CellularPotts.jl: simulating multiscale cellular models in Julia.

CellularPotts.jl: simulating multiscale cellular models in Julia.
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DOI:
10.1093/bioinformatics/btad773
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发表时间:
2024-01-02
期刊:
Bioinformatics (Oxford, England)
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CellularPotts.jl 是一个用 Julia 编写的软件包,用于模拟生物细胞过程,例如分裂、粘附和信号传导。准确建模和预测这些简单的过程至关重要,因为它们促进了与肿瘤生长、伤口愈合和感染等重要疾病状态相关的更复杂的生物现象。在这里,我们利用细胞波茨建模来模拟细胞相互作用,并将其与微分方程相结合来模拟动态细胞信号传导模式。这些模型比其他方法更有优势,因为它们保留了每个单元的空间信息,同时在更大的尺度上保持计算效率。该包的用户定义三个关键输入来创建有效的模型定义:2 维或 3 维空间、描述要在该空间中定位的细胞的表格以及指示细胞行为的模型惩罚列表。然后,模型可以随着时间的推移不断发展以收集统计数据,反复模拟以研究改变特定属性如何影响细胞行为,并使用 Julia 中任何可用的绘图库进行可视化。 CellularPotts.jl 软件包根据 MIT 许可证发布,可从 https://github.com/RobertGregg/CellularPotts.jl 获取。提交时的代码存档版本 (v0.3.2) 也可以在 https://doi.org/10.5281/zenodo.10407783 上找到。
CellularPotts.jl is a software package written in Julia to simulate biological cellular processes such as division, adhesion, and signaling. Accurately modeling and predicting these simple processes is crucial because they facilitate more complex biological phenomena related to important disease states like tumor growth, wound healing, and infection. Here we take advantage of Cellular Potts Modeling to simulate cellular interactions and combine them with differential equations to model dynamic cell signaling patterns. These models are advantageous over other approaches because they retain spatial information about each cell while remaining computationally efficient at larger scales. Users of this package define three key inputs to create valid model definitions: a 2- or 3-dimensional space, a table describing the cells to be positioned in that space, and a list of model penalties that dictate cell behaviors. Models can then be evolved over time to collect statistics, simulated repeatedly to investigate how changing a specific property impacts cellular behavior, and visualized using any of the available plotting libraries in Julia. The CellularPotts.jl package is released under the MIT license and is available at https://github.com/RobertGregg/CellularPotts.jl. An archived version of the code (v0.3.2) at time of submission can also be found at https://doi.org/10.5281/zenodo.10407783.
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