# A 3D Agent-Based Model of Lung Fibrosis
###### Abstract  
Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease). a 20221202202202202202202202202202202202202202202202202202202202202220222022202220222022220222222222222222222222222222222222222222222222222222222222  
progenitor capability (thus leading to AECI depletion) and release proinflammatory and profibrotic factors through the acquisition of a senescence-associated secretory phenotype (SASP) [3; 10]. The deterioration of the epithelial membrane, along with the release of cytokines and chemokines, such as the tumor necrosis factor, TNF\(\alpha\), and the monocyte chemoattractant protein 1, MCP1 or CCL2, activates the Th1 immune response and allows monocyte-derived macrophages (M0, which later differentiate into classically activated or type I macrophages, M1) to breach into the alveolus [3; 5]. M1 macrophages can then differentiate into their alternatively activated form, M2, and represent an additional source of TNF\(\alpha\), which is able to reverse the transition [12]. M2 macrophages, in turn, secrete a plethora of mediators (including the active form of the transforming growth factor beta, TGF\(\beta_{\text{a}}\)[13; 14], platelet-derived growth factor, PDGF [15], interleukin 13, IL13 [16], matrix metalloproteinases, MMP [17], and tissue inhibitors of metalloproteinases, TIMPs [17] which affect several cellular species. Among these, fibroblasts are mesenchymal cells that secrete inactive TGF\(\beta\) (TGF\(\beta_{\text{i}}\)) [18], the ECM [19], and are able to differentiate into myofibals. The ECM is degraded by the MMP, whose effect is inhibited by TIMP [20]. TGF\(\beta\) (which can be activated by damaged AEC2 [21] and whose secretion by M2 is amplified by IL13 [22]), in its active form, increases the proliferation of fibroblasts [23], stimulates their differentiation into myofibals [24], and damages healthy AEC2 cells [25]. IL13 further enhances the proliferation of fibroblasts [26] together with the basic fibroblast growth factor (bFGF or FGF2) secreted by the AEC2 [27; 28], while PDGF triggers the differentiation of the fibroblasts [29]. Myofibroblasts deposit additional ECM [30] (a mechanism intensified by TGF\(\beta\)[31]) and have a pivotal role in the remodeling process that takes place in the alveolar region.