Biochemical and immunological characterization of ceruloplasmin genetic variants. A proposed model for quantitative control.
Explore the source record for details and available documents.
Antibody Specificity↗
SEARCH · Search PubMed
Results for “quantitative model”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Multiple biomarkers of colorectal tumor in a differential diagnosis model: a quantitative study.
AIM: To evaluate the multiple biomarkers of colorectal tumor and their potential usage in early diagnosis of colorectal cancers. METHODS: Multiple biomarkers (DNA contents, AgNOR, PCNA, p53, c-erbB-2) in 10 normal colorectal mucosae, 37 colorectal adenomas and 55 colorectal cancers were analyzed quantitatively in the computed processing imaging system. Discrimination patterns were employed to evaluate the significance of single and multiple indices in diagnosis of colorectal cancers. RESULTS: The mean values of the analyzed parameters increased in order of the normal mucosa, adenoma and adenocarcinoma, and this tendency reflected the progression of colorectal malignancy. The parameters including DNA index, positive rates, densities of AgNOR, c-erbB-2, and p53, shape and density of nucleus were relatively valuable for diagnoses. Then a diagnostic discrimination model was established. The samples were confirmed with the model, the sensitivity rates in cancer group and adenoma group were 96.36% and 89.19%, respectively. The value of proliferating cell nuclear antigen (PCNA) in early diagnosis of colorectal cancers was uncertain. CONCLUSION: The quantitative evaluation of some parameters for colorectal tumor can provide reproducible data for differential diagnosis. The established diagnostic discrimination model may be of clinicopathological value, and can make the early diagnosis of colorectal cancer possible.
Adenoma↗
Computer models of quantitative characteristics in genetics. Communication II. Dynamics of the frequency of alleles with different types of selection.
Explore the source record for details and available documents.
Alleles↗
Cyclic, nonequilibrium models of glucocorticoid antagonism: role of activation, nuclear binding and receptor recycling.
Quantitative models that have been proposed to date to explain mechanisms of glucocorticoid antagonism have generally been of the equilibrium type, involving hypothetical allosteric equilibria between active and inactive states of the receptor or the steroid-receptor complex. We describe here the agonist-antagonist relationships predicted by a nonequilibrium cyclic model that we have recently devised to account for the kinetic behavior of glucocorticoid-receptor complexes in intact rat thymus cells. This model simulates quantitatively most kinetic and steady state results that have been obtained so far. It postulates the existence of only well-established receptor species, and its kinetic parameters can in principle be determined by receptor measurements with intact cells. To calculate the steady state agonist-antagonist properties it is assumed that biological activity is proportional to the total amount of nuclear-bound complex, whether formed by agonist or antagonist. The agonist activity of a steroid is determined by the steady state ratio of nuclear-bound to total complexes it forms. This ratio varies from 0 for a pure antagonist to 1 for a pure agonist. It turns out to be independent of agonist and antagonist concentrations, and a function only of the rate constants for the reactions of the complexes formed by a steroid. Analysis of the dependence of the ratio on each rate constant shows quantitatively how each reaction in the cyclic model--activation of the nonactivated complex, nuclear binding of the activated complexes, and dissociation and recycling of activated and nuclear-bound complexes--affects antagonist properties. Steady state interactions of agonists with antagonists are found to be determined by equations that are identical to those for competition in simple equilibrium systems. Predicted dose-response relations agree qualitatively with experimentally observed relations. They are similar to those predicted by two-state allosteric models, although the cyclic model has no allosteric mechanisms and is based on quite different assumptions. Present limitations of the model arise particularly from lack of information about the mechanisms by which nuclear-bound complexes generate biological activity; for lack of such information the model includes no steps to account for substances that have low agonist activity despite forming nuclear-bound complexes.
Allosteric Regulation↗
A model for quantitating in vivo platelet aggregation.
Explore the source record for details and available documents.
Adenosine Diphosphate↗
Oculomotor instabilities in zebrafish mutant belladonna: a behavioral model for congenital nystagmus caused by axonal misrouting.
