FBCA (Flux Balance Cellular Automata) has been recently proposed as a new multi-scale modeling framework to represent the spatial dynamics of multi-cellular systems, while simultaneously taking into account the metabolic activity of individual cells. Preliminary results have revealed the potentialities of the framework in enabling to identify and analyze complex emergent properties of cellular populations, such as spatial patterns phenomena and synchronization effects. Here we move a step forward, by exploring the possibility of integrating real-world data into the framework. To this end, we seek to customize the metabolism of individual cells according to single-cell gene expression profiles. We investigate the effect on cell metabolism of the interplay between: (a) the environmental conditions determined by nutrient diffusion dynamics; (b) the activation or deactivation of metabolic pathways determined by gene expression.
Maspero, D., Di Filippo, M., Angaroni, F., Pescini, D., Mauri, G., Vanoni, M., et al. (2020). Integration of single-cell RNA-sequencing data into flux balance cellular automata. In CIBB 2019 – Computational Intelligence methods for Bioinformatics and Biostatistics, Revised selected papers (pp.207-215). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-63061-4_19].
Integration of single-cell RNA-sequencing data into flux balance cellular automata
Maspero D.;Di Filippo M.;Angaroni F.;Pescini D.;Mauri G.;Vanoni M.;Graudenzi A.
;Damiani C.
2020
Abstract
FBCA (Flux Balance Cellular Automata) has been recently proposed as a new multi-scale modeling framework to represent the spatial dynamics of multi-cellular systems, while simultaneously taking into account the metabolic activity of individual cells. Preliminary results have revealed the potentialities of the framework in enabling to identify and analyze complex emergent properties of cellular populations, such as spatial patterns phenomena and synchronization effects. Here we move a step forward, by exploring the possibility of integrating real-world data into the framework. To this end, we seek to customize the metabolism of individual cells according to single-cell gene expression profiles. We investigate the effect on cell metabolism of the interplay between: (a) the environmental conditions determined by nutrient diffusion dynamics; (b) the activation or deactivation of metabolic pathways determined by gene expression.
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