPAR ligand-binding domain, a putative binding site for peroxisome proliferators, consisting of one isoleucine, one lysine and two phenylalanine moieties (residues 354, 358, 359 and 361, respectively), has been identified. 3. The interaction of 12 peroxisome proliferators with the putative PPAR binding site has been investigated and energetics of binding calculated from ligand-bound and ligand-free receptor geometries. 4. The interaction data have been used to establish quantitative structure-activity relationships (QSARs) between peroxisome proliferator binding and either PPAR activation in COS1 cells or induction of palmitoyl-CoA oxidation in rat hepatocyte cultures. 5. The results are discussed in terms of the role of PPAR in the mechanism of initiation of peroxisome proliferation in rodent liver.
Amino Acid Sequence↗
The genetics of schizophrenia. Current knowledge and future directions.
Multiple research paradigms have provided evidence for a substantial genetic component in the etiology of schizophrenic disorders. This article reviews the major research strategies which have been employed in the examination of the genetic hypothesis in schizophrenia. Family studies have provided overwhelming support regarding familial transmission but cannot clearly resolve issues related to genetic-versus-environmental mechanisms. Twin and adoption studies, however, offer consistent evidence for a substantial genetic component and indicate environmental familial factors to be much less important. Quantitative modeling studies represent more specific attempts to identify the genetic mechanism and mode of inheritance responsible for the familial distribution of schizophrenia. To date, however, these quantitative models have not unequivocally supported a specific mode of genetic transmission. For instance, relevant studies provide little support for the mechanism of single major locus inheritance. Furthermore, although a mechanism involving two, three, or four loci cannot be ruled out, there is no compelling support for such models. The multifactorial polygenic model has received the most support and indicates that genetic factors play a greater role than environmental factors in familial transmission. A mixed genetic model including both a multifactorial component and a single major locus cannot be ruled out. Finally, studies of linkage analysis offer a more powerful technique used for testing the hypothesis of a single pathogenic gene, but the results of linkage analysis in schizophrenia are still preliminary and inconsistent. Evidence for a chromosome 5 gene locus has been provided in some studies but not replicated in others. The important implications of genetic-phenotypic heterogeneity and methodological deficiencies are discussed with respect to limitations on the interpretability of these studies and directions for future research.
Adoption↗
Possible clinical models for quantitative comparison of effects of different radiations on tumours.
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Clinical Trials as Topic↗
A model for quantitative measurement of sulfur mustard skin lesions in the rabbit ear.
The search for treatment and protection against the vesicant and inflammatory skin lesions induced by sulfur mustard suffers from the lack of a good in vivo reproducible model. We applied sulfur mustard (25-500 microg/cm2) to the outer surface of the ears of 10 rabbits and measured the edema formation 12, 24 and 48 h post-application with a caliper especially designed for soft matter. There was a dose-dependent linear increase in edema magnitude in the range from 25 to 150 microg/cm2. Maximal edema was observed after 12 h. There was a 12% reduction in edema size 24 h after application and a further decrease after 48 h. Skin thickness, inflammatory cell infiltrate, necrosis and vesiculation were evaluated in biopsies taken after 24 h. We found the same dose-related increase both in skin thickness and in degree of blister formation. This simple dose-response in vivo model can be used for evaluation of the dermal inflammation induced by topical application of sulfur mustard. This model has the additional advantage of a built-in control, namely the untreated contralateral ear. Consequently, this model can serve as a useful tool for future screening of potential compounds for prevention and treatment of sulfur mustard-induced skin lesions.
Animals↗
Computational models for neurogenic gene expression in the Drosophila embryo.
