Characterization of bidirectional molecular motor-assisted transport models

Aßmann, M. Muhler J.; Lenz, Peter

doi:10.1088/1478-3975/10/1/016003

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…Ideally, comparison with experimental data should also identify if the model is relevant for the particular trajectory. For this purpose, Assmann et al 111 addressed how trajectories with bidirectional character may be analyzed. Here, a series of statistical tools useful for analyzing and characterizing cargo trajectories were introduced, and it was demonstrated how the statistical properties of observed cargo trajectories could provide information on the motor proteins involved.…”

Section: Active Motion In Biological Processesmentioning

confidence: 99%

Manipulation and Motion of Organelles and Single Molecules in Living Cells

et al. 2017

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The biomolecule is among the most important building blocks of biological systems, and a full understanding of its function forms the scaffold for describing the mechanisms of higher order structures as organelles and cells. Force is a fundamental regulatory mechanism of biomolecular interactions driving many cellular processes. The forces on a molecular scale are exactly in the range that can be manipulated and probed with single molecule force spectroscopy. The natural environment of a biomolecule is inside a living cell, hence, this is the most relevant environment for probing their function. In vivo studies are, however, challenged by the complexity of the cell. In this review, we start with presenting relevant theoretical tools for analyzing single molecule data obtained in intracellular environments followed by a description of state-of-the art visualization techniques. The most commonly used force spectroscopy techniques, namely optical tweezers, magnetic tweezers, and atomic force microscopy, are described in detail, and their strength and limitations related to in vivo experiments are discussed. Finally, recent exciting discoveries within the field of in vivo manipulation and dynamics of single molecule and organelles are reviewed.

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Section: Active Motion In Biological Processesmentioning

confidence: 99%

Manipulation and Motion of Organelles and Single Molecules in Living Cells

et al. 2017

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“…Suggestions for statistical features of the trajectories can be found in the literature, e.g. for quantitative comparison of trajectories from single particle tracking (SPT) experiments [20,[22][23][24][25]. For our study, we selected eight features, which are described in the following.…”

Section: Plos Onementioning

confidence: 99%

“…As input for training one can either use complete trajectories (as high-dimensional input vectors) or extracted statistical features of the trajectories (as low-dimensional input vectors). In the latter case training is less time consuming and less demanding on computational resources [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation

Geisel

Lenz²

2022

PLoS ONE

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In contrast to the well characterized mitotic machinery in eukaryotes it seems as if there is no universal mechanism organizing chromosome segregation in all bacteria. Apparently, some bacteria even use combinations of different segregation mechanisms such as protein machines or rely on physical forces. The identification of the relevant mechanisms is a difficult task. Here, we introduce a new machine learning approach to this problem. It is based on the analysis of trajectories of individual loci in the course of chromosomal segregation obtained by fluorescence microscopy. While machine learning approaches have already been applied successfully to trajectory classification in other areas, so far it has not been possible to use them to discriminate segregation mechanisms in bacteria. A main obstacle for this is the large number of trajectories required to train machine learning algorithms that we overcome here by using trajectories obtained from molecular dynamics simulations. We used these trajectories to train four different machine learning algorithms, two linear models and two tree-based classifiers, to discriminate segregation mechanisms and possible combinations of them. The classification was performed once using the complete trajectories as high-dimensional input vectors as well as on a set of features which were used to transform the trajectories into low-dimensional input vectors for the classifiers. Finally, we tested our classifiers on shorter trajectories with duration times comparable (or even shorter) than typical experimental trajectories and on trajectories measured with varying temporal resolutions. Our results demonstrate that machine learning algorithms are indeed capable of discriminating different segregation mechanisms in bacteria and to even resolve combinations of the mechanisms on rather short time scales.

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“…The activity should be regulated by cofactors (Gross et al 2002;Gross 2004;Welte 2004;Schuster et al 2011). Evidences for both models were published recently (Bouzat and Falo 2011;Muller et al 2008;Assmann and Lenz 2013;Leidel et al 2012).…”

Section: Part IIImentioning

confidence: 99%

Cell Polarity 1

Ebnet¹

2015

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Characterization of bidirectional molecular motor-assisted transport models

Cited by 5 publications

References 30 publications

Manipulation and Motion of Organelles and Single Molecules in Living Cells

Manipulation and Motion of Organelles and Single Molecules in Living Cells

Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation

Cell Polarity 1

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