在系统生物学中，数学模型和模拟在理解复杂的生物系统中发挥着至关重要的作用。根据所研究系统的性质和规模，采用不同的建模框架。例如，可以使用布尔建模来模拟信号传导和调节网络，而可以使用基于代理的建模来研究多细胞系统。在此，我们提出了 PhysiBoSS 2.0，这是一种基于混合代理的建模框架，允许模拟单个细胞代理内的信号传导和调控网络。 PhysiBoSS 2.0 是 PhysiBoSS 1.0 的重新设计和重新实现，被认为是一个附加组件，通过使用 MaBoSS 模拟细胞内细胞信号传导，同时保持解耦、可维护和模型无关的设计，从而扩展 PhysiCell 功能。 PhysiBoSS 2.0 还扩展了为用户提供的功能集，包括定制模型和电池规格、基质内化的机械子模型以及对模拟参数的详细控制。与 PhysiBoSS 2.0 一起，我们引入了 PCTK，这是​​一个为处理和处理模拟输出以及生成摘要图和 3D 渲染而开发的 Python 包。 PhysiBoSS 2.0 允许研究微环境、控制细胞过程和群体动态的信号通路之间的相互作用，适用于癌症建模。我们展示了将布尔网络集成到多尺度模拟中的不同方法，使用策略来研究癌细胞系模型中的药物作用和协同作用，并使用实验数据对其进行验证。 PhysiBoSS 2.0 是开源的，可在 GitHub 上公开获取，并附带多个可互操作工具的存储库。© 2023。作者。

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.© 2023. The Author(s).