Version 1
: Received: 26 September 2024 / Approved: 27 September 2024 / Online: 29 September 2024 (05:58:46 CEST)
How to cite:
Ledda, M.; Pluchino, A.; Ragusa, M. Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach. Preprints2024, 2024092259. https://doi.org/10.20944/preprints202409.2259.v1
Ledda, M.; Pluchino, A.; Ragusa, M. Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach. Preprints 2024, 2024092259. https://doi.org/10.20944/preprints202409.2259.v1
Ledda, M.; Pluchino, A.; Ragusa, M. Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach. Preprints2024, 2024092259. https://doi.org/10.20944/preprints202409.2259.v1
APA Style
Ledda, M., Pluchino, A., & Ragusa, M. (2024). Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach. Preprints. https://doi.org/10.20944/preprints202409.2259.v1
Chicago/Turabian Style
Ledda, M., Alessandro Pluchino and Marco Ragusa. 2024 "Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach" Preprints. https://doi.org/10.20944/preprints202409.2259.v1
Abstract
Complexity of issues in cancer research has led to the introduction of powerful computational tools for helping experimental in vivo and in vitro methods. These tools, typically focused on studying cell behavior and dynamic cell populations, range from systems of differential equations solved numerically, to lattice models and agent-based simulations. In particular, agent-based models (ABMs) are increasingly used for they capability to incorporate multi-scale features ranging from the individual up to the population level, thus combining statistically aggregated assumptions with individual heterogeneity. In this work, we present an ABM that simulates tumor progression in a colonic crypt, with the aim of providing an experimental in silico environment for testing results achieved in traditional laboratory research and developing alternative scenarios of tumor development. The model also allows some speculations about causal relationships in biologically inspired systems.
Keywords
Agent-based Models; Colorectal cancer (CRC); Biological evolution
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.
