Abstract
The relationship between the genotype, the set of instructions encoded into a genome, and the phenotype, the macroscopic realization of those instructions, has not been fully explored. This is mostly due to the general absence of tools capable of uncovering this relationship. In this work, we develop an unsupervised learning framework relating changes in cellular morphology to genetic modifications. We focus on yeast organelles called vacuoles, which are cellular compartments that vary in size and shape as a response to various stimuli. Our approach can be applied extensively for live fluorescence image analysis, potentially unveiling the basic principles relating genotypic variation to vacuole morphology in yeast cells. This can, in turn, be a first step for the inference of cell design principles of cellular organelles with a desired morphology.
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Acknowledgements
This material is partially based upon work supported by NSF grant No. DBI-1548297. VPP was supported by FSE REACT-EU-PON 2014–2020, DM 1062/2021.
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Pastore, V.P. et al. (2023). An Unsupervised Learning Approach to Resolve Phenotype to Genotype Mapping in Budding Yeasts Vacuoles. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14234. Springer, Cham. https://doi.org/10.1007/978-3-031-43153-1_21
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