Periodontitis is a multifactorial disease that progresses via dynamic interaction between bacterial and host-derived genetic factors. The recent trend of omics analyses has discovered many periodontitis-related risk factors. However, how much the individual factor affects the pathogenesis of periodontitis is still unknown. This article aims to identify multiple key factors related to the pathogenesis of periodontitis and quantitatively predict the influence of each factor on alveolar bone resorption by omics analysis and mathematical modeling. First, we induced periodontitis in mice (n = 3 or 4 at each time point) by tooth ligation. Next, we assessed alveolar bone resorption by micro–computed tomography, alterations in the gene expression by RNA sequencing, and the microbiome of the gingivae by 16S ribosomal RNA sequencing during disease pathogenesis. Omics data analysis identified key players (bacteria and molecules) involved in the pathogenesis of periodontitis. We then constructed a mathematical model of the pathogenesis of periodontitis by employing ordinary differential equations that described the dynamic regulatory interplay between the key players and predicted the alveolar bone integrity as output. Finally, we estimated the model parameters using our dynamic experimental data and validated the model prediction of influence on alveolar bone resorption by in vivo experiments. The model predictions and experimental results revealed that monocyte recruitment induced by bacteria-mediated Toll-like receptor activation was the principal reaction regulating alveolar bone resorption in a periodontitis condition. On the other hand, osteoblast-mediated osteoclast differentiation had less impact on bone integrity in a periodontitis condition.