A large fraction of homozygous zebrafish mutant belladonna (bel) larvae display a reversed optokinetic response (OKR) that correlates with failure of the retinal ganglion cells to cross the midline and form the optic chiasm. Some of these achiasmatic mutants display strong spontaneous eye oscillations (SOs) in the absence of motion in the surround. The presentation of a stationary grating was necessary and sufficient to evoke SO. Both OKR reversal and SO depend on vision and are contrast sensitive. We built a quantitative model derived from bel fwd (forward) eye behaviors. To mimic the achiasmatic condition, we reversed the sign of the retinal slip velocity in the model, thereby successfully reproducing both reversed OKR and SO. On the basis of the OKR data, and with the support of the quantitative model, we hypothesize that the reversed OKR and the SO can be completely attributed to RGC misrouting. The strong resemblance between the SO and congenital nystagmus (CN) seen in humans with defective retinotectal projections implies that CN, of so far unknown etiology, may be directly caused by a projection defect.
Animals↗
Molecular modeling of quantitative structure retention relationship studies: retention behavior of polychlorinated dibenzofurans on gas chromatographic stationary phases of varying polarity by a novel molecular distance edge vector.
Chemical structures of polychlorinated dibenzodioxin (PCDD) congeners are described by a novel molecular distance edge vector (VMDE), developed in our laboratory, that consists of the modified molecular distance edge parameters based on the identical group as a pseudo-atom instead of a traditional atom. Quantitative structure retention relationships (QSRRs) between the new VMDE parameters and the gas chromatographic retention behavior of PCDDs are then generated by a multiple linear regression method for nonpolar, moderately polar, and polar stationary phases. All QSRR models with a high correlation (R > 0.99) are developed for nonpolar, moderately polar, and polar columns (DB-5, SP-2100, SE-54, and OV-1701). Cross validation with the leave-one-out procedure is performed, and satisfactory results are obtained with high correlation. The obtained results show that the new VMDE vector is adapted to characterize the chemical structure and model the retention behavior of PCDDs on various polar stationary phases.
Journal Article↗
Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis.
BACKGROUND: Carbon (C) and nitrogen (N) metabolites can regulate gene expression in Arabidopsis thaliana. Here, we use multi-network analysis of microarray data to identify molecular networks regulated by C and N in the Arabidopsis root system. RESULTS: We used the Arabidopsis whole genome Affymetrix gene chip to explore global gene expression responses in plants exposed transiently to a matrix of C and N treatments. We used ANOVA analysis to define quantitative models of regulation for all detected genes. Our results suggest that about half of the Arabidopsis transcriptome is regulated by C, N or CN interactions. We found ample evidence for interactions between C and N that include genes involved in metabolic pathways, protein degradation and auxin signaling. To provide a global, yet detailed, view of how the cell molecular network is adjusted in response to the CN treatments, we constructed a qualitative multi-network model of the Arabidopsis metabolic and regulatory molecular network, including 6,176 genes, 1,459 metabolites and 230,900 interactions among them. We integrated the quantitative models of CN gene regulation with the wiring diagram in the multi-network, and identified specific interacting genes in biological modules that respond to C, N or CN treatments. CONCLUSION: Our results indicate that CN regulation occurs at multiple levels, including potential post-transcriptional control by microRNAs. The network analysis of our systematic dataset of CN treatments indicates that CN sensing is a mechanism that coordinates the global and coordinated regulation of specific sets of molecular machines in the plant cell.
Arabidopsis↗
Validation of an algorithm for delay stochastic simulation of transcription and translation in prokaryotic gene expression.
The quantitative modeling of gene transcription and translation requires a treatment of two key features: stochastic fluctuations due to the limited copy numbers of key molecules (genes, RNA polymerases, ribosomes), and delayed output due to the time required for biopolymer synthesis. Recently proposed algorithms allow for efficient simulations of such systems. However, it is critical to know whether the results of delay stochastic simulations agree with those from more detailed models of the transcription and translation processes. We present a generalization of previous delay stochastic simulation algorithms which allows both for multiple delays and for distributions of delay times. We show that delay stochastic simulations closely approximate simulations of a detailed transcription model except when two-body effects (e.g. collisions between polymerases on a template strand) are important. Finally, we study a delay stochastic model of prokaryotic transcription and translation which reproduces observations from a recent experimental study in which a single gene was expressed under the control of a repressed lac promoter in E. coli cells. This demonstrates our ability to quantitatively model gene expression using these new methods.
Algorithms↗
A quantitative physiologic model of blood oxygenation for functional magnetic resonance imaging.
