Topographic distribution of idiopathic pulmonary fibrosis: a hybrid physics- and agent-based model

Elucidating the interaction between stretch and stiffness using an agent-based spring network model of progressive pulmonary fibrosis

Pulmonary fibrosis is a deadly disease that involves the dysregulation of fibroblasts and myofibroblasts, which are mechanosensitive. Previous computational models have succeeded in modeling stiffness-mediated fibroblasts behaviors; however, these models have neglected to consider stretch-mediated behaviors, especially stretch-sensitive channels and the stretch-mediated release of latent TGF-β. Here, we develop and explore an agent-based model and spring network model hybrid that is capable of recapitulating both stiffness and stretch. Using the model, we evaluate the role of mechanical signaling in homeostasis and disease progression during self-healing and fibrosis, respectively. We develop the model such that there is a fibrotic threshold near which the network tends towards instability and fibrosis or below which the network tends to heal. The healing response is due to the stretch signal, whereas the fibrotic response occurs when the stiffness signal overpowers the stretch signal, creating a positive feedback loop. We also find that by changing the proportional weights of the stretch and stiffness signals, we observe heterogeneity in pathological network structure similar to that seen in human IPF tissue. The system also shows emergent behavior and bifurcations: whether the network will heal or turn fibrotic depends on the initial network organization of the damage, clearly demonstrating structure's pivotal role in healing or fibrosis of the overall network. In summary, these results strongly suggest that the mechanical signaling present in the lungs combined with network effects contribute to both homeostasis and disease progression.

An agent-based model of tissue maintenance and self-repair

We hypothesized that a system that possesses the capacity for ongoing maintenance of its tissues will necessarily also have the capacity to self-heal following a perturbation. We used an agent-based model of tissue maintenance to investigate this idea, and in particular to determine the extent to which the current state of the tissue must influence cell behavior in order for tissue maintenance and self-healing to be stable. We show that a mean level of tissue density is robustly maintained when catabolic agents digest tissue at a rate proportional to local tissue density, but that the spatial heterogeneity of the tissue at homeostasis increases with the rate at which tissue is digested. The rate of self-healing is also increased by increasing either the amount of tissue removed or deposited at each time step by catabolic or anabolic agents, respectively, and by increasing the density of both agent types on the tissue. We also found that tissue maintenance and self-healing are stable with an alternate rule in which cells move preferentially to tissue regions of low density. The most basic form of self-healing can thus be achieved with cells that follow very simple rules of behavior, provided these rules are based in some way on the current state of the local tissue. Straightforward mechanisms can accelerate the rate of self-healing, as might be beneficial to the organism.

Predicting alveolar ventilation heterogeneity in pulmonary fibrosis using a non-uniform polyhedral spring network model

Pulmonary Fibrosis (PF) is a deadly disease that has limited treatment options and is caused by excessive deposition and cross-linking of collagen leading to stiffening of the lung parenchyma. The link between lung structure and function in PF remains poorly understood, although its spatially heterogeneous nature has important implications for alveolar ventilation. Computational models of lung parenchyma utilize uniform arrays of space-filling shapes to represent individual alveoli, but have inherent anisotropy, whereas actual lung tissue is isotropic on average. We developed a novel Voronoi-based 3D spring network model of the lung parenchyma, the Amorphous Network, that exhibits more 2D and 3D similarity to lung geometry than regular polyhedral networks. In contrast to regular networks that show anisotropic force transmission, the structural randomness in the Amorphous Network dissipates this anisotropy with important implications for mechanotransduction. We then added agents to the network that were allowed to carry out a random walk to mimic the migratory behavior of fibroblasts. To model progressive fibrosis, agents were moved around the network and increased the stiffness of springs along their path. Agents migrated at various path lengths until a certain percentage of the network was stiffened. Alveolar ventilation heterogeneity increased with both percent of the network stiffened, and walk length of the agents, until the percolation threshold was reached. The bulk modulus of the network also increased with both percent of network stiffened and path length. This model thus represents a step forward in the creation of physiologically accurate computational models of lung tissue disease.

