# A 3D Agent-Based Model of Lung Fibrosis
###### Abstract
The population of mesenchymal cells can be further expanded through the epithelial-mesenchymal transition (EMT), a process by which activated AEC2 differentiate into fibroblasts and/or myofibals [32]. The overall effect is an increase in the number of ECM-secreting cells that leads to the abnormal accumulation of the ECM. Figure 1: Schematic representation of the main cells, extracellular mediators, and pathways in lung fibrosis. AEC1 = alveolar epithelial cells type 1, AEC2 = alveolar epithelial cells type 2, AEC2\({}_{\text{d}}\) = damaged AEC2, AEC2\({}_{\text{ap}}\) = apoptotic AEC2, AEC2\({}_{\text{ac}}\) = activated AEC2, F = fibroblast, MF = myofiboblast, M1 = type 1 macrophage, M2 = type 2 macrophage, M0 = monocyte, MMP = matrix metalloproteinases, ECM = extracellular matrix, TIMP = tissue inhibitors of metalloproteinases, IL13 = interleukin 13, TGF\(\beta_{\text{i}}\) = inactive form of the transforming growth factor beta, TGF\(\beta_{\text{a}}\) = active form of the transforming growth factor beta, FGF2 = basic fibroblast growth factor, TNF\(\alpha\) = tumor necrosis factor, PDGF = platelet-derived growth factor, MCP1 = monocyte chemoattractant protein 1. Although the chain of events presented in Figure 1 above highlights a causal link between the lung damage and the fibrosis onset, it only unfolds the backbone of a disease that is far from being fully understood [3]. The motivation to elucidate this link stems from multiple factors, including the lack of animal models that thoroughly reflect the human complexity and the time needed for the fibrosis to fully develop [33]. In light of these considerations, in-silico or computational models represent invaluable tools towards a better understanding of complex biological (among others) systems [34] and hold the potential for several clinical applications. In fact, at present, high-performance computers allow us to simulate experiments at a fast pace, with moderate costs and without the need for laboratories. In-silico modeling is nowadays applied in clinically relevant research, whose applications include, but are not limited to, drug design, clinical decision making, treatment optimization, and personalized medicine [35]. Computational models lay the foundations for personalized medicine and drug development: the parameters of a validated disease model can be calibrated to reflect patients-specific conditions and the simulation outputs employed to elaborate tailored therapies. Moreover, computational models are powerful tools for addressing a various number of diseases on multiple scales, ranging from the subcellular to the global, as recently shown for COVID-19 [36; 37]. The use of the aforementioned models in biomedical frameworks has a well-defined pattern: the theoretical prototype is originally built on the basis of available in-vitro and/or in-vivo data and implemented in-silico; the model is then used to simulate existing data and altered until a good match between in-silico and laboratory data is observed; finally, in-silico models are used to predict new data for clinical studies [34]. Equation-based modeling and agent-based modeling are common and powerful approaches when it comes to implementing computational models, with the choice being mainly affected by the spatiotemporal scales of the simulated phenomenon [38]. On the one hand, equation-based models (EBMs) assume homogenous populations and consist of sets of ordinary or partial differential equations (ODEs or PDEs) that are evaluated at every time step and location [39; 40]. On the other hand, heterogeneities among entities of the same species (i.e., randomness) in the simulation space can be recapitulated by agent-based models (ABM) in which interacting individual agents behave according to a set of rules [41; 42].