Marco Ruscone, Andrea Checcoli, Randy Heiland, Emmanuel Barillot, Paul Macklin, Laurence Calzone, Vincent Noël
{"title":"Building multiscale models with PhysiBoSS, an agent-based modeling tool","authors":"Marco Ruscone, Andrea Checcoli, Randy Heiland, Emmanuel Barillot, Paul Macklin, Laurence Calzone, Vincent Noël","doi":"arxiv-2406.18371","DOIUrl":null,"url":null,"abstract":"Multiscale models provide a unique tool for studying complex processes that\nstudy events occurring at different scales across space and time. In the\ncontext of biological systems, such models can simulate mechanisms happening at\nthe intracellular level such as signaling, and at the extracellular level where\ncells communicate and coordinate with other cells. They aim to understand the\nimpact of genetic or environmental deregulation observed in complex diseases,\ndescribe the interplay between a pathological tissue and the immune system, and\nsuggest strategies to revert the diseased phenotypes. The construction of these\nmultiscale models remains a very complex task, including the choice of the\ncomponents to consider, the level of details of the processes to simulate, or\nthe fitting of the parameters to the data. One additional difficulty is the\nexpert knowledge needed to program these models in languages such as C++ or\nPython, which may discourage the participation of non-experts. Simplifying this\nprocess through structured description formalisms -- coupled with a graphical\ninterface -- is crucial in making modeling more accessible to the broader\nscientific community, as well as streamlining the process for advanced users.\nThis article introduces three examples of multiscale models which rely on the\nframework PhysiBoSS, an add-on of PhysiCell that includes intracellular\ndescriptions as continuous time Boolean models to the agent-based approach. The\narticle demonstrates how to easily construct such models, relying on PhysiCell\nStudio, the PhysiCell Graphical User Interface. A step-by-step tutorial is\nprovided as a Supplementary Material and all models are provided at:\nhttps://physiboss.github.io/tutorial/.","PeriodicalId":501572,"journal":{"name":"arXiv - QuanBio - Tissues and Organs","volume":"28 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Tissues and Organs","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2406.18371","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Multiscale models provide a unique tool for studying complex processes that
study events occurring at different scales across space and time. In the
context of biological systems, such models can simulate mechanisms happening at
the intracellular level such as signaling, and at the extracellular level where
cells communicate and coordinate with other cells. They aim to understand the
impact of genetic or environmental deregulation observed in complex diseases,
describe the interplay between a pathological tissue and the immune system, and
suggest strategies to revert the diseased phenotypes. The construction of these
multiscale models remains a very complex task, including the choice of the
components to consider, the level of details of the processes to simulate, or
the fitting of the parameters to the data. One additional difficulty is the
expert knowledge needed to program these models in languages such as C++ or
Python, which may discourage the participation of non-experts. Simplifying this
process through structured description formalisms -- coupled with a graphical
interface -- is crucial in making modeling more accessible to the broader
scientific community, as well as streamlining the process for advanced users.
This article introduces three examples of multiscale models which rely on the
framework PhysiBoSS, an add-on of PhysiCell that includes intracellular
descriptions as continuous time Boolean models to the agent-based approach. The
article demonstrates how to easily construct such models, relying on PhysiCell
Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is
provided as a Supplementary Material and all models are provided at:
https://physiboss.github.io/tutorial/.