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From microscopy data to in silico environments for in vivo-oriented simulations

In our previous study, we introduced a combination methodology of Fluorescence Correlation Spectroscopy (FCS) and Transmission Electron Microscopy (TEM), which is powerful to investigate the effect of intracellular environment to biochemical reaction processes. Now, we developed a reconstruction met...

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Detalles Bibliográficos
Autores principales: Hiroi1, Noriko, Klann, Michael, Iba, Keisuke, Heras Ciechomski, Pablo de, Yamashita, Shuji, Tabira, Akito, Okuhara, Takahiro, Kubojima, Takeshi, Okada, Yasunori, Oka, Kotaro, Mange, Robin, Unger, Michael, Funahashi, Akira, Koeppl, Heinz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3698665/
https://www.ncbi.nlm.nih.gov/pubmed/22734658
http://dx.doi.org/10.1186/1687-4153-2012-7
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author Hiroi1, Noriko
Klann, Michael
Iba, Keisuke
Heras Ciechomski, Pablo de
Yamashita, Shuji
Tabira, Akito
Okuhara, Takahiro
Kubojima, Takeshi
Okada, Yasunori
Oka, Kotaro
Mange, Robin
Unger, Michael
Funahashi, Akira
Koeppl, Heinz
author_facet Hiroi1, Noriko
Klann, Michael
Iba, Keisuke
Heras Ciechomski, Pablo de
Yamashita, Shuji
Tabira, Akito
Okuhara, Takahiro
Kubojima, Takeshi
Okada, Yasunori
Oka, Kotaro
Mange, Robin
Unger, Michael
Funahashi, Akira
Koeppl, Heinz
author_sort Hiroi1, Noriko
collection PubMed
description In our previous study, we introduced a combination methodology of Fluorescence Correlation Spectroscopy (FCS) and Transmission Electron Microscopy (TEM), which is powerful to investigate the effect of intracellular environment to biochemical reaction processes. Now, we developed a reconstruction method of realistic simulation spaces based on our TEM images. Interactive raytracing visualization of this space allows the perception of the overall 3D structure, which is not directly accessible from 2D TEM images. Simulation results show that the diffusion in such generated structures strongly depends on image post-processing. Frayed structures corresponding to noisy images hinder the diffusion much stronger than smooth surfaces from denoised images. This means that the correct identification of noise or structure is significant to reconstruct appropriate reaction environment in silico in order to estimate realistic behaviors of reactants in vivo. Static structures lead to anomalous diffusion due to the partial confinement. In contrast, mobile crowding agents do not lead to anomalous diffusion at moderate crowding levels. By varying the mobility of these non-reactive obstacles (NRO), we estimated the relationship between NRO diffusion coefficient (D(nro)) and the anomaly in the tracer diffusion (α). For D(nro)=21.96 to 44.49 μm(2)/s, the simulation results match the anomaly obtained from FCS measurements. This range of the diffusion coefficient from simulations is compatible with the range of the diffusion coefficient of structural proteins in the cytoplasm. In addition, we investigated the relationship between the radius of NRO and anomalous diffusion coefficient of tracers by the comparison between different simulations. The radius of NRO has to be 58 nm when the polymer moves with the same diffusion speed as a reactant, which is close to the radius of functional protein complexes in a cell.
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spelling pubmed-36986652013-08-17 From microscopy data to in silico environments for in vivo-oriented simulations Hiroi1, Noriko Klann, Michael Iba, Keisuke Heras Ciechomski, Pablo de Yamashita, Shuji Tabira, Akito Okuhara, Takahiro Kubojima, Takeshi Okada, Yasunori Oka, Kotaro Mange, Robin Unger, Michael Funahashi, Akira Koeppl, Heinz EURASIP J Bioinform Syst Biol Research In our previous study, we introduced a combination methodology of Fluorescence Correlation Spectroscopy (FCS) and Transmission Electron Microscopy (TEM), which is powerful to investigate the effect of intracellular environment to biochemical reaction processes. Now, we developed a reconstruction method of realistic simulation spaces based on our TEM images. Interactive raytracing visualization of this space allows the perception of the overall 3D structure, which is not directly accessible from 2D TEM images. Simulation results show that the diffusion in such generated structures strongly depends on image post-processing. Frayed structures corresponding to noisy images hinder the diffusion much stronger than smooth surfaces from denoised images. This means that the correct identification of noise or structure is significant to reconstruct appropriate reaction environment in silico in order to estimate realistic behaviors of reactants in vivo. Static structures lead to anomalous diffusion due to the partial confinement. In contrast, mobile crowding agents do not lead to anomalous diffusion at moderate crowding levels. By varying the mobility of these non-reactive obstacles (NRO), we estimated the relationship between NRO diffusion coefficient (D(nro)) and the anomaly in the tracer diffusion (α). For D(nro)=21.96 to 44.49 μm(2)/s, the simulation results match the anomaly obtained from FCS measurements. This range of the diffusion coefficient from simulations is compatible with the range of the diffusion coefficient of structural proteins in the cytoplasm. In addition, we investigated the relationship between the radius of NRO and anomalous diffusion coefficient of tracers by the comparison between different simulations. The radius of NRO has to be 58 nm when the polymer moves with the same diffusion speed as a reactant, which is close to the radius of functional protein complexes in a cell. BioMed Central 2012 2012-06-26 /pmc/articles/PMC3698665/ /pubmed/22734658 http://dx.doi.org/10.1186/1687-4153-2012-7 Text en Copyright © 2012 Hiroi et al.; licensee Springer. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Hiroi1, Noriko
Klann, Michael
Iba, Keisuke
Heras Ciechomski, Pablo de
Yamashita, Shuji
Tabira, Akito
Okuhara, Takahiro
Kubojima, Takeshi
Okada, Yasunori
Oka, Kotaro
Mange, Robin
Unger, Michael
Funahashi, Akira
Koeppl, Heinz
From microscopy data to in silico environments for in vivo-oriented simulations
title From microscopy data to in silico environments for in vivo-oriented simulations
title_full From microscopy data to in silico environments for in vivo-oriented simulations
title_fullStr From microscopy data to in silico environments for in vivo-oriented simulations
title_full_unstemmed From microscopy data to in silico environments for in vivo-oriented simulations
title_short From microscopy data to in silico environments for in vivo-oriented simulations
title_sort from microscopy data to in silico environments for in vivo-oriented simulations
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3698665/
https://www.ncbi.nlm.nih.gov/pubmed/22734658
http://dx.doi.org/10.1186/1687-4153-2012-7
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