Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics DOI Open Access
Alexandre Sarmento Queiroga, Mauro César Cafundó Morais, Beatriz Stransky

et al.

Published: Nov. 2, 2023

Cellular Automata and Boolean Networks are generalizations of one another because algorithms to compute the preimage cellular automata reveal underlying network, i.e., global dynamics in terms basins attraction. Therefore, we hypothesize can local from basin attraction an inferred boolean network. Our motivation was observation that human keratinocytes melanoma stick together form clusters after eight days co-culture. This cluster formation would be attractor population dynamics, cell seeding initial condition Hence, propose a method estimate rules automata, which consist comparing density states within each state transition reaching consensus among transitions belonging aim: (1) infer network \emph{in vitro} co-culture growth curve; (2) automata; (3) implement for spatial simulations. The binarization curve shows high four days; estimated were compatible with proliferation migration agreement experimental observations. Spatial that: exhibit higher neighborhoods where is present; chance keratinocyte increases until fourth day, but probability survival increases; space freed cells maximum capacity through compensated by death. approach suggests induced increase survival, as well balance death concerning melanoma. has potential offer valuable clues about microenvironmental interactions or configurations drive dynamics.

Language: Английский

Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics DOI Open Access
Alexandre Sarmento Queiroga, Mauro César Cafundó Morais, Beatriz Stransky

et al.

Published: Nov. 7, 2023

Human keratinocytes and melanoma can stick together to form clusters after eight days in co-culture. As dynamic system concepts, one consider cluster formation as the attractor, cell seeding initial condition, density change over time a path within basin of attraction. Herein, Cellular Automata, which is class Agent-Based Models, Boolean Networks are discrete modeling methods used population dynamics such that running cellular automata backward reveals an underlying network terms attraction, also known global dynamics. Thus, we hypothesize estimate local agent-based model from attraction boolean network. Here, propose approach estimating these rules, consists comparing states each state transition reaching consensus among transitions belonging The objectives this study are: (1) infer co-culture growth curve; (2) rules models; (3) implement spatial simulations. binarization curve shows high four days; estimated were compatible with proliferation migration agreement literature, so had individual for survival death. Spatial exhibit higher neighborhoods where present; chance keratinocyte increases until fourth day, then decreases, probability substantially cells low capacity die free space those ones. Our suggests attractor induced mainly by increase survival. has potential offer valuable clues about microenvironmental interactions or configurations drive

Language: Английский

Citations

2
Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics DOI Open Access
Alexandre Sarmento Queiroga, Mauro César Cafundó Morais, Beatriz Stransky

et al.

Published: Nov. 2, 2023

Cellular Automata and Boolean Networks are generalizations of one another because algorithms to compute the preimage cellular automata reveal underlying network, i.e., global dynamics in terms basins attraction. Therefore, we hypothesize can local from basin attraction an inferred boolean network. Our motivation was observation that human keratinocytes melanoma stick together form clusters after eight days co-culture. This cluster formation would be attractor population dynamics, cell seeding initial condition Hence, propose a method estimate rules automata, which consist comparing density states within each state transition reaching consensus among transitions belonging aim: (1) infer network \emph{in vitro} co-culture growth curve; (2) automata; (3) implement for spatial simulations. The binarization curve shows high four days; estimated were compatible with proliferation migration agreement experimental observations. Spatial that: exhibit higher neighborhoods where is present; chance keratinocyte increases until fourth day, but probability survival increases; space freed cells maximum capacity through compensated by death. approach suggests induced increase survival, as well balance death concerning melanoma. has potential offer valuable clues about microenvironmental interactions or configurations drive dynamics.

Language: Английский

Citations

0