Cell-Specific Response of NSIP- and IPF-Derived Fibroblasts to the Modification of the Elasticity, Biological Properties, and 3D Architecture of the Substrate

The fibrotic fibroblasts derived from idiopathic pulmonary fibrosis (IPF) and nonspecific interstitial pneumonia (NSIP) are surrounded by specific environments, characterized by increased stiffness, aberrant extracellular matrix (ECM) composition, and altered lung architecture. The presented research was aimed at investigating the effect of biological, physical, and topographical modification of the substrate on the properties of IPF- and NSIP-derived fibroblasts, and searching for the parameters enabling their identification. Soft and stiff polydimethylsiloxane (PDMS) was chosen for the basic substrates, the properties of which were subsequently tuned. To obtain the biological modification of the substrates, they were covered with ECM proteins, laminin, fibronectin, and collagen. The substrates that mimicked the 3D structure of the lungs were prepared using two approaches, resulting in porous structures that resemble natural lung architecture and honeycomb patterns, typical of IPF tissue. The growth of cells on soft and stiff PDMS covered with proteins, traced using fluorescence microscopy, confirmed an altered behavior of healthy and IPF- and NSIP-derived fibroblasts in response to the modified substrate properties, enabling their identification. In turn, differences in the mechanical properties of healthy and fibrotic fibroblasts, determined using atomic force microscopy working in force spectroscopy mode, as well as their growth on 3D-patterned substrates were not sufficient to discriminate between cell lines.

ECM Mechanoregulation in Malignant Pleural Mesothelioma

Malignant pleural mesothelioma is a relatively rare, but devastating tumor, because of the difficulties in providing early diagnosis and effective treatments with conventional chemo- and radiotherapies. Patients usually present pleural effusions that can be used for diagnostic purposes by cytological analysis. This effusion cytology may take weeks or months to establish and has a limited sensitivity (30%-60%). Then, it is becoming increasingly urgent to develop alternative investigative methods to support the diagnosis of mesothelioma at an early stage when this cancer can be treated successfully. To this purpose, mechanobiology provides novel perspectives into the study of tumor onset and progression and new diagnostic tools for the mechanical characterization of tumor tissues. Here, we report a mechanical and biophysical characterization of malignant pleural mesothelioma cells as additional support to the diagnosis of pleural effusions. In particular, we examined a normal mesothelial cell line (Met5A) and two epithelioid mesothelioma cell lines (REN and MPP89), investigating how malignant transformation can influence cellular function like proliferation, cell migration, and cell spreading area with respect to the normal ones. These alterations also correlated with variations in cytoskeletal mechanical properties that, in turn, were measured on substrates mimicking the stiffness of patho-physiological ECM.

A 3D Agent-Based Model of Lung Fibrosis

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).

Pathologic Proteolytic Processing of N-Cadherin as a Marker of Human Fibrotic Disease

Prior research has implicated the involvement of cell adhesion molecule N-cadherin in tissue fibrosis and remodeling. We hypothesize that anomalies in N-cadherin protein processing are involved in pathological fibrosis. Diseased tissues associated with fibrosis of the heart, lung, and liver were probed for the precursor form of N-cadherin, pro-N-cadherin (PNC), by immunohistochemistry and compared to healthy tissues. Myofibroblast cell lines were analyzed for cell surface pro-N-cadherin by flow cytometry and immunofluorescent microscopy. Soluble PNC products were immunoprecipitated from patient plasmas and an enzyme-linked immunoassay was developed for quantification. All fibrotic tissues examined show aberrant PNC localization. Cell surface PNC is expressed in myofibroblast cell lines isolated from cardiomyopathy and idiopathic pulmonary fibrosis but not on myofibroblasts isolated from healthy tissues. PNC is elevated in the plasma of patients with cardiomyopathy (p ≤ 0.0001), idiopathic pulmonary fibrosis (p ≤ 0.05), and nonalcoholic fatty liver disease with cirrhosis (p ≤ 0.05). Finally, we have humanized a murine antibody and demonstrate that it significantly inhibits migration of PNC expressing myofibroblasts. Collectively, the aberrant localization of PNC is observed in all fibrotic tissues examined in our study and our data suggest a role for cell surface PNC in the pathogenesis of fibrosis.

