Epigenetic forest and flower morphogenesis. (June 2022)

• Record Type: Journal Article
• Authors: Perez-Buendia, J. Rogelio; Cortes-Poza, Yuriria; Padilla-Longoria, Pablo
• Is Part Of: Computational biology and chemistry. Volume 98(2022)
• Publication Date: 2022-06
• Subjects: Biomathematics -- Gene regulatory networks -- Epigenetic landscapes -- Morphogenetic model; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107667
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: This paper studies the epigenetic process that leads to Angiosperms' flower architecture (flowering plants). As a case study, we analyze the flower Arabidopsis thaliana 's GRN obtained during cell fate determination in the early stages of the flower's development, which was constructed in a previous work using experimental data. We start by constructing and analyzing the Epigenetic Forest, a discrete representation of Waddington's Epigenetic Landscape, obtained as the transition graph of the discrete dynamical system associated with the GRN. Next, we propose an optimization problem to model morphogenesis by defining a biologically meaningful function that accounts for the work involved in cell specialization. Finally, the problem is solved using a genetic algorithm. The optimal solution found by the algorithm correctly recovers the flower's architecture, as observed in wild type flowers and recovered in other theoretical works. Even though the case study addresses this specific problem, the method is directly applicable to other GRN's with attractors consisting of equilibrium points only and could be extended to the situation where there are periodic attractors. Graphical Abstract: ga1 Highlights: We propose Epigenetic Forests as a tool to study morphogenesis from Genetic Regulatory Networks (GRN). We propose an optimization problem to model morphogenesis by defining a biologically meaningful energy function Our method unfolds the richness and structure within the GRN and it is a novel approach. The model is based on experimental data and is general enough to be used to study the relationship between genotype-phenotype in other GRN with similar characteristics. Our method allows us to use tools from finite field arithmetic and finite field dynamical systems.