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      PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

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          Abstract

          Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification – a markup language – that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open-source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a GUI that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years, which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open-source software and contributions from the community are encouraged.

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            Matplotlib: A 2D Graphics Environment

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              Array programming with NumPy

              Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: SoftwareRole: Visualization
                Role: SoftwareRole: Writing - original draft
                Role: SoftwareRole: Visualization
                Role: Software
                Role: Visualization
                Role: SoftwareRole: Visualization
                Role: Software
                Role: SoftwareRole: Visualization
                Role: Funding acquisitionRole: Conceptualization
                Journal
                GigaByte
                GigaByte
                Gigabyte
                GigaByte
                GigaScience Press (Sha Tin, New Territories, Hong Kong SAR )
                2709-4715
                19 June 2024
                2024
                : 2024
                : gigabyte128
                Affiliations
                [ 1 ] Department of Intelligent Systems Engineering, Indiana University , Bloomington, IN, USA
                [ 2 ] Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University , Baltimore, MD, USA
                [ 3 ] Convergence Institute, Johns Hopkins University , Baltimore, MD, USA
                [ 4 ] Allen Institute for Cell Science , Seattle, WA, USA
                [ 5 ] University of Wisconsin , Madison, WI, USA
                [ 6 ] Institut Curie, Université PSL , F-75005, Paris, France
                [ 7 ] INSERM , U900, F-75005, Paris, France
                [ 8 ] Mines ParisTech, Université PSL , F-75005, Paris, France
                [ 9 ] Sorbonne Université , Collège Doctoral, F-75005, Paris, France
                Author notes
                [ * ] Corresponding author. E-mail: macklinp@ 123456iu.edu
                Author information
                https://orcid.org/0000-0002-7440-2905
                https://orcid.org/0000-0002-2166-6616
                https://orcid.org/0000-0002-6038-6035
                https://orcid.org/0000-0002-5876-2644
                https://orcid.org/0000-0002-7607-0203
                https://orcid.org/0000-0003-3448-291X
                https://orcid.org/0000-0002-9925-0151
                Article
                TRR-202401-01 128
                10.46471/gigabyte.128
                11211762
                6068f26b-78ad-4649-a54a-9ebdd12590f6
                © The Author(s) 2024.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 January 2024
                : 10 June 2024
                Funding
                Funded by: National Science Foundation;
                Award ID: 1720625
                Funded by: National Science Foundation;
                Award ID: 2303695
                Funded by: National Institutes of Health;
                Award ID: U01-CA232137-01
                Funded by: Jayne Koskinas Ted Giovanis Foundation for Health and Policy;
                Funded by: European Commission;
                Award ID: H2020-ICT-951773
                We thank the National Science Foundation (Awards 1720625 and 2303695), the National Institutes of Health (U01-CA232137-01), and the Jayne Koskinas Ted Giovanis Foundation for Health and Policy. This work was also supported by the European Commission under the PerMedCoE project (H2020-ICT-951773) and the Inserm amorçage project.
                Categories
                Technical Release
                Software and Workflows
                Cell Biology
                Systems Biology

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