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Open Access Research

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

Noriko Hiroi11*, Michael Klann2, Keisuke Iba1, Pablo de Heras Ciechomski3, Shuji Yamashita4, Akito Tabira1, Takahiro Okuhara1, Takeshi Kubojima1, Yasunori Okada4, Kotaro Oka1, Robin Mange2, Michael Unger2, Akira Funahashi1 and Heinz Koeppl2*

Author Affiliations

1 Department of BioSciences and Informatics, Keio University, Yokohama, Kanagawa, Japan

2 Automatic Control Laboratory, Swiss Federal Institute of Technology, Zurich, Switzerland

3 , ScienceVisuals Sarl, Lausanne, Switzerland

4 Department of Pathology,School of Medicine, Keio University, Tokyo, Shinjuku-ku, Japan

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EURASIP Journal on Bioinformatics and Systems Biology 2012, 2012:7  doi:10.1186/1687-4153-2012-7

Published: 26 June 2012

Abstract

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 (Dnro) and the anomaly in the tracer diffusion (α). For Dnro=21.96 to 44.49 μm2/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.