A Computational Framework for Bioimaging Simulation.
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4509736?pdf=render |
| _version_ | 1818331218201018368 |
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| author | Masaki Watabe Satya N V Arjunan Seiya Fukushima Kazunari Iwamoto Jun Kozuka Satomi Matsuoka Yuki Shindo Masahiro Ueda Koichi Takahashi |
| author_facet | Masaki Watabe Satya N V Arjunan Seiya Fukushima Kazunari Iwamoto Jun Kozuka Satomi Matsuoka Yuki Shindo Masahiro Ueda Koichi Takahashi |
| author_sort | Masaki Watabe |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T13:16:21Z |
| format | Article |
| id | doaj.art-5eec631ed5974f89a9afb9438230147d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T13:16:21Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5eec631ed5974f89a9afb9438230147d2022-12-21T23:44:31ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01107e013008910.1371/journal.pone.0130089A Computational Framework for Bioimaging Simulation.Masaki WatabeSatya N V ArjunanSeiya FukushimaKazunari IwamotoJun KozukaSatomi MatsuokaYuki ShindoMasahiro UedaKoichi TakahashiUsing 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.http://europepmc.org/articles/PMC4509736?pdf=render |
| spellingShingle | Masaki Watabe Satya N V Arjunan Seiya Fukushima Kazunari Iwamoto Jun Kozuka Satomi Matsuoka Yuki Shindo Masahiro Ueda Koichi Takahashi A Computational Framework for Bioimaging Simulation. PLoS ONE |
| title | A Computational Framework for Bioimaging Simulation. |
| title_full | A Computational Framework for Bioimaging Simulation. |
| title_fullStr | A Computational Framework for Bioimaging Simulation. |
| title_full_unstemmed | A Computational Framework for Bioimaging Simulation. |
| title_short | A Computational Framework for Bioimaging Simulation. |
| title_sort | computational framework for bioimaging simulation |
| url | http://europepmc.org/articles/PMC4509736?pdf=render |
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