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2020 NIMBioS SRE Program

Agent-based simulation of Caenorhabditis elegans

Matthew Clark, Computer Science, Mathematics, Fisk Univ.
Chelsea Seggern, Kinesiology, Univ. of Tennessee
Anna Thomas, Mathematics, Computer Science, Lehigh Univ.

Dr. Xiaopeng Zhao, Mechanical, Aerospace and Biomedical Engineering, Univ. of Tennessee
Dr. Tian Hong, Biochemistry & Cellular and Molecular Biology, Genome Science & Technology, Univ. of Tennessee

C. elegans photo. Project Description. Caenorhabditis elegans (C. elegans) is an ideal model organism for biomedical research due to its short life cycle, simple genetics, transparent body, and ease of cultivation. The embryogenesis of C. elegans is about 12-14 hours. Although a fully grown C. elegans has no more than 1000 body cells, it has well-differentiated organs, such as muscles, nerves, gut cells, etc. Despite its simple body structure, researchers can use C. elegans to elicit information about more complex biological functions in humans and other animals. This project aims to develop a research and teaching tool using agent-based modeling (ABM) approaches of C. elegans. The ABM model will simulate the early to mid-embryogenesis stages up to 300 cells. The model will include a complete description of the lineage tree. A number of rules will be defined based on biological observations to simulate cell division, orientation, and migration. The simulation is a stochastic process to account for random variation in cell division time, polarity, fate etc. The ABM will be written in Python. A user-friendly interface will be created for ease of examining and visualizing the simulation results. Scripts of the model will be uploaded to an open forum, such as Github, to allow open access. The model can be easily modified to describe signal pathways in more complex organisms. Methods to analyze the sensitivity of model outputs to input rates will be used by the students.

C. Seggern photo. A. Thomas photo.
Chelsea Seggern Anna Thomas

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From 2008 until early 2021, NIMBioS was supported by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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