User:David Dreher/Notebook/Chromatin controlled cell pattern
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Chromatin describes the complex of DNA and proteins that packs DNA into condensed structures. But this is not its only function: by packing the DNA it prevents possible transcription and therefore is a powerful mechanism for control of gene expression. Especially since the chromatin state can be passed on to progenies allowing for a fixed gene expression over multiple proliferations. Experiments in Drosophila could show that dependent on the position of gene on the chromosome and therefore it’s chromatin packing state different reproducible cell proliferation patterns emerged. The resulting different eye patterns lead to the hypothesis that the chromatin packaging state of a gene can govern the pattern phenotype of the corresponding tissue. Therefore chromatin engineering yields the possibility to further understand and manipulate cell patterns in mammalian cells. The basis for this project is a mammalian cell line that switches between two possible states reported by fluorescent proteins by means of engineered chromatin. The engineering approach allows for tuning production rate of chromatin binding proteins and their association and disassociation rates. The aim of this project is to assay whether those parameters, e.g. production, dis- and association rate, influence possible proliferation patterns. The main emphasis will lie on developing a multi-parametric analysis for cell culture images. Multi-parametric analysis describes an assay with multiple measured parameters of the same entity, in this case a microscopy image of a cell culture. Since the aim is to examine cell patterns we have to analyze images of complete cell colonies. To allow for computational comparison we need to determine multiple parameters that sufficiently describe the underlying pattern, for example fluorescence intensity, mean distance between cells in state 1 and in state 2. The goal hereby would be to develop a automated method to assay taken images of cell colonies for later comparison. The leading question is whether we can determine a correlation between our input parameters and resulting cell patterns. This could also allow for determination of modeling parameters for the kinetic interactions. Hopefully the engineering approach allows us to program the resulting patterns. This would be a very powerful tool for future synthetic biology, especially with an outlook towards use in tissue engineering and regenerative medicine.