RATIONALE AND OBJECTIVES: Variations in venous deoxyhemoglobin levels in response to neuronal activation represent a complex interplay between focal changes in cerebral blood flow (CBF), cerebral blood volume (CBV), and regional metabolism. The authors present a mathematic model that characterizes the response of venous oxygenation to changes in these variables. METHODS: Using a mass balance approach, the equations for a simple input-output model are derived and solved using Matlab. Changes in blood oxygenation are related to available results from functional magnetic resonance imaging experiments. RESULTS: Increases in CBF produce declines in oxygen extraction fraction and venous deoxyhemoglobin according to Fick's law, and are quantitatively in agreement with available magnetic resonance and positron-emission tomography data. A flow-volume envelope defines the changes in CBF relative to CBV. CONCLUSIONS: It is possible to obtain a quantitative understanding of changes in blood oxygenation and to relate these changes to the observed dynamics of magnetic resonance signal change in the setting of functional stimulation.
Algorithms↗
Two-step multivariate adaptive regression splines for modeling a quantitative relationship between gas chromatography retention indices and molecular descriptors.
The relationship between retention indices and molecular descriptors of alkanes is established by two-step multivariate adaptive regression splines (TMARS). TMARS combines linear regression with multivariate adaptive regression splines (MARS). It is demonstrated for the present data set that using linear regression or MARS modeling alone causes lack of fit. TMARS avoids lack of fit and appreciably improves the prediction ability for the model. The use of this combined approach permits the development of additional understanding of the adaptive nature in MARS modeling.
Chromatography, Gas↗
Influences on decision-making for undergoing plastic surgery: a mental models and quantitative assessment.
BACKGROUND: Research was conducted to gain insight into potential clients' decisions to undergo plastic surgery, their perception of benefits and risks, their judgment of outcomes, and their selection of a plastic surgeon. METHODS: Semistructured, open-ended interviews were conducted with 60 people who expressed interest in plastic surgery. Qualitative analysis revealed their "mental models" regarding influences on their decision to undergo plastic surgery and their choice of a surgeon. Interview results were used to design a Web-based survey in which 644 individuals considering plastic surgery responded. RESULTS: The desire for change was the most direct motivator to undergo plastic surgery. Improvements to physical well-being were related to emotional and social benefits. When prompted about risks, participants mentioned physical, emotional, and social risks. Surgeon selection was a critical influence on decisions to undergo plastic surgery. Participants gave considerable weight to personal consultation and believed that finding the "right" plastic surgeon would minimize potential risks. Findings from the Web-based survey were similar to the mental models interviews in terms of benefit ratings but differed in risk ratings and surgeon selection criteria. CONCLUSIONS: The mental models interviews revealed that interview participants were thoughtful about their decision to undergo plastic surgery and focused on finding the right plastic surgeon.
Adolescent↗
Near-infrared spectroscopic monitoring of biomass, glucose, ethanol and protein content in a high cell density baker's yeast fed-batch bioprocess.
The use of at-line NIRS to monitor a high cell density fed-batch baker's yeast bioprocess was investigated. Quantification of the key analytes (biomass, ethanol and glucose) and the product quality indicator (percentage protein content) was studied. Biomass was quantitatively modelled using whole matrix samples (as was percentage protein content). The dominance of the whole matrix spectrum by biomass, and its associated light scattering effects, were overcome by use of filtrate samples and adapted (semi-synthetic) filtrate samples, which allowed successful ethanol and glucose modelling, respectively. Calibrations were rigorously challenged via external validation with large sample sets relative to the calibration sample size, ensuring model robustness and potential practical utility. The standard errors of calibration for biomass, glucose, ethanol and total intracellular protein were (g/l) 1.79, 0.19, 0.79 and 0.91, respectively, comparable to those of the primary assays. The calibration strategies necessary to generate quantitative models for this range of analytes in such a complex high cell density bioprocess fluid are discussed.
Biomass↗
Mixture models for quantitative HIV RNA data.
Clinical investigators are increasing their use of quantitative determinations of HIV viral load in their study populations. The distributions of these measures may be highly skewed, left-censored, and with an extra spike below the detection limit of the assay. We recommended use of a mixture model in this situation, with two sets of explanatory covariates. We extend this model to incorporate multiple measures across time, and to employ shared parameters as a way of increasing model efficiency and parsimony. Data from a cohort of HIV-infected men are used to illustrate these features, and simulations are performed to assess the utility of shared parameters.
