Single molecule dynamics in a virtual cell: a three-dimensional model that produces simulated fluorescence video-imaging data

J R Soc Interface. 2014 Sep 6;11(98):20140442. doi: 10.1098/rsif.2014.0442.


The analysis of single molecule imaging experiments is complicated by the stochastic nature of single molecule events, by instrument noise and by the limited information which can be gathered about any individual molecule observed. Consequently, it is important to cross check experimental results using a model simulating single molecule dynamics (e.g. movements and binding events) in a virtual cell-like environment. The output of such a model should match the real data format allowing researchers to compare simulated results with the real experiments. The proposed model exploits the advantages of 'object-oriented' computing. First of all, the ability to create and manipulate a number of classes, each containing an arbitrary number of single molecule objects. These classes may include objects moving within the 'cytoplasm'; objects moving at the 'plasma membrane'; and static objects located inside the 'body'. The objects of a given class can interact with each other and/or with the objects of other classes according to their physical and chemical properties. Each model run generates a sequence of images, each containing summed images of all fluorescent objects emitting light under given illumination conditions with realistic levels of noise and emission fluctuations. The model accurately reproduces reported single molecule experiments and predicts the outcome of future experiments.

Keywords: cell model; object-oriented modelling; single molecule imaging.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cell Membrane / metabolism*
  • Cytoplasm / metabolism*
  • Diffusion
  • Fluorescence
  • Imaging, Three-Dimensional
  • Kinetics
  • Membrane Microdomains
  • Microscopy, Confocal
  • Microscopy, Fluorescence*
  • Microscopy, Video*
  • Models, Theoretical*
  • Movement
  • Probability
  • Protein Binding
  • Stochastic Processes