PhysiPKPD: A pharmacokinetics and pharmacodynamics module for PhysiCell

Bergman, Daniel; Marazzi, Lauren; Chowkwale, Mukti; M, Deepa Maheshvare; Bidanta, Supriya; Mapder, Tarunendu; Li, Jialun

doi:10.46471/gigabyte.72

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…We are currently working on expanding this tool to include the extracellular matrix and its interactions with the cells 28 , blood vessels and their vascularisation and complex three-dimensional architectures taken from spatial omics of patients. Additionally, we are also considering expanding this work with the recent inclusion of PK/PD models in PhysiCell 44 .…”

Section: Discussionmentioning

confidence: 99%

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

Ponce-de-Leon,

Montagud,

Noël

et al. 2023

npj Syst Biol Appl

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In systems biology, mathematical models and simulations play a crucial role in understanding complex biological systems. Different modelling frameworks are employed depending on the nature and scales of the system under study. For instance, signalling and regulatory networks can be simulated using Boolean modelling, whereas multicellular systems can be studied using agent-based modelling. Herein, we present PhysiBoSS 2.0, a hybrid agent-based modelling framework that allows simulating signalling and regulatory networks within individual cell agents. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS 1.0 and was conceived as an add-on that expands the PhysiCell functionalities by enabling the simulation of intracellular cell signalling using MaBoSS while keeping a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 also expands the set of functionalities offered to the users, including custom models and cell specifications, mechanistic submodels of substrate internalisation and detailed control over simulation parameters. Together with PhysiBoSS 2.0, we introduce PCTK, a Python package developed for handling and processing simulation outputs, and generating summary plots and 3D renders. PhysiBoSS 2.0 allows studying the interplay between the microenvironment, the signalling pathways that control cellular processes and population dynamics, suitable for modelling cancer. We show different approaches for integrating Boolean networks into multi-scale simulations using strategies to study the drug effects and synergies in models of cancer cell lines and validate them using experimental data. PhysiBoSS 2.0 is open-source and publicly available on GitHub with several repositories of accompanying interoperable tools.

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Section: Discussionmentioning

confidence: 99%

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

Ponce-de-Leon,

Montagud,

Noël

et al. 2023

npj Syst Biol Appl

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show abstract

“…Among these platforms, PhysiCell (Ghaffarizadeh et al, 2018) is an open-source hybrid framework that is able to simulate cells as discrete agents and model the reaction-diffusion dynamics of the substances present in the surrounding microenvironment through a continuous approach. Furthermore, recent add-ons have been developed to introduce new biological processes into the PhysiCell ecosystem (Letort et al, 2018; Bergman et al, 2022; Gonçalves and Garcia-Aznar, 2021).…”

Section: Introductionmentioning

confidence: 99%

PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects

Gonçalves

Hormuth

Prabhakaran

et al. 2022

Preprint

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In silico models of biological systems are usually very complex and rely on a large number of parameters describing physical and biological properties that require validation. As such, exploration of parameter space is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance to model parameters. One widely used computational platform in the mathematical biology community is PhysiCell which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. One limitation of PhysiCell, however, is that there has not been a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Taking this into account, we present PhysiCOOL, an open-source Python library tailored to create standardized calibration and optimization routines of PhysiCell models.

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Building multiscale models with PhysiBoSS, an agent-based modeling tool

Ruscone,

Checcoli,

Heiland

et al. 2024

Briefings in Bioinformatics

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Multiscale models provide a unique tool for analyzing 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. These models 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 construct these models more easily, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as Supplementary Material and all models are provided at https://physiboss.github.io/tutorial/.

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PhysiPKPD: A pharmacokinetics and pharmacodynamics module for PhysiCell

Cited by 4 publications

References 19 publications

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects

Building multiscale models with PhysiBoSS, an agent-based modeling tool

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