## 1 Introduction  
Voice disorders involve impairments with varying pitch, quality and loudness in daily conversation as well as in occupational voice use such as during voice performance and teaching. Among all voice disorders, vocal fold scarring is considered as one of the most perplexing clinical problems [1; 2; 3]. Current treatment options to fully repair the fibrotic tissue are deemed to be limited [2; 3; 4]. Vocal fold scars can develop within the lamina propria after surgical removal of benign or malignant vocal fold lesions. The scarred tissue alters the microarchitecture and vibratory functions of the vocal folds, resulting in a debilitating condition of the human voice, namely, dysphonia [2; 3; 4; 5; 6; 7; 8]. Iatrogenic vocal fold scarring triggers a cascade of cellular and molecular events associated with inflammation and wound repair, whereas the degree of response varies across patients depending on myriad personal and lifestyle factors. Computer simulation has become an appealing approach to integrate and analyze massive patient data towards more personalized and precise medical treatments. Agent-based computational models (ABM) are often used to simulate complex system dynamics. The basic framework of ABM consists of agents and agent-rules. Agents are decision-making units that interact with other agents and the environment. Agent-rules are formulated to control the action and decision of agents in the virtual world [9]. ABM have been widely applied to ecology, economics, geography, business, political sciences and social sciences [10; 11]. For biomedical applications, ABM are used to understand complex acute and chronic diseases such as diabetes, traumatic brain injury, spinal cord injuries, acute liver failure and sepsis [12; 13; 14; 15; 16]. ABM can be further developed to numerically simulate individual treatment response of a disease as a function of patient profile [17]. In silico experiments allow the exploration of a much wider parameter space and a much longer time scale than what would be very costly or sometimes impossible with animal and human models. In the era of personalized/ precision medicine, computational models have become an indispensable tool to integrate different forms of patient data (e.g., demographics, clinical, lifestyle etc.) and generate specific disease phenotypes or cohorts for in silico clinical trials [18; 19]. Our laboratory has developed vocal fold agent-based models (VF-ABM) to numerically simulate the inflammatory and healing process following a surgical injury [20; 21; 22; 23; 24; 25; 26; 27]. VF-ABM were composed of: (1) cells (platelets, macrophages, neutrophils and fibroblasts), (2) extracellular matrix (ECM) substances (collagen type I, elastin and hyaluronic acid (HA)) and (3) chemical mediators (tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)), interleukin-1\(\beta\) (IL-18), interleukin-10 (IL-10), interleukin-6 (IL-6) interleukin-8 (IL-8), basic fibroblast growth factor (bFGF), and transforming growth factor (TGF-\(\beta\)1)). The model used initial wound size as the inputs to numerically simulate the post-injury trajectories of inflammatory cytokines and ECM markers in surgical vocal fold injury [24]. The dimensions of the VF-ABM correspond to the physiological size of rat vocal folds, since most of the empirical data were available for this animal model [28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. Partial calibration and verification were performed with vocal fold biological data [36; 37; 38; 39; 40]. However as quantitative data on vocal fold cellularity were limited, the cellular data have not been fully calibrated and verified in the current VF-ABM. Model calibration is an important step to ensure sufficient authenticity of model representation to the real world. Model calibration involves systematically altering the values of parameters in the model in an iterative fashion until the simulated outputs and the observable behavior of the system are as close as possible.