A Personalized Spring Network Representation of Emphysematous Lungs From CT Images

Emphysema is a progressive disease characterized by irreversible tissue destruction and airspace enlargement, which manifest as low attenuation area (LAA) on CT images. Previous studies have shown that inflammation, protease imbalance, extracellular matrix remodeling and mechanical forces collectively influence the progression of emphysema. Elastic spring network models incorporating force-based mechanical failure have been applied to investigate the pathogenesis and progression of emphysema. However, these models were general without considering the patient-specific information on lung structure available in CT images. The aim of this work was to develop a novel approach that provides an optimal spring network representation of emphysematous lungs based on the apparent density in CT images, allowing the construction of personalized networks. The proposed method takes into account the size and curvature of LAA clusters on the CT images that correspond to a pre-stressed condition of the lung as opposed to a naïve method that excludes the effects of pre-stress. The main findings of this study are that networks constructed by the new method 1) better preserve LAA cluster sizes and their distribution than the naïve method; and 2) predict different course of emphysema progression compared to the naïve method. We conclude that our new method has the potential to predict patient-specific emphysema progression which needs verification using clinical data.

Mechanical communication in fibrosis progression

Mechanical hallmarks of fibrotic microenvironments are both outcomes and causes of fibrosis progression. Understanding how cells sense and transmit mechanical cues in the interplay with extracellular matrix (ECM) and hemodynamic forces is a significant challenge. Recent advances highlight the evolvement of intracellular mechanotransduction pathways responding to ECM remodeling and abnormal hemodynamics (i.e., low and disturbed shear stress, pathological stretch, and increased pressure), which are prevalent biomechanical characteristics of fibrosis in multiple organs (e.g., liver, lung, and heart). Here, we envisage the mechanical communication in cell-ECM, cell-hemodynamics and cell-ECM-cell crosstalk (namely paratensile signaling) during fibrosis expansion. We also provide a comprehensive overview of in vitro and in silico engineering systems for disease modeling that will aid the identification and prediction of mechano-based therapeutic targets to ameliorate fibrosis progression.

Percolation of collagen stress in a random network model of the alveolar wall

Fibrotic diseases are characterized by progressive and often irreversible scarring of connective tissue in various organs, leading to substantial changes in tissue mechanics largely as a result of alterations in collagen structure. This is particularly important in the lung because its bulk modulus is so critical to the volume changes that take place during breathing. Nevertheless, it remains unclear how fibrotic abnormalities in the mechanical properties of pulmonary connective tissue can be linked to the stiffening of its individual collagen fibers. To address this question, we developed a network model of randomly oriented collagen and elastin fibers to represent pulmonary alveolar wall tissue. We show that the stress–strain behavior of this model arises via the interactions of collagen and elastin fiber networks and is critically dependent on the relative fiber stiffnesses of the individual collagen and elastin fibers themselves. We also show that the progression from linear to nonlinear stress–strain behavior of the model is associated with the percolation of stress across the collagen fiber network, but that the location of the percolation threshold is influenced by the waviness of collagen fibers.

An Analytic Model of Tissue Self-Healing and Its Network Implementation: Application to Fibrosis and Aging

Here we present a model capable of self-healing and explore its ability to resolve pathological alterations in biological tissue. We derive a simple analytic model consisting of an agent representing a cell that exhibits anabolic or catabolic activity, and which interacts with its tissue substrate according to tissue stiffness. When perturbed, this system returns toward a stable fixed point, a process corresponding to self-healing. We implemented this agent-substrate mechanism numerically on a hexagonal elastic network representing biological tissue. Agents, representing fibroblasts, were placed on the network and allowed to migrate around while they remodeled the network elements according to their activity which was determined by the stiffnesses of network elements that each agent encountered during its random walk. Initial injury to the network was simulated by increasing the stiffness of a single central network element above baseline. This system also exhibits a fixed point represented by the uniform baseline state. During the approach to the fixed point, interactions between the agents and the network create a transient spatially extended halo of stiffer network elements around the site of initial injury, which aids in overall injury repair. Non-equilibrium constraints generated by persistent injury prohibit the network to return to baseline and results in progressive stiffening, mimicking the development of fibrosis. Additionally, reducing anabolic or catabolic rates delay self-healing, reminiscent of aging. Our model thus embodies what may be the simplest set of attributes required of a spatiotemporal self-healing system, and so may help understand altered self-healing in chronic fibrotic diseases and aging.

