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

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/.

Keywords: Agent-based modeling; Boolean modeling; Multiscale modeling.

Fig. 1.

Simulation of the cell fate model upon TNF treatment, at t=2,4,8 days. A) MaBoSS simulation of a prolonged and continuous TNF treatment. B) MaBoSS simulation of pulses of TNF treatment.

Fig. 2.

Simulation of the Sizek cell cycle model. A) Wild-type simulation at both time 0 and after 96 simulated hours. B) Knock-in of p110 inactivates the apoptosis pathway which increases the growth rate of the population, with 520 cells after 96 hours vs. 310 cells in Wild Type condition. C) FoxO3 knock-out simulation slows down the cell cycle, diminishing the number of cell divisions, with 22 cells after 48 simulated hours. D) Plk1 knock-out simulation causes the majority of cells to be stuck in G2/M phase. All the simulations were executed with a value of scaling of 37.5 and intracellular_dt of 2.5

Fig. 3.

Simulation of the T cell differentiation model in 2 and 3 dimensions. A) Initial population of T cell (gray), with an endothelial cell (pink) secreting CC21. A population of dendritic cells (blue) is attracted towards the source of CCL21. B) Upon contact, the dendritic cells trigger the receptors of the naive T cell, which start the differentiation process according to the outputs of the intracellular model, into Treg (red), Th1 (yellow), and Th17 (green). C) Simulation of the T cell differentiation with NFkB knock-out, resulting in only Treg. D) Simulation of the T cell differentiation with FOXP3 knock-out, resulting in only Th1 and Th17.