The early Drosophila embryo is emerging as a premiere model system for the computational analysis of gene regulation in development because most of the genes, and many of the associated regulatory DNAs, that control segmentation and gastrulation are known. The comprehensive elucidation of Drosophila gene networks provides an unprecedented opportunity to apply quantitative models to metazoan enhancers that govern complex patterns of gene expression during development. Models based on the fractional occupancy of defined DNA binding sites have been used to describe the regulation of the lac operon in E. coli and the lysis/lysogeny switch of phage lambda. Here, we apply similar models to enhancers regulated by the Dorsal gradient in the ventral neurogenic ectoderm (vNE) of the early Drosophila embryo. Quantitative models based on the fractional occupancy of Dorsal, Twist, and Snail binding sites raise the possibility that cooperative interactions among these regulatory proteins mediate subtle differences in the vNE expression patterns. Variations in cooperativity may be attributed to differences in the detailed linkage of Dorsal, Twist, and Snail binding sites in vNE enhancers. We propose that binding site occupancy is the key rate-limiting step for establishing localized patterns of gene expression in the early Drosophila embryo.
Animals↗
[Machine models of quantitative characters in genetics. III. Effect of linkage on variability of a 2d generation character].
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Computers↗
The OR control system of bacteriophage lambda. A physical-chemical model for gene regulation.
A quantitative model has been developed for processes in the bacteriophage lambda that control the switchover from lysogenic to lytic modes of growth. These processes include the interactions of cI repressor and cro proteins at the three DNA sites of the right operator, OR, the binding of RNA polymerase at promoters PR and PRM, the synthesis of cI repressor and cro proteins, and the degradative action of recA during induction of lysis. The model is comprised of two major physical-chemical components: a statistical thermodynamic theory for relative probabilities of the various molecular configurations of the control system; and a kinetic model for the coupling of these probabilities to functional events, including synthesis of regulatory proteins cI and cro. Using independently evaluated interaction constants and rate parameters, the model was found capable of predicting essential physiological characteristics of the system over an extended time. Sufficiency of the model to predict known physiological properties lends credence to the physical-chemical assumptions used in its construction. Several major physiological characteristics were found to arise as "system properties" through the non-linear, time-dependent, feedback-modulated combinations of molecular interactions prescribed by the model. These include: maintenance of the lysogenic state in the absence of recA-mediated cI repressor degradation; induction of lysis and the phenomenon of subinduction; and autogenous negative control of cro. We have used the model to determine the roles, within the composite system, of several key molecular processes previously characterized by studies in vitro. These include: co-operativity in cI repressor binding to DNA; interactions between repressors and RNA polymerase (positive control); and the monomer-dimer association of cI repressor molecules. A major role of cI repressor co-operativity is found to be that of guaranteeing stability of the lysogenic state against minor changes in cI repressor levels within the cell. The role of positive control seems to be that of providing for a peaked, rather than monotonic, dependence of PRM activity on cI repressor level, while permitting PR activity to be a step function. The model correlates an immense body of studies in vivo and in vitro, and it makes testable predictions about molecular phenomena as well as physiological characteristics of bacteriophage lambda. The approach developed in this study can be extended to include more features of the lambda system and to treat other systems of gene regulation.
Allosteric Regulation↗
Estimating the relationship between measured wind speed and overturning truck crashes using a binary logit model.
This paper develops a quantitative model that correlates overturning freight vehicle crash records in Wyoming to measured wind speeds at nearby weather stations. The database consists of 14,700 truck crashes from 1994 to 2003 and wind speed and gust information from 21 weather stations. A binary logit model was estimated from the data to determine if there was significant correlation between weather station wind data and the likelihood that the crash was of the overturning type. While it is reasonably known that local wind speeds at the crash location are critical in predicting overturning truck crash likelihood, it was not known if distant weather station data were an adequate predictor of these crash types. The results from this work indicate that weather station data can be used as a predictor of overturning crashes. This work provides the necessary first step for the development of operational rules for roadway sections that run high risk of overturning truck crashes in high wind conditions.
Acceleration↗
A new approach to reconstruction models of dendritic branching patterns.
