Particle Simulations of Growth: Application to Tumorigenesis

Bergdorf, Michael; Milde, Florian; Koumoutsakos, Petros

doi:10.1007/978-1-4614-0052-3_11

Cited by 1 publication

(2 citation statements)

References 51 publications

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“…This can be done using a variety of methods 57, including triangulated surfaces 58, pixel/voxel sets 59, and implicit surface representations such as level sets 60 or phase fields 61. Level-set methods are well suited for simulating moving and deforming geometries 6, 62, 63.…”

Section: The Third Step: Simulationmentioning

confidence: 99%

“…This is because the curvature of the surface needs to be explicitly accounted for, which often requires that it first be computed or numerically approximated. If the surface additionally moves or deforms over time, possibly in function of the local concentration of the substances diffusing on it, the problem gets even more complicated 62, 63. In the ER FRAP example 11, the geometry of the ER was represented as a level set 64, and the method has also been extended to simulating diffusion in the ER membrane 12.…”

Section: The Third Step: Simulationmentioning

confidence: 99%

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Modeling and simulation of biological systems from image data

Sbalzarini

2013

BioEssays

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This essay provides an introduction to the terminology, concepts, methods, and challenges of image-based modeling in biology. Image-based modeling and simulation aims at using systematic, quantitative image data to build predictive models of biological systems that can be simulated with a computer. This allows one to disentangle molecular mechanisms from effects of shape and geometry. Questions like “what is the functional role of shape” or “how are biological shapes generated and regulated” can be addressed in the framework of image-based systems biology. The combination of image quantification, model building, and computer simulation is illustrated here using the example of diffusion in the endoplasmic reticulum.

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