Multi-scale models of lung fibrosis

The architectural complexity of the lung is crucial to its ability to function as an organ of gas exchange; the branching tree structure of the airways transforms the tracheal cross-section of only a few square centimeters to a blood-gas barrier with a surface area of tens of square meters and a thickness on the order of a micron or less. Connective tissue comprised largely of collagen and elastic fibers provides structural integrity for this intricate and delicate system. Homeostatic maintenance of this connective tissue, via a balance between catabolic and anabolic enzyme-driven processes, is crucial to life. Accordingly, when homeostasis is disrupted by the excessive production of connective tissue, lung function deteriorates rapidly with grave consequences leading to chronic lung conditions such as pulmonary fibrosis. Understanding how pulmonary fibrosis develops and alters the link between lung structure and function is crucial for diagnosis, prognosis, and therapy. Further information gained could help elaborate how the healing process breaks down leading to chronic disease. Our understanding of fibrotic disease is greatly aided by the intersection of wet lab studies and mathematical and computational modeling. In the present review we will discuss how multi-scale modeling has facilitated our understanding of pulmonary fibrotic disease as well as identified opportunities that remain open and have produced techniques that can be incorporated into this field by borrowing approaches from multi-scale models of fibrosis beyond the lung.

Agent-based models of inflammation in translational systems biology: A decade later

Agent-based modeling is a rule-based, discrete-event, and spatially explicit computational modeling method that employs computational objects that instantiate the rules and interactions among the individual components ("agents") of system. Agent-based modeling is well suited to translating into a computational model the knowledge generated from basic science research, particularly with respect to translating across scales the mechanisms of cellular behavior into aggregated cell population dynamics manifesting at the tissue and organ level. This capacity has made agent-based modeling an integral method in translational systems biology (TSB), an approach that uses multiscale dynamic computational modeling to explicitly represent disease processes in a clinically relevant fashion. The initial work in the early 2000s using agent-based models (ABMs) in TSB focused on examining acute inflammation and its intersection with wound healing; the decade since has seen vast growth in both the application of agent-based modeling to a wide array of disease processes as well as methodological advancements in the use and analysis of ABM. This report presents an update on an earlier review of ABMs in TSB and presents examples of exciting progress in the modeling of various organs and diseases that involve inflammation. This review also describes developments that integrate the use of ABMs with cutting-edge technologies such as high-performance computing, machine learning, and artificial intelligence, with a view toward the future integration of these methodologies. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Models of Systems Properties and Processes > Organismal Models.

Influence of Pathogens and Mechanical Stimuli in Inflammation

Inflammation is a process driven by underlying cell-cell communication and many other factors. In this study, a model of cell-cell communications was proposed to study factors driving the inflammation time course. Analyses of inflammations that are driven by the combined effects of strain (mechanical stimuli) and/or pathogens are considered in this paper. An agent-based model was employed to simulate inflammation where macrophages and fibroblasts influence each other through cell signaling cytokines that diffuse and spread in the tissue space. The communication network of macrophages and fibroblasts was then inferred and its network model (termed TE network) was generated and analyzed. The results suggest that factors driving inflammation time course can be discriminated by the characteristics of TE networks. Inflammation driven only by pathogens has certain TE network characteristics indicating slower and lower information exchange among cells. Multiple stimuli can help to maintain sufficient information exchange among cells, which is beneficial for inflammation resolution. The TE network captures the unfolding of the innate immune system over time, and the history of pathogens invasion. The resulting network leads to an improved understanding of the resilience of the system to future pathogen invasion.

Mesothelial to mesenchyme transition as a major developmental and pathological player in trunk organs and their cavities

The internal organs embedded in the cavities are lined by an epithelial monolayer termed the mesothelium. The mesothelium is increasingly implicated in driving various internal organ pathologies, as many of the normal embryonic developmental pathways acting in mesothelial cells, such as those regulating epithelial-to-mesenchymal transition, also drive disease progression in adult life. Here, we summarize observations from different animal models and organ systems that collectively point toward a central role of epithelial-to-mesenchymal transition in driving tissue fibrosis, acute scarring, and cancer metastasis. Thus, drugs targeting pathways of mesothelium’s transition may have broad therapeutic benefits in patients suffering from these diseases.

Tim Koopmans and Yuval Rinkevich review recent findings linking the mesothelium’s embryonic programs that drive epithelial-to-mesenchyme transition with adult pathologies, such as fibrosis, acute scarring, and cancer metastasis. They highlight new avenues for drug development that would target pathways of the mesothelium’s mesenchymal transition.