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PhysiBoSS:一个整合物理维度和细胞信号的多尺度基于代理的建模框架。

PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling.

机构信息

Institut Curie, PSL Research University, Paris, France.

INSERM, U900, Paris, France.

出版信息

Bioinformatics. 2019 Apr 1;35(7):1188-1196. doi: 10.1093/bioinformatics/bty766.

Abstract

MOTIVATION

Due to the complexity and heterogeneity of multicellular biological systems, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful. We present PhysiBoSS, an open source software which combines intracellular signalling using Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell).

RESULTS

PhysiBoSS provides a flexible and computationally efficient framework to explore the effect of environmental and genetic alterations of individual cells at the population level, bridging the critical gap from single-cell genotype to single-cell phenotype and emergent multicellular behaviour. PhysiBoSS thus becomes very useful when studying heterogeneous population response to treatment, mutation effects, different modes of invasion or isomorphic morphogenesis events. To concretely illustrate a potential use of PhysiBoSS, we studied heterogeneous cell fate decisions in response to TNF treatment. We explored the effect of different treatments and the behaviour of several resistant mutants. We highlighted the importance of spatial information on the population dynamics by considering the effect of competition for resources like oxygen.

AVAILABILITY AND IMPLEMENTATION

PhysiBoSS is freely available on GitHub (https://github.com/sysbio-curie/PhysiBoSS), with a Docker image (https://hub.docker.com/r/gletort/physiboss/). It is distributed as open source under the BSD 3-clause license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

由于多细胞生物系统的复杂性和异质性,考虑到细胞信号转导、细胞群体行为和细胞外环境的数学模型特别有帮助。我们介绍了 PhysiBoSS,这是一个开源软件,它结合了使用布尔模型(MaBoSS)进行细胞内信号转导和使用基于代理的模型(PhysiCell)进行多细胞行为的方法。

结果

PhysiBoSS 提供了一个灵活且计算效率高的框架,可以在群体水平上探索单个细胞的环境和遗传改变对个体细胞的影响,从而弥合从单细胞基因型到单细胞表型和涌现的多细胞行为的关键差距。因此,当研究异质群体对治疗、突变效应、不同入侵模式或同态形态发生事件的反应时,PhysiBoSS 非常有用。为了具体说明 PhysiBoSS 的潜在用途,我们研究了对 TNF 治疗的异质细胞命运决定。我们探讨了不同处理方法的效果和几种抗性突变体的行为。我们通过考虑对资源(如氧气)的竞争等因素对群体动态的影响,强调了空间信息的重要性。

可用性和实现

PhysiBoSS 可在 GitHub(https://github.com/sysbio-curie/PhysiBoSS)上免费获得,并有一个 Docker 镜像(https://hub.docker.com/r/gletort/physiboss/)。它以 BSD 3 条款许可证的形式作为开源分发。

补充信息

补充数据可在 Bioinformatics 在线获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3bb/6449758/01921c05daf2/bty766f1.jpg

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