Complex Analysis of Fluorescence Intensity Fluctuations of Molecular Compounds

Yatskou, Mikalai M.; Skakun, Victor V.; Nederveen-Schippers, L.; Kortholt, Arjan; Apanasovich, V. V.

doi:10.1007/s10812-020-01055-6

Cited by 4 publications

(4 citation statements)

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“…A comprehensive approach based on the application of data mining procedures was used to assess the heterogeneity of the sample [12]. A key element of this approach is the use of principal component analysis (PCA) [13] to test the hypothesis about the separability of a set of multidimensional experimental data into several subsets corresponding to diff erent types of molecules in the space of the fi rst principal components (PC).…”

Section: Introductionmentioning

confidence: 99%

“…It is assumed that the PC fl uctuations in the fl uorescence 931 intensity of the molecular systems are due to the eff ect of two independent factors -the brightness and the concentration of molecular compounds [4]. For a dataset of a particular molecular system in a state of thermodynamic equilibrium a spherical or ellipsoidal cluster of the HNP is expected in the space of the fi rst PCs [12]. However, for a number of studied systems, for example, GFP and diGFP proteins in the cytoplasm of "depleted" dictyostelium cells, an arcuate cloud is observed (Fig.…”

Section: Introductionmentioning

confidence: 99%

“…1a. Simulation of the system without additional scattering of the concentration and brightness of molecules shows clearly separated points of clouds in the space of the fi rst two PCs [12] (Fig. 2a).…”

mentioning

confidence: 99%

“…In view of the fact that spread of the parameters is also a characteristic of the system the semicircular shape of the data cloud suggests the presence of additional important information -high variability in the parameters. According to [12], in fl uorescence fl uctuation spectroscopy a spherical cloud is expected for monomers, dimers, and even aggregates of a certain protein with relatively stable characteristics, while an ellipsoidal cloud is expected for a mixture of molecules of several types. In our investigation, however, elongation of the cloud may be the result of an increase in the variance of the measured parameter, indicating the formation of a certain type of aggregate or the presence of unavoidable experimental eff ects (photobleaching, uncontrolled ligandizing of monomers by nonremovable impurities, etc.…”

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confidence: 99%

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Component Analysis of Photon Counting Histograms in Fluorescence Fluctuation Spectroscopy Experiments

Skakun

Yatskou²,

Nederveen-Schippers³

et al. 2022

J Appl Spectrosc

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An integrated approach based on the use of data mining methods is proposed in order to improve the effi ciency of the analysis of photon counting histograms during study of the molecular composition of a substance by fl uorescence fl uctuation spectroscopy. The principal component method is used to test the hypothesis about cluster separability of the multidimensional experimental data. The reason for compression of the data cloud to characteristic nonlinearity, or so-called arcuate (arc-shaped) cloud, in the space of the fi rst two principal components is investigated. Examples of simulated data sets of molecular systems with various brightness and concentration characteristics are considered. Interpretation and subsequent quantitative analysis of the data are complicated by nonlinear eff ects. It was established that the arcuate nature of the data cloud is a consequence of the presence of signifi cant variation in one or more physical parameters and results and, in particular, from signifi cant increase in the spread of the brightness or concentration parameters of the molecules. If only one type of molecule is being studied these parameters can provide additional indicators for assessing the quality of the experiments and can also be used for characterizing the system in the case of a mixture of various types of molecules. It was proposed to use normalization of local weighted smoothing of the scattering diagram to eliminate nonlinear eff ects in the space of the principal components.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Component Analysis of Photon Counting Histograms in Fluorescence Fluctuation Spectroscopy Experiments

Skakun

Yatskou²,

Nederveen-Schippers³

et al. 2022

J Appl Spectrosc

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Simulation Modelling and Machine Learning Platform for Processing Fluorescence Spectroscopy Data

Yatskou

Apanasovich

2022

Communications in Computer and Information Science

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Data analysis in complex biomolecular systems

Yatskou

Apanasovich

2021

Informatika (Minsk)

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The biomolecular technology progress is directly related to the development of effective methods and algorithms for processing a large amount of information obtained by modern high-throughput experimental equipment. The priority task is the development of promising computational tools for the analysis and interpretation of biophysical information using the methods of big data and computer models. An integrated approach to processing large datasets, which is based on the methods of data analysis and simulation modelling, is proposed. This approach allows to determine the parameters of biophysical and optical processes occurring in complex biomolecular systems. The idea of an integrated approach is to use simulation modelling of biophysical processes occurring in the object of study, comparing simulated and most relevant experimental data selected by dimension reduction methods, determining the characteristics of the investigated processes using data analysis algorithms. The application of the developed approach to the study of bimolecular systems in fluorescence spectroscopy experiments is considered. The effectiveness of the algorithms of the approach was verified by analyzing of simulated and experimental data representing the systems of molecules and proteins. The use of complex analysis increases the efficiency of the study of biophysical systems during the analysis of big data.

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Complex Analysis of Fluorescence Intensity Fluctuations of Molecular Compounds

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Cited by 4 publications

References 16 publications

Component Analysis of Photon Counting Histograms in Fluorescence Fluctuation Spectroscopy Experiments

Component Analysis of Photon Counting Histograms in Fluorescence Fluctuation Spectroscopy Experiments

Simulation Modelling and Machine Learning Platform for Processing Fluorescence Spectroscopy Data

Data analysis in complex biomolecular systems

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