Detail of Rat_ERK_LSCM_SIM



Project
Title
BDML file for quantitative information about ERK nuclear translocation dynamics obtained by a laser-scanning confocal microscopy (LSCM) simulation module
Description
NA
Release, Updated
2016-01-18,
2018-11-15
License
CC BY
Kind
Quantitative data based on Simulation
File Formats
Data size
132.4 GB

Organism
R. norvegicus ( NCBI:txid10116 )
Strain(s)
PC-12
Cell Line
-

Datatype
single molecule dynamics
Molecular Function (MF)
Biological Process (BP)
cellular protein localization ( GO:0034613 )
Cellular Component (CC)
-
Biological Imaging Method
XYZ Scale
XY: 1 micrometer, Z: 1 micrometer
T scale
0.1 second for each time interval

Image Acquisition
Experiment type
-
Microscope type
-
Acquisition mode
-
Contrast method
-
Microscope model
-
Detector model
-
Objective model
-
Filter set
-

Summary of Methods
See details in Watabe et al. (2015) PLoS One, 10(7): e0130089
Related paper(s)

Masaki Watabe, Satya N V Arjunan, Seiya Fukushima, Kazunari Iwamoto, Jun Kozuka, Satomi Matsuoka, Yuki Shindo, Masahiro Ueda, Koichi Takahashi (2015) A Computational Framework for Bioimaging Simulation., PloS one, Volume 10, Number 7, pp. e0130089

Published in 2015 (Electronic publication in July 6, 2015, midnight )

(Abstract) Using bioimaging technology, biologists have attempted to identify and document analytical interpretations that underlie biological phenomena in biological cells. Theoretical biology aims at distilling those interpretations into knowledge in the mathematical form of biochemical reaction networks and understanding how higher level functions emerge from the combined action of biomolecules. However, there still remain formidable challenges in bridging the gap between bioimaging and mathematical modeling. Generally, measurements using fluorescence microscopy systems are influenced by systematic effects that arise from stochastic nature of biological cells, the imaging apparatus, and optical physics. Such systematic effects are always present in all bioimaging systems and hinder quantitative comparison between the cell model and bioimages. Computational tools for such a comparison are still unavailable. Thus, in this work, we present a computational framework for handling the parameters of the cell models and the optical physics governing bioimaging systems. Simulation using this framework can generate digital images of cell simulation results after accounting for the systematic effects. We then demonstrate that such a framework enables comparison at the level of photon-counting units.
(MeSH Terms)

Contact
Koichi Takahashi , RIKEN , Quantitative Biology Center , Laboratory for Biochemical Simulation
Contributors
Masaki Watabe, Satya N. V. Arjunan, Seiya Fukushima, Kazunari Iwamoto, Jun Kozuka, Satomi Matsuoka, Yuki Shindo, Masahiro Ueda, Koichi Takahashi

Local ID
Rat_ERK_LSCM_SIM
BDML ID
fcb710b2-e94b-4c7c-a9c9-876ef1fb3c4e
BDML/BD5