An agent-based modeling approach for lung fibrosis in response to COVID-19

Mathematical Modeling of Impacts of Patient Differences on Renin-Angiotensin System and Applications to COVID-19 Lung Fibrosis Outcomes

Patient-specific premorbidity, age, and sex are significant heterogeneous factors that influence the severe manifestation of lung diseases, including COVID-19 fibrosis. The renin-angiotensin system (RAS) plays a prominent role in regulating the effects of these factors. Recent evidence shows patient-specific alterations of RAS homeostasis concentrations with premorbidity and the expression level of angiotensin-converting enzyme 2 (ACE2) during COVID-19. However, conflicting evidence suggests decreases, increases, or no changes in RAS peptides after SARS-CoV-2 infection. In addition, detailed mechanisms connecting the patient-specific conditions before infection to infection-induced RAS alterations are still unknown. Here, a multiscale computational model was developed to quantify the systemic contribution of heterogeneous factors of RAS during COVID-19. Three submodels were connected-an agent-based model for in-host COVID-19 response in the lung tissue, a RAS dynamics model, and a fibrosis dynamics model to investigate the effects of patient-group-specific factors in the systemic alteration of RAS and collagen deposition in the lung. The model results indicated cell death due to inflammatory response as a major contributor to the reduction of ACE and ACE2. In contrast, there were no significant changes in ACE2 dynamics due to viral-bound internalization of ACE2. The model explained possible mechanisms for conflicting evidence of patient-group-specific changes in RAS peptides in previously published studies. Simulated results were consistent with reported RAS peptide values for SARS-CoV-2-negative and SARS-CoV-2-positive patients. RAS peptides decreased for all virtual patient groups with aging in both sexes. In contrast, large variations in the magnitude of reduction were observed between male and female virtual patients in the older and middle-aged groups. The patient-specific variations in homeostasis RAS peptide concentrations of SARS-CoV-2-negative patients also affected the dynamics of RAS during infection. The model results also showed that feedback between RAS signaling and renin dynamics could restore ANGI homeostasis concentration but failed to restore homeostasis values of RAS peptides downstream of ANGI. In addition, the results showed that ACE2 variations with age and sex significantly altered the concentrations of RAS peptides and led to collagen deposition with slight variations depending on age and sex. This model may find further applications in patient-specific calibrations of tissue models for acute and chronic lung diseases to develop personalized treatments.

An in silico modeling approach to understanding the dynamics of the post-burn immune response

Introduction

Burns are characterized by a massive and prolonged acute inflammation, which persists for up to months after the initial trauma. Due to the complexity of the inflammatory process, Predicting the dynamics of wound healing process can be challenging for burn injuries. The aim of this study was to develop simulation models for the post-burn immune response based on (pre)clinical data.

Methods

The simulation domain was separated into blood and tissue compartments. Each of these compartments contained solutes and cell agents. Solutes comprise pro-inflammatory cytokines, anti-inflammatory cytokines and inflammation triggering factors. The solutes diffuse around the domain based on their concentration profiles. The cells include mast cells, neutrophils, and macrophages, and were modeled as independent agents. The cells are motile and exhibit chemotaxis based on concentrations gradients of the solutes. In addition, the cells secrete various solutes that in turn alter the dynamics and responses of the burn wound system.

Results

We developed an Glazier-Graner-Hogeweg method-based model (GGH) to capture the complexities associated with the dynamics of inflammation after burn injuries, including changes in cell counts and cytokine levels. Through simulations from day 0 - 4 post-burn, we successfully identified key factors influencing the acute inflammatory response, i.e., the initial number of endothelial cells, the chemotaxis threshold, and the level of chemoattractants.

Conclusion

Our findings highlight the pivotal role of the initial endothelial cell count as a key parameter for intensity of inflammation and progression of acute inflammation, 0 - 4 days post-burn.

Mmp2 Deficiency Leads to Defective Parturition and High Dystocia Rates in Mice

Parturition is the final and essential step for mammalian reproduction. While the uterus is quiescent during pregnancy, fundamental changes arise in the myometrial contractility, inducing fetal expulsion. Extracellular matrix (ECM) remodeling is fundamental for these events. The gelatinases subgroup of matrix metalloproteinases (MMPs), MMP2 and MMP9, participate in uterine ECM remodeling throughout pregnancy and parturition. However, their loss-of-function effect is unknown. Here, we determined the result of eliminating Mmp2 and/or Mmp9 on parturition in vivo, using single- and double-knockout (dKO) mice. The dystocia rates were measured in each genotype, and uterine tissue was collected from nulliparous synchronized females at the ages of 2, 4, 9 and 12 months. Very high percentages of dystocia (40-55%) were found in the Mmp2-/- and dKO females, contrary to the Mmp9-/- and wild-type females. The histological analysis of the uterus and cervix revealed that Mmp2-/- tissues undergo marked structural alterations, including highly enlarged myometrial, endometrial and luminal cavity. Increased collagen deposition was also demonstrated, suggesting a mechanism of extensive fibrosis in the Mmp2-/- myometrium, which may result in dystocia. Overall, this study describes a new role for MMP2 in myometrium remodeling during mammalian parturition process, highlighting a novel cause for dystocia due to a loss in MMP2 activity in the uterine tissue.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.