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:
Queiroga, A.S.; Morais, M.C.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.v4
Queiroga, A.S.; Morais, M.C.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.v4
Queiroga, A.S.; Morais, M.C.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.v4
APA Style
Queiroga, A.S., Morais, M.C.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.v4
Chicago/Turabian Style
Queiroga, A.S., 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.v4
Abstract
Human keratinocytes and melanoma can stick together to form clusters after eight days in co-culture. As in dynamic system concepts, one can consider cluster formation as the system attractor, cell seeding as the initial condition, and density change over time as a path within the basin of attraction. Herein, Cellular Automata, which is a class of Agent-Based Models, and Boolean Networks are discrete modeling methods used in population dynamics such that running the dynamics of cellular automata backward reveals an underlying network in terms of basin of attraction, also known as global dynamics. Thus, we hypothesize that one can estimate the local dynamic of an agent-based model from the basin of attraction of a boolean network. Here, we propose an approach to estimating these agent-based model rules, which consists of comparing the density states within each state transition and reaching a consensus among state transitions belonging to a basin of attraction of a boolean network. The objectives of this study are: (1) to infer a boolean network from the co-culture growth curve; (2) to estimate the rules of agent-based models; and (3) to implement spatial dynamics simulations. The binarization of the growth curve shows high population density after four days; the estimated agent-based model rules were compatible with cell proliferation and migration in agreement with literature, so we had to propose individual rules for survival and death. Spatial dynamics shows that (1) keratinocytes exhibit a higher density in neighborhoods where melanoma is present; (2) the chance of keratinocyte migration increases until the fourth day, then decreases, and the probability of survival increases substantially after four days; and (3) cells with low proliferation capacity die and free space for those with high ones. Our approach suggests that the attractor state of cell co-culture is induced mainly by an increase in keratinocyte migration and survival. 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.
Received:
7 November 2023
Commenter:
Alexandre Queiroga
Commenter's Conflict of Interests:
Author
Comment:
I've received some good feedback by email from the authors cited, so I decided to upload an ultimate revised version before submission to peer review. I made some changes to my writing in the introduction and discussion. In short, I have rewritten the reason why boolean network state transition would be a substrate to estimate CA rules, and I have updated a discussion about the logical equation and tumor microenvironment features.
Commenter: Alexandre Queiroga
Commenter's Conflict of Interests: Author