Quantitative models for characterising the detailed branching patterns of dendritic trees aim to explain these patterns either in terms of growth models based on principles of dendritic development or reconstruction models that describe an existing structure by means of a canonical set of elementary properties of dendritic morphology, which when incorporated into an algorithmic procedure will generate samples of dendrites that are statistically indistinguishable in both canonical and emergent features from those of the original sample of real neurons. This article introduces a conceptually new approach to reconstruction modelling based on the single assumption that dendritic segments are built from sequences of units of constant diameter, and that the distribution of the lengths of units of similar diameter is independent of location within a dendritic tree. This assumption in combination with non-parametric methods for estimating univariate and multivariate probability densities leads to an algorithm that significantly reduces the number of basic parameters required to simulate dendritic morphology. It is not necessary to distinguish between stem and terminal segments or to specify daughter branch ratios or dendritic taper. The procedure of sampling probability densities conditioned on local morphological features eliminates the need, for example, to specify daughter branch ratios and dendritic taper since these emerge naturally as a consequence of the conditioning process. Thus several basic parameters of previous reconstruction algorithms become emergent parameters of the new reconstruction process. The new procedure was applied successfully to a sample of 51 interneurons from lamina II/III of the spinal dorsal horn.
Algorithms↗
Quantitation of model digestive mixtures by 13C NMR.
13C nuclear magnetic resonance (NMR) spectra were obtained at 50.3 and 100.5 MHz for methanolic and aqueous mixtures of sodium taurocholate, 1-monocapryloyl-rac-glycerol, and caprylic acid. Distortionless Enhancement by Polarization Transfer (DEPT) was used to improve spectral sensitivity and resolution, and to generate calibration curves for quantitative determinations of each lipid in methanol. Alternatively, the heights for nonoverlapping peaks in a 13C NMR spectrum acquired with inverse-gated decoupling provide reliable quantitative estimates for each component of the mixture, particularly when the data are obtained in methanol. These experiments also demonstrate the feasibility of detailed NMR structural investigations in model systems for glyceride digestion.
Carbon Isotopes↗
truPK -- human pharmacokinetic models for quantitative ADME prediction.
The use of in silico prediction of absorption, distribution, metabolism and excretion (ADME) properties is gaining acceptance as a useful assessment tool for early identification of likely drug candidate failures. However, it has been difficult to locate reliable models for the prediction of human pharmacokinetics (PK) in silico Currently available methods for estimating ADME and toxicity properties, such as in vitro and animal models, are not very predictive of what is observed in the clinic. Existing in silico ADME prediction tools concentrate on physicochemical properties, such as solubility, log P, rule-of-five compliance, Caco-2 permeability, blood-brain barrier and so on, or only classify drug-like candidates as 'poor', 'medium' or 'good' for a PK parameter, without ascribing values. Although physiology-based pharmacokinetic -models can predict ADME properties, they rely on using various measured properties as input for better accuracy. Strand Genomics has developed a tool, truPK, that predicts the properties of a molecule (bioavailability, protein binding, volume of distribution, elimination half-life and absorption rate) that affect its dose and dose frequency in humans. truPK's five models built using sophisticated machine methods have predicted with > 75% accuracies in external validation sets.
Animals↗
A computational model for quantitative analysis of cell cycle arrest and its contribution to overall growth inhibition by anticancer agents.
Most anticancer agents induce cell cycle arrest (cytostatic effect) and cell death (cytotoxic effect), resulting in the inhibition of population growth of cancer cells. When asynchronous cells are to be examined, the currently used flow cytometric method can not provide checkpoint-specific and quantitative information on the drug-induced cell cycle arrest. Hence, despite its significance, no good method to analyze in detail the mechanism of cell cycle arrest and its contribution to overall growth inhibition induced by an anticancer agent has yet been established. We describe in this study the development of a discrete time (Markov model)-based computational model for cell cycle progression / arrest with transition probability (TP(i)) as a model parameter. TP(i) was calculated using model equations that include easily measurable parameters such as the fraction of cells in each cell cycle phase and population doubling time. The TP(i) was then used to analyze checkpoint-specific and quantitative changes in cell cycle progression. We also used TP(i) in a Monte-Carlo simulation to predict growth inhibition caused by cell cycle arrest only. Human SCLC cells (SBC-3) exposed to UCN-01 were used to validate the model. The model-predicted growth curves agreed with the observed data for SBC-3 cells not treated or treated at a cytostatic concentration (0.2 mM) of UCN-01, indicating validity of the present model. The changes in TP(i) indicated that UCN-01 reduced the G(1)-to-S transition rate and increased the S-to-G(2) / M and G(2) / M-to-G(1) transition rates of SBC-3 cells in a concentration- and time-dependent manner. When the model-predicted growth curves were compared with the observed data for cells treated at a cytotoxic concentration (2 mM), they suggested that 22% out of 65% and 32% out of 73% of the growth inhibition could be attributed to the cell cycle arrest effect after 48 h and 72 h exposure, respectively. In conclusion, we report here the establishment of a novel method of analysis that can provide checkpoint-specific and quantitative information about cell cycle arrest induced by an anticancer agent and that can be used to assess the contribution of cell cycle arrest effect to the overall growth inhibition.
