Forest structure in epigenetic landscapes
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912965165318144 |
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| author | Cortes-Poza, Yuriria Perez-Buendia, J. Rogelio |
| author_facet | Cortes-Poza, Yuriria Perez-Buendia, J. Rogelio |
| contents | Morphogenesis is the biological process that causes the emergence and changes of patterns (tissues and organs) in living organisms. It is a robust, self-organising mechanism, governed by Genetic Regulatory Networks (GRN), that hasn't been thoroughly understood. In this work we propose Epigenetic Forests as a tool to study morphogenesis and to extract valuable information from GRN. Our method unfolds the richness and structure within the GRN.
As a case study, we analyze the GRN during cell fate determination during the early stages of development of the flower Arabidopsis thaliana and its spatial dynamics. By using a genetic algorithm we optimize cell differentiation in our model and correctly recover the architecture of the flower. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1903_09386 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Forest structure in epigenetic landscapes Cortes-Poza, Yuriria Perez-Buendia, J. Rogelio Molecular Networks Neural and Evolutionary Computing Morphogenesis is the biological process that causes the emergence and changes of patterns (tissues and organs) in living organisms. It is a robust, self-organising mechanism, governed by Genetic Regulatory Networks (GRN), that hasn't been thoroughly understood. In this work we propose Epigenetic Forests as a tool to study morphogenesis and to extract valuable information from GRN. Our method unfolds the richness and structure within the GRN. As a case study, we analyze the GRN during cell fate determination during the early stages of development of the flower Arabidopsis thaliana and its spatial dynamics. By using a genetic algorithm we optimize cell differentiation in our model and correctly recover the architecture of the flower. |
| title | Forest structure in epigenetic landscapes |
| topic | Molecular Networks Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/1903.09386 |