Speaker
Description
Predicting injury severity and time-to-recovery after ocular chemical exposure is critical for chemical classification and risk assessment. Time-resolved in vitro data from localized injury on reconstructed corneal epithelium reveal concentration-dependent lesion evolution, including secondary propagation likely driven by necrotic cell rupture, yet these assays cannot alone predict tissue-level recovery. To address this gap, we extend V-Cornea, a published multi-scale agent-based model of epithelial homeostasis and recovery from slight to mild injuries \cite{Vanin2025}, with stochastic Boolean networks governing cell-fate decisions between survival, apoptosis, and necrosis \cite{Calzone2010} as functions of local chemical concentration. This extended framework serves as an in silico NAM and engine for in vitro to in vivo extrapolation (IVIVE). Leveraging the model as a computational hypothesis generator, we performed systematic sweeps of concentration and exposure duration, producing phase diagrams that map exposure conditions to outcome regimes ranging from containment to catastrophic spread. Without immune-mediated debris clearance, the damage loop lacks an off-switch, mirroring in vitro observations but diverging from in vivo recovery. This divergence defines the IVIVE boundary and motivates the introduction of immune dynamics to provide the missing recovery signals, enabling extrapolation to in vivo predictions of injury severity and recovery time.
Bibliography
@article{Vanin2025,
author = {Vanin, Joel and Getz, Michael and Mahony, Catherine and Knudsen, Thomas B. and Glazier, James A.},
title = {V-Cornea: A computational model of corneal epithelium homeostasis, injury, and recovery},
journal = {PLOS Computational Biology},
volume = {21},
number = {12},
pages = {e1013410},
year = {2025},
doi = {10.1371/journal.pcbi.1013410}
}
@article{Calzone2010,
author = {Calzone, Laurence and Tournier, Laurent and Fourquet, Simon and others},
title = {Mathematical Modelling of Cell-Fate Decision in Response to Death Receptor Engagement},
journal = {PLOS Computational Biology},
volume = {6},
number = {3},
pages = {e1000702},
year = {2010},
doi = {10.1371/journal.pcbi.1000702}
}