Alkaloids↗
Dual β-lactam therapy against high-risk Pseudomonas aeruginosa isolates: a dynamic in-vitro infection model study integrating population genomics with quantitative systems pharmacology modelling and simulations.
BACKGROUND: Pseudomonas aeruginosa has an extraordinary capacity for resistance emergence during treatment, even with newer antipseudomonals. There is a gap in understanding how resistance mechanisms affect the time-course of bacterial response to these newer agents. Traditional approaches for predicting pathogen response to an antibiotic do not apply to combination therapy. We aimed to develop a modelling framework to predict treatment response based on resistome information, using isolates of the worldwide-disseminated high-risk clone sequence type (ST) 235 and β-lactam antibiotics as the example. METHODS: In this hollow-fibre in-vitro infection study, we used three extensively drug-resistant ST235 clinical isolates from the national collection of the Clinical Microbiology Department of the Hospital Son Espases (Palma de Mallorca, Spain) that were hospital-acquired, were isolated following routine microbiological procedures from different patients between 2017 and 2022, were susceptible to ceftolozane-tazobactam, and had different levels of meropenem resistance. The selected isolates (ST235-05, ST235-09, and ST235-10) showed classical β-lactam resistance mechanisms pre-treatment. The isolates were investigated in 240-h dynamic hollow-fibre in-vitro infection models (HFIMs). The studies exposed the isolates to pharmacokinetic profiles of ceftolozane-tazobactam (simulating 1 g of ceftolozane and 0·5 g of tazobactam as a 3-h infusion every 8 h) and meropenem (simulating 6 g per day continuous infusion) as observed in hospitalised patients, as monotherapy and in combination. Treatment response was assessed through the quantification of the time-courses of viable total and resistant bacteria. Whole-genome sequencing identified the mechanisms of emerging resistance. A quantitative systems pharmacology (QSP) approach was used to model total and resistant bacterial counts and corresponding pharmacokinetic data from the HFIM. Monte Carlo simulations were used to predict treatment responses in 1000 virtual infected patients treated with ceftolozane-tazobactam and meropenem as monotherapies or in combination over 10 days. FINDINGS: In the HFIMs, each antibiotic alone amplified resistance by approximately 48 h for all isolates; that is, monotherapies resulted in a higher concentration of resistant bacteria compared with the control treatment at the respective time, except ceftolozane-tazobactam against ST235-10. Combination of ceftolozane-tazobactam and meropenem was synergistic (bacterial counts ≥2 log10 colony forming units [CFU] per mL lower than the best performing monotherapy and initial inoculum) against all isolates and suppressed resistance. Against ST235-10, ceftolozane-tazobactam monotherapy reduced counts to less than 1 log10 CFU per mL from 192 h onwards, whereas the combination reached less than 1 log10 CFU per mL by 24 h. Across strains, population genomics confirmed monotherapy failures were associated with emerging resistance mechanisms (ceftolozane-tazobactam: ampC Ω-loop mutations; meropenem: ftsl mutation). The developed QSP model incorporated baseline resistance mechanisms and those emerging in resistant mutant subpopulations. The model explained and predicted the monotherapy failures involving amplification of these subpopulations, and synergistic killing and resistance suppression by the combination. Simulations using the model predicted bacterial regrowth above the initial inoculum for more than 90% of patients after 0 to approximately 3 days for meropenem monotherapy across all strains and for ceftolozane-tazobactam monotherapy against ST235-05 and ST235-09. For ceftolozane-tazobactam monotherapy against ST235-10, regrowth was predicted for approximately 30% of patients. In contrast, the simulations predicted sustained bacterial killing of at least 2 log10 CFU per mL compared with the initial inoculum by the combination for more than 89% of patients across all strains. INTERPRETATION: To our knowledge, this model is the first to characterise and predict the time-course of responses of clinical isolates to antibiotics only by the resistance mechanisms present and their complex interplay, representing a step towards pathogen-specific, personalised medicine. FUNDING: Australian National Health and Medical Research Council.
