Biological cells are usually operating in conditions characterized by intercellular signaling and interaction, which are supposed to strongly influence individual cell dynamics. In this work, we study the dynamics of interacting random Boolean networks, focusing on attractor properties and response to perturbations. We observe that the properties of isolated critical Boolean networks are substantially maintained also in interaction settings, while interactions bias the dynamics of chaotic and ordered networks toward that of critical cells. The increase in attractors observed in multicellular scenarios, compared to single cells, allows us to hypothesize that biological processes, such as ontogeny and cell differentiation, leverage interactions to modulate individual and collective cell responses.
Braccini, M., Baldini, P., Roli, A. (2025). Cell–Cell Interactions: How Coupled Boolean Networks Tend to Criticality. ARTIFICIAL LIFE, 31(1), 68-80 [10.1162/artl_a_00444].
Cell–Cell Interactions: How Coupled Boolean Networks Tend to Criticality
Braccini, Michele
Primo
;Baldini, PaoloSecondo
;Roli, AndreaUltimo
2025
Abstract
Biological cells are usually operating in conditions characterized by intercellular signaling and interaction, which are supposed to strongly influence individual cell dynamics. In this work, we study the dynamics of interacting random Boolean networks, focusing on attractor properties and response to perturbations. We observe that the properties of isolated critical Boolean networks are substantially maintained also in interaction settings, while interactions bias the dynamics of chaotic and ordered networks toward that of critical cells. The increase in attractors observed in multicellular scenarios, compared to single cells, allows us to hypothesize that biological processes, such as ontogeny and cell differentiation, leverage interactions to modulate individual and collective cell responses.File | Dimensione | Formato | |
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alife-cell-cellartl_a_00444.pdf
embargo fino al 24/05/2025
Descrizione: © 2024 Massachusetts Institute of Technology Artificial Life 31: 68–80 (2025) https://doi.org/10.1162/artl_a_00444
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