Version 1
: Received: 23 October 2023 / Approved: 24 October 2023 / Online: 25 October 2023 (08:19:41 CEST)
Version 2
: Received: 25 October 2023 / Approved: 25 October 2023 / Online: 27 October 2023 (07:12:26 CEST)
Version 3
: Received: 27 October 2023 / Approved: 1 November 2023 / Online: 2 November 2023 (09:15:48 CET)
Version 4
: Received: 6 November 2023 / Approved: 7 November 2023 / Online: 7 November 2023 (12:50:45 CET)
How to cite:
Sarmento Queiroga, A.; Cafundó Morais, M.C.; Stransky, B. Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics. Preprints2023, 2023101530. https://doi.org/10.20944/preprints202310.1530.v3
Sarmento Queiroga, A.; Cafundó Morais, M.C.; Stransky, B. Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics. Preprints 2023, 2023101530. https://doi.org/10.20944/preprints202310.1530.v3
Sarmento Queiroga, A.; Cafundó Morais, M.C.; Stransky, B. Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics. Preprints2023, 2023101530. https://doi.org/10.20944/preprints202310.1530.v3
APA Style
Sarmento Queiroga, A., Cafundó Morais, M.C., & Stransky, B. (2023). Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics. Preprints. https://doi.org/10.20944/preprints202310.1530.v3
Chicago/Turabian Style
Sarmento Queiroga, A., Mauro Cesar Cafundó Morais and Beatriz Stransky. 2023 "Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics" Preprints. https://doi.org/10.20944/preprints202310.1530.v3
Abstract
Cellular Automata and Boolean Networks are generalizations of one another because algorithms to compute the preimage of cellular automata reveal the underlying network, i.e., the global dynamics in terms of the basins of attraction. Therefore, we hypothesize we can reveal the local dynamics of cellular automata from the basin of attraction of an inferred boolean network. Our motivation was the observation that human keratinocytes and melanoma can stick together to form clusters after eight days in co-culture. This cluster formation would be the attractor of population dynamics, and cell seeding would be the initial condition of the basin of attraction. Hence, we propose a method to estimate the rules of cellular automata, which consist of comparing the density states within each state transition and reaching a consensus among state transitions belonging to a basin of attraction. Therefore, we aim: (1) to infer a boolean network from the \emph{in vitro} co-culture growth curve; (2) to estimate the rules of cellular automata; and (3) to implement a cellular automata for spatial dynamics simulations. The binarization of the growth curve shows high population density after four days; the estimated cellular automata rules were compatible with cell proliferation and migration in agreement with experimental observations. Spatial dynamics shows that: (1) keratinocytes exhibit higher density in neighborhoods where melanoma is present; (2) the chance of keratinocyte migration increases until the fourth day, but the probability of survival increases; and (3) space is freed for cells with maximum proliferation capacity through proliferation compensated by death. Our approach suggests that the attractor state of cell co-culture would be induced by an increase in keratinocyte migration and survival, as well as the balance of proliferation and death concerning melanoma. Our approach has the potential to offer valuable clues about microenvironmental interactions or configurations that drive population dynamics.
Keywords
cells co-culture; population dynamic; cellular automata; boolean network; basin of attraction
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Alexandre Queiroga
Commenter's Conflict of Interests: Author