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PLoS One. 2015 Jul 6;10(7):e0130089. doi: 10.1371/journal.pone.0130089. eCollection 2015.

A Computational Framework for Bioimaging Simulation.

Author information

1
Laboratory for Biochemical Simulation, Quantitative Biology Center, RIKEN, Suita, Osaka, Japan.
2
Laboratory for Cell Signaling Dynamics, Quantitative Biology Center, RIKEN, Suita, Osaka, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan.
3
Laboratory for Cell Signaling Dynamics, Quantitative Biology Center, RIKEN, Suita, Osaka, Japan.
4
Laboratory for Biochemical Simulation, Quantitative Biology Center, RIKEN, Suita, Osaka, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan.
5
Laboratory for Biochemical Simulation, Quantitative Biology Center, RIKEN, Suita, Osaka, Japan; Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.

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.

PMID:
26147508
PMCID:
PMC4509736
DOI:
10.1371/journal.pone.0130089
[Indexed for MEDLINE]
Free PMC Article

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