Publikationsserver
Machine Learning Classification of Trajectories from Molecular Dynamics Simulations of Chromosome Segregation
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...
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
Philipps-Universität Marburg
2022
|
| Materias: | |
| Acceso en línea: | Texto Completo PDF |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | 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. |
|---|---|
| Notas: | Gefördert durch den Open-Access-Publikationsfonds der UB Marburg. |
| Descripción Física: | 33 Seiten |
| DOI: | 10.1371/journal.pone.0262177 |