Brazil↗
Quantitative genetic models of sexual conflict based on interacting phenotypes.
Evolutionary conflict arises between reproductive partners when alternative reproductive opportunities are available. Sexual conflict can generate sexually antagonistic selection, which mediates sexual selection and intersexual coevolution. However, despite intense interest, the evolutionary implications of sexual conflict remain unresolved. We propose a novel theoretical approach to study the evolution of sexually antagonistic phenotypes based on quantitative genetics and the measure of social selection arising from male-female interactions. We consider the phenotype of one sex as both a genetically influenced evolving trait as well as the (evolving) social environment in which the phenotype of the opposite sex evolves. Several important points emerge from our analysis, including the relationship between direct selection on one sex and indirect effects through selection on the opposite sex. We suggest that the proposed approach may be a valuable tool to complement other theoretical approaches currently used to study sexual conflict. Most importantly, our approach highlights areas where additional empirical data can help clarify the role of sexual conflict in the evolutionary process.
Animals↗
A quantitative molecular model for modulation of mammalian translation by the eIF4E-binding protein 1.
Translation initiation is a key point of regulation in eukaryotic gene expression. 4E-binding proteins (4E-BPs) inhibit initiation by blocking the association of eIF4E with eIF4G, two integral components of the mRNA cap-binding complex. Phosphorylation of 4E-BP1 reduces its ability to bind to eIF4E and thereby to compete with eIF4G. A novel combination of biophysical and biochemical tools was used to measure the impact of phosphorylation and acidic side chain substitution at each potentially modulatory site in 4E-BP1. For each individual site, we have analyzed the effects of modification on eIF4E binding using affinity chromatography and surface plasmon resonance analysis, and on the regulatory function of the 4E-BP1 protein using a yeast in vivo model system and a mammalian in vitro translation assay. We find that modifications at the two sites immediately flanking the eIF4E-binding domain, Thr(46) and Ser(65), consistently have the most significant effects, and that phosphorylation of Ser(65) causes the greatest reduction in binding affinity. These results establish a quantitative framework that should contribute to understanding of the molecular interactions underlying 4E-BP1-mediated translational regulation.
Adaptor Proteins, Signal Transducing↗
Degradation model: a quantitative EIA instrument, acting as a decision support system (DSS) for environmental management.
Environmental assessment of alternative development plans, programs, and policies may bring conflict among decision-makers, particularly when some quantitative measures for decision-making are needed and where cumulative impacts are neglected. Environmental impact assessment (EIA) and environmental economics theories, despite their usefulness, are not capable of addressing those issues and problems alone. In recent years, the decision support system (DSS) has provided some solutions, but mathematical analysis of the system to show the internal structure of the problem is not always possible. To addres the above shortcomings and ongoing problems of decision-making in Iran, a degradation model (DM) was introduced as an instrument of EIA, to act as a DSS for managers. The model is a compromise between knowledge-based decision support systems, detailed models, digested information models, and the basic theorem of environmental economics. In the present study (1996-2000), the model was applied in three provinces of Iran, representing three of four biogeographical regions of Iran. The study area was divided into a set of grids (100 km(2)). The degradation coefficient ( H) was computed for all grids (1333), representing the degree of degradation in the grid. It is obvious that the higher the coefficient the more area is degraded and less prone to further development, and vice versa. In order to provide decision-makers with a set of quantitative measures to observe impacted areas (critical and noncritical) for resource allocation and further development, the degradation coefficients of all grids were classified into categories and criteria, using a fuzzy set theoretic approach. Accordingly, only 24% of study areas are prone to further development. The degradation model as a knowledge-based decision support system has its strengths and weaknesses, but it has solved managers' ongoing problems in Iran and it could be used elsewhere.
Conservation of Natural Resources↗
An immunoprecipitation-dissociation technique for large scale antibody purification and an antigen consumption electroimmunoassay for antibody quantitation. A model study with antibodies to pregnancy zone protein.
A simple immunoprecipitation--dissociation technique for large scale purification of antibodies is described, which comprises selective denaturation of the antigen and recovery of the antibody fraction by exclusion chromatography at low pH. Its use is illustrated by the purification of antibodies to pregnancy zone protein. A purification factor of about 60 was achieved. An antigen consumption electroimmunoassay was also developed which permits quantitative determination of the antigen binding activity of antibodies with a given specificity. The methods have general application.
Antibodies↗