Computational Modeling of Fluorescence Loss in Photobleaching

Christian Valdemar Hansen, Achim Schroll, Daniel Wüstner

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Fluorescence loss in photobleaching (FLIP) is a modern microscopy method for visualization of transport processes in living cells. Although FLIP is widespread, an automated reliable analysis of image data is still lacking. This paper presents a framework for modeling and simulation of FLIP sequences as reaction– diffusion systems on segmented cell images. The cell geometry is extracted from microscopy images using the Chan–Vese active contours algorithm [8]. The PDE model is subsequently solved by the automated Finite Element software package FEniCS [20]. The flexibility of FEniCS allows for spatially resolved reaction diffu- sion coefficients in two (or more) spatial dimensions.
Original languageEnglish
JournalComputing and Visualization in Science
Volume17
Issue number4
Pages (from-to)151-166
ISSN1432-9360
DOIs
Publication statusPublished - Aug 2015

Fingerprint

Photobleaching
Computational Modeling
Fluorescence
Microscopy
Cell
Microscopic examination
Active Contours
Transport Processes
Reaction-diffusion
Reaction-diffusion System
Software Package
Software packages
Modeling and Simulation
Visualization
Flexibility
Cells
Finite Element
Geometry
Coefficient
Model

Cite this

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title = "Computational Modeling of Fluorescence Loss in Photobleaching",
abstract = "Fluorescence loss in photobleaching (FLIP) is a modern microscopy method for visualization of transport processes in living cells. Although FLIP is widespread, an automated reliable analysis of image data is still lacking. This paper presents a framework for modeling and simulation of FLIP sequences as reaction– diffusion systems on segmented cell images. The cell geometry is extracted from microscopy images using the Chan–Vese active contours algorithm [8]. The PDE model is subsequently solved by the automated Finite Element software package FEniCS [20]. The flexibility of FEniCS allows for spatially resolved reaction diffu- sion coefficients in two (or more) spatial dimensions.",
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Computational Modeling of Fluorescence Loss in Photobleaching. / Hansen, Christian Valdemar; Schroll, Achim; Wüstner, Daniel.

In: Computing and Visualization in Science, Vol. 17, No. 4, 08.2015, p. 151-166.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - Computational Modeling of Fluorescence Loss in Photobleaching

AU - Hansen, Christian Valdemar

AU - Schroll, Achim

AU - Wüstner, Daniel

PY - 2015/8

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N2 - Fluorescence loss in photobleaching (FLIP) is a modern microscopy method for visualization of transport processes in living cells. Although FLIP is widespread, an automated reliable analysis of image data is still lacking. This paper presents a framework for modeling and simulation of FLIP sequences as reaction– diffusion systems on segmented cell images. The cell geometry is extracted from microscopy images using the Chan–Vese active contours algorithm [8]. The PDE model is subsequently solved by the automated Finite Element software package FEniCS [20]. The flexibility of FEniCS allows for spatially resolved reaction diffu- sion coefficients in two (or more) spatial dimensions.

AB - Fluorescence loss in photobleaching (FLIP) is a modern microscopy method for visualization of transport processes in living cells. Although FLIP is widespread, an automated reliable analysis of image data is still lacking. This paper presents a framework for modeling and simulation of FLIP sequences as reaction– diffusion systems on segmented cell images. The cell geometry is extracted from microscopy images using the Chan–Vese active contours algorithm [8]. The PDE model is subsequently solved by the automated Finite Element software package FEniCS [20]. The flexibility of FEniCS allows for spatially resolved reaction diffu- sion coefficients in two (or more) spatial dimensions.

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DO - 10.1007/s00791-015-0259-6

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VL - 17

SP - 151

EP - 166

JO - Computing and Visualization in Science

JF - Computing and Visualization in Science

SN - 1432-9360

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