Biosimulation of acute phonotrauma: an extended model.
OBJECTIVE
Personalized, preemptive, and predictive medicine is a central goal of contemporary medical care. The central aim of the present study was to investigate the utility of mechanistic computational modeling of inflammation and healing to address personalized therapy for patients with acute phonotrauma.


METHODS
Computer simulation.


METHODS
Previously reported agent-based models (ABMs) of acute phonotrauma were extended with additional inflammatory mediators as well as extracellular matrix components. The models were calibrated with empirical data for a panel of biomarkers--interleukin (IL)-1β, IL-6, IL-8, IL-10, tumor necrosis factor-α and matrix metalloproteinase-8--from individual subjects following experimentally induced phonotrauma and a randomly assigned voice treatment namely voice rest, resonant voice exercise, and spontaneous speech. The models' prediction accuracy for biomarker levels was tested for a 24-hour follow-up time point.


RESULTS
The extended ABMs reproduced and predicted trajectories of biomarkers seen in experimental data. The simulation results also agreed qualitatively with various known aspects of inflammation and healing. Model prediction accuracy was generally better following individual-based calibration as compared to population-based calibration. Simulation results also suggested that the special form of vocal fold oscillation in resonant voice may accelerate acute vocal fold healing.


CONCLUSIONS
The calibration of inflammation/healing ABMs with subject-specific data appears to optimize the models' prediction accuracy for individual subjects. This translational application of biosimulation might be used to predict individual healing trajectories, the potential effects of different treatment options, and most importantly, provide new understanding of health and healing in the larynx and possibly in other organs and tissues as well.
Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Nicole Y K
NY
Li
Yoram
Y
Vodovotz
Kevin H
KH
Kim
Qi
Q
Mi
Patricia A
PA
Hebda
Katherine Verdolini
KV
Abbott
D000328:Adult; D015415:Biomarkers / Q000378:metabolism; D001792:Blood Platelets / Q000502:physiology; D003198:Computer Simulation; D004549:Elastin / Q000378:metabolism; D005109:Extracellular Matrix / Q000502:physiology; D005260:Female; D016222:Fibroblast Growth Factor 2 / Q000378:metabolism; D005347:Fibroblasts / Q000502:physiology; D006801:Humans; D006820:Hyaluronic Acid / Q000378:metabolism; D018836:Inflammation Mediators / Q000378:metabolism; D007378:Interleukins / Q000378:metabolism; D007827:Laryngitis / Q000503:physiopathology* / Q000628:therapy*; D008264:Macrophages / Q000502:physiology; D008297:Male; D020784:Matrix Metalloproteinase 8 / Q000378:metabolism; D008875:Middle Aged; D010699:Phonation / Q000502:physiology; D012146:Rest; D013018:Sound Spectrography; D013060:Speech; D014827:Vocal Cords / Q000503:physiopathology*; D014832:Voice Disorders / Q000503:physiopathology* / Q000628:therapy*; D014834:Voice Training; D014945:Wound Healing / Q000502:physiology; D055815:Young Adult
D015415:Biomarkers; D018836:Inflammation Mediators; D007378:Interleukins; D016222:Fibroblast Growth Factor 2; D006820:Hyaluronic Acid; D004549:Elastin; D020784:Matrix Metalloproteinase 8