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  CellularPotts. jl: simulating multiscale cellular models in Julia

  Gregg, Robert W and Benos, Panayiotis V

  CellularPotts.jl is a software package written in Julia to simulate biological cellular processes such as division, adhesion, and signaling. Accurately modeling and predicting these simple processes is crucial because they facilitate more complex biological phenomena related to important disease states like tumor growth, wound healing, and infection. Here we take advantage of Cellular Potts Modeling to simulate cellular interactions and combine them with differential equations to model dynamic cell signaling patterns. These models are advantageous over other approaches because they retain spatial information about each cell while remaining computationally efficient at larger scales. Users of this package define three key inputs to create valid model definitions: a 2- or 3-dimensional space, a table describing the cells to be positioned in that space, and a list of model penalties that dictate cell behaviors. Models can then be evolved over time to collect statistics, simulated repeatedly to investigate how changing a specific property impacts cellular behavior, and visualized using any of the available plotting libraries in Julia.

  Bioinformatics vol 40 pp btad773 2024

  DOI Link
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  Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study

  Gregg, Robert W and Karoleski, Chad M and Silverman, Edwin K and Sciurba, Frank C and DeMeo, Dawn L and Benos, Panayiotis V

  Identifying which healthy individuals with a history of smoking are likely to progress to chronic obstructive pulmonary disease (COPD) remains challenging. This study aimed to determine clinical, genetic, and radiological predictors of future lung function abnormalities in 2,643 COPDGene participants with normal spirometry at enrollment. Using graph modeling, logistic regression, and random forest techniques, six key predictors were identified: forced expiratory flow in the middle range (FEF25-75%), average lung wall thickness (Pi10), severe emphysema, age, sex, and height. These predictors successfully distinguished individuals who maintained normal lung function from those who experienced decline, with logistic regression and random forest models demonstrating high predictive accuracy (AUROC = 0.75 and 0.7, respectively). External validation on a separate cohort confirmed the models' effectiveness (AUROC = 0.89). The findings suggest that emphysema and airway pathology significantly influence early lung function decline, offering valuable insights for predicting COPD progression in smokers with initially normal spirometry.

  PLoS medicine vol 21 pp e1004444 2024

  DOI Link
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  Early evidence of chronic obstructive pulmonary disease obscured by race-specific prediction equations

  Regan, Elizabeth A and Lowe, Melissa E and Make, Barry J and Curtis, Jeffrey L and Chen, Quan and Crooks, James L and Wilson, Carla and Oates, Gabriela R and Gregg, Robert W and Baldomero, Arianne K and others

  Early identification of chronic obstructive pulmonary disease (COPD) is crucial for effective management and treatment, but race-specific spirometry equations may impact the accuracy of diagnosis. This study analyzed data from the COPDGene cohort to compare COPD classifications between African American (AA) and non-Hispanic White individuals using race-specific, multiethnic, and 'race-reversed' spirometry equations. The results showed that race-specific equations often underestimated disease severity in AA participants, particularly in early stages, leading to potential delays in diagnosis. Conversely, race-reversed equations reclassified a significant number of AA participants to more severe stages, emphasizing the need for more inclusive diagnostic approaches to avoid underestimating disease severity in minority populations.

  American journal of respiratory and critical care medicine vol 209 pp 59-69 2023

  DOI Link
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  Agent-based modeling reveals benefits of heterogeneous and stochastic cell populations during cGAS-mediated IFNβ production

  Gregg, Robert W and Shabnam, Fathima and Shoemaker, Jason E

  The cGAS pathway, crucial for detecting pathogenic DNA and triggering interferon beta (IFNβ) production, exhibits stochastic and heterogeneous behavior in immune responses. This study developed an agent-based model to explore how these attributes influence IFNβ production during DNA transfection and viral infection. Results indicate that cellular heterogeneity enhances IFNβ responses, and a moderate level of stochasticity—where 20–30% of cells respond—is sufficient for effective immune protection. Exceeding this response range does not further improve viral load reduction or cell survival. These findings highlight that both heterogeneity and stochasticity play key roles in optimizing immune responses while minimizing cell death during infections.

  Bioinformatics vol 37 pp 1428-1434 2021

  DOI Link