Pseudomonas aeruginosa↗
QSAR and classification of murine and human soluble epoxide hydrolase inhibition by urea-like compounds.
A data set of 348 urea-like compounds that inhibit the soluble epoxide hydrolase enzyme in mice and humans is examined. Compounds having IC(50) values ranging from 0.06 to >500 microM (murine) and 0.10 to >500 microM (human) are categorized as active or inactive for classification, while quantitation is performed on smaller compound subsets ranging from 0.07 to 431 microM (murine) and 0.11 to 490 microM (human). Each compound is represented by calculated structural descriptors that encode topological, geometrical, electronic, and polar surface features. Multiple linear regression (MLR) and computational neural networks (CNNs) are employed for quantitative models. Three classification algorithms, k-nearest neighbor (kNN), linear discriminant analysis (LDA), and radial basis function neural networks (RBFNN), are used to categorize compounds as active or inactive based on selected data split points. Quantitative modeling of human enzyme inhibition results in a nonlinear, five-descriptor model with root-mean-square errors (log units of IC(50) [microM]) of 0.616 (r(2) = 0.66), 0.674 (r(2) = 0.61), and 0.914 (r(2) = 0.33) for training, cross-validation, and prediction sets, respectively. The best classification results for human and murine enzyme inhibition are found using kNN. Human classification rates using a seven-descriptor model for training and prediction sets are 89.1% and 91.4%, respectively. Murine classification rates using a five-descriptor model for training and prediction sets are 91.5% and 88.6%, respectively.
Animals↗
Statistical models for quantitative bioassay.
We discuss various statistical approaches useful in the analysis of nutritional dose-response data with a continuous response. The emphasis is on the multivariate case with several predictors. The methods which will be discussed can be classified into parametric models, including change-point models, and nonparametric models, which rely on smoothing methods such as weighted local linear fitting. The methods will be illustrated with the analysis of data generated from a folate depletion-repletion bioassay experiment conducted on rats, where the measured growth rate of the rate is the response variable. We also discuss the biological conclusions that can be drawn from applying various statistical methods to this data set.
Animal Nutritional Physiological Phenomena↗
Validation of a digital angiographic model to quantitate autoregulatory vasodilation of the coronary system.
Compensatory vasodilation of the distal coronary vascular bed is the major autoregulatory mechanism in response to coronary stenosis. Using impulse response analysis (IRA) of digital angiographic time-density curves, myocardial contrast-transit was modelled as two-compartment system to obtain total coronary transit times (T) and microcirculation transit times (Tmicro) as parameters of flow (Q) divided by distribution volume (V) of the corresponding compartments. IRA parameters were compared with electromagnetic Q in eight dogs. At rest, Vmicro/V increased (P less than 0.02) from 0.69 +/- 0.08 in normal arteries (n = 25) to 0.86 +/- 0.06 in stenotic arteries (n = 24). With maximal vasodilation during hyperaemia, Vmicro/V was similar for normal (0.89 +/- 0.05; n = 19) and stenotic arteries (0.9 +/- 0.05; n = 18). There was a close linear (r = 0.88) correlation between 1/Tmicro and Q during hyperaemia. However, at rest with intact vasomotor tone, 1/Tmicro and Q were linearly related (r = 0.94; n = 12) only when Q was reduced below normal by tight stenosis; but the relationship became curvilinear in non-Q-limiting stenosed and normal arteries due to progressive decrease in Vmicro. Instead, resting 1/Tmicro demonstrated a very close linear correlation with coronary flow reserve (r = 0.95). We conclude that two-compartmental modelling of coronary contrast transit reflects stenosis-mediated autoregulatory vasodilation of the coronary microcirculation by a single measurement at rest.
Animals↗