Multiple active components quantitation

Very active research has been devoted to the development of complexity measures that would allow the quantitative characterization of a complex system. In the present context, complexity is not just described by the number of states, the multiplicity of a system, as defined in information science, or by the characteristics of the graphs representing a molecule or an assembly of molecules, or by structural complexity. Complexity implies and results from multiple components and interactions between them with integration, i.e. long range correlation, coupling and feedback. It is interaction between components that makes the whole more than the sum of the parts and leads to collective properties. Thus, the complexity of an organized system involves three basic features ... 

HQSARhologram quantitative structure-activity relationship, MLR multiple linear regression, PCA principal component analysis, PLS-QSAR partial least-squares quantitative structure-activity relationship, TAACF Tuberculosis Antimicrobial Acquisition and Coordinating Facility STATOO Consulting (Berne, Switzerland)... 

Reverse-phase protein arrays offer a robust new method of quantitatively assessing expression levels and the activation status of a panel of proteins. For this purpose, the lysate of protein(s) of interest is arrayed without selection via a capture molecule. This array can then be queried with an antibody or ligand probe, or an unknown biological component. Since an individual test sample is immobilized in each array spot, this array can be composed of a variety of different patient samples. Each array is incubated with one detection protein or antibody, and a single end point is measured across the arrayed cohort and can be directly compared across multiple samples. Replicates can be reproducibly printed at a given sitting, increasing quality control over a series of queried arrays (reviewed in [33]). 

The QShAR (Quantitative Shape-Activity Relations) method, combined with the integrated main and side effect modeling of bioactive molecules, forms the conceptual basis of the approaches described in this chapter. The density scalable FSGH method for a simple representation of molecular bodies, in combination with the Shape Group Method and various other shape code approaches for quantitative shape analysis, as well as the multiple shape ranking methods for integrated main and side effect analysis, are the components of a computational implementation of the basic concepts. 

An important feature of the biogeochemistry of trace elements in the rhizosphere is the interaction between plant root surfaces and the ions in the soil solution. These ions may accumulate in the aqueous phases of cell surfaces external to the plasma membranes (PMs). In addition, ions may bind to cell wall (CW) components or to the PM surface with variable strength. In this chapter, we shall describe the distribution of ions among the extracellular phases using electrostatic models (i.e. Gouy-Chapman-Stem and Donnan-plus-binding models) for which parameters are now available. Many plant responses to ions correlate well with computed PM-surface activities, but only poorly with activities in the soil solution. These responses include ion uptake, ion-induced intoxication, and the alleviation of intoxication by other ions. We illustrate our technique for the quantitative resolution of multiple ion effects by inserting cell-surface activities into nonlinear equations. 

Having many active synergistic constituents (a multicomponent character) interacting with multiple receptors in the human body is seen as a major advantage of TCMs and pivotal for the overall therapeutic effect [2]. However, the multicomponent character is also a major obstacle for the quantitation of components, identification of structures, explanation of the mode of action, and elucidating metabolic pathways. To date, himdreds of papers report on the simultaneous determination of multiple components in crude materia medica or finished TCMs [10,12—20]. 

Imperfections or nonidealities of the optical components in an ellipsometer can cause errors in A and il/. Quantitative analyses of errors have been made by Jerrard for inexactness of the quarter-wave plate, by Azzam and Bashara" for nonideal optical activity of the quarter-wave plate and surface roughness, and by Smith" for finite bandwidths of the monochromatic light source as well as the source polarization. Archer and Shank" and Yolken, Waxier, and Kruger considered the effect of multiple reflection within the compensator plate. It is reported that antireflection coatings on the surfaces of the compensator plate are beneficial. Aspnes" treated in a formal way the first-order effects from... 

Linear or nonlinear multiple regression analysis is used as a statistical tool to derive quantitative models, to check the significance of these models and of each individual term in the regression equation. Other statistical methods, such as discriminant analysis, principal component analysis (PCA), or partial least squares (PLS) analysis (see Partial Least Squares Projections to Latent Structures (PLS) in Chemistry) are alternatives to regression analysis (see Che mo me tries Multivariate View on Chemical Problems)Newer approaches compare the similarity of molecules with respect to different physicochemical or other properties with their biological activities. 

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