After myocardial infarction (MI), cardiac cells work together to regulate wound healing of the infarct. The pathological response to MI yields cardiac remodelling comprising inflammatory and fibrosis phases, and the interplay of cellular dynamics that underlies these phases has not been elucidated. This study developed a computational model to identify cytokine and cellular dynamics post‐MI to predict mechanisms driving post‐MI inflammation, resolution of inflammation, and scar formation. Additionally, this study evaluated the interdependence between inflammation and fibrosis. Our model bypassed limitations of in vivo approaches in achieving cellular specificity and performing specific perturbations such as global knockouts of chemical factors. The model predicted that inflammation is a graded response to initial infarct size that is amplified by a positive feedback loop between neutrophils and interleukin 1β (IL‐1β). Resolution of inflammation was driven by degradation of IL‐1β, matrix metalloproteinase 9, and transforming growth factor β (TGF‐β), as well as apoptosis of neutrophils. Inflammation regulated TGFβ secretion directly through immune cell recruitment and indirectly through upregulation of macrophage phagocytosis. Lastly, we found that mature collagen deposition was an ultrasensitive switch in response to inflammation, which was amplified primarily by cardiac fibroblast proliferation. These findings describe the relationship between inflammation and fibrosis and highlight how the two responses work together post‐MI. This model revealed that post‐MI inflammation and fibrosis are dynamically coupled, which provides rationale for designing novel anti‐inflammatory, pro‐resolving or anti‐fibrotic therapies that may improve the response to MI. Key points Inflammation and matrix remodelling are two processes involved in wound healing after a heart attack. Cardiac cells work together to facilitate these processes; this is done by secreting cytokines that then regulate the cells themselves or other cells surrounding them. This study developed a computational model of the dynamics of cardiac cells and cytokines to predict mechanisms through which inflammation and matrix remodelling is regulated. We show the roles of various cytokines and signalling motifs in driving inflammation, resolution of inflammation and fibrosis. The novel concept of inflammation–fibrosis coupling, based on the model prediction that inflammation and fibrosis are dynamically coupled, provides rationale for future studies and for designing therapeutics to improve the response after a heart attack.

No abstract

Pharmacokinetics and pharmacodynamics (PKPD) are key considerations in any study of molecular therapies. It is thus imperative to factor their effects into any in silico model of biological tissue involving such therapies. Furthermore, creating a standardized and flexible framework will benefit the community by increasing access to such modules and enhancing their communicability. PhysiCell is an open-source physics-based cell simulator, i.e., a platform for modeling biological tissue, that is quickly being adopted and utilized by the mathematical biology community. We present here PhysiPKPD, an open-source PhysiCell-based package that allows users to include PKPD in PhysiCell models. Availability & Implementation The source code for PhysiPKPD is located here: https://github.com/drbergman/PhysiPKPD.

Pharmacokinetics and pharmacodynamics are key considerations in any study of molecular therapies. It is thus imperative to factor their effects in to any in silico model of biological tissue involving such therapies. Furthermore, creation of a standardized and flexible framework will benefit the community by increasing access to such modules and enhancing their communicability. PhysiCell is an open source physics-based cell simulator, i.e. a platform for modeling biological tissue, that is quickly being adopted and utilized by the mathematical biology community. We present here PhysiPKPD, an open source PhysiCell-based package that allows users to include PKPD in PhysiCell models.

Introduction: Post-myocardial infarction (MI), cardiac fibroblasts and macrophages work together to regulate tissue homeostasis and infarct repair. Macrophage-fibroblast interactions in healthy tissue are stable and resistant to perturbations. However, this robustness post-MI has not been assessed. This study designs and implements an intercellular communication model of macrophage-fibroblast crosstalk to determine drivers of infarct repair. Methods: An ordinary differential equation model of post-MI cellular dynamics was developed ( Figure 1A ). Model inputs are time courses of cardiomyocytes, neutrophils, and monocytes. These cells, along with simulated macrophages and fibroblasts, secrete chemokines and cytokines which directed cell proliferation, removal, and chemical secretion. The outputs are macrophage and fibroblast densities, secreted factor dynamics, and produced collagen. Model validation was done using published data in post-MI mice. A sensitivity analysis was conducted by knocking down individual parameters to identify key drivers of fibroblast collagen production. Results: The simulated trends matched the validation time courses. Of the 28 validation relationships, 12 were input-output, 7 were knockouts, and 9 were inhibitor relationships. The validation passed at 78.5%; the 6 failed validations were due to the independent nature of the input curves. Sensitivity analysis ( Figure 1B ) identified macrophage differentiation rate, removal rate, and transforming growth factor-beta (TGFB) secretion rate as pro-fibrotic. Prolonged exposure to TGFB and granulocyte-macrophage colony stimulating factor was anti-fibrotic. Several clustered parameters differentially regulated macrophages and collagen production. Conclusions: The multicellular model identified macrophage density as a pro-fibrotic driver in the healing infarct. Differential regulation of macrophages and collagen production was predicted by the model.