The growth of Salmonella in food is a major threat to public health. The objective of this study was to develop growth prediction models of Salmonella in tuna sashimi under static and dynamic conditions. The growth of Salmonella in tuna sashimi was investigated under constant temperature conditions （8-35 ℃） to evaluate the effect of temperature on growth rates and lag times. Duplicated experiments were conducted， and the data set from one replicate was used to directly construct both primary model （Huang model and Baranyi model） and secondary model （Huang Square-Root model， HSR） through one-step kinetic analysis. The Fourth order Runge-Kutta method and nonlinear least square method were combined to searching the parameters of the models. The data set from the other replicate under constant temperatures and newly designed dynamic temperature conditions were chosen for model validation. The results showed that one-step approach can be used to analyze the growth curves of Salmonella in tuna sashimi. Though both Huang-HSR models and Baranyi-HSR models had an equal goodness of fit， the former was the recommended model in this study， because of the explicit form of definition for the lag phase in Huang model. The minimum growth temperature of Salmonella was 6.91 ℃， and the maximum cell density of Salmonella was 9.15 lg（CFU/g）. The root-mean-square errors （RMSE） of validation at isothermal condition and dynamic condition were only 0.37 lg（CFU/g） and 0.44 lg（CFU/g）， with the residual errors of predictions following Normal distribution and Laplace distribution， respectively. This study showed that one-step kinetic analysis is a useful and efficient method to directly construct primary and secondary growth models. The models developed in this study can be used to predict the growth of Salmonella in tuna sashimi and conduct risk assessment.