This paper introduces a generative adversarial network (GAN) for low-dose CT (LDCT) simulation, which is an inverse process for network-based low-dose CT denoising. Within our GAN framework, the generator is an encoder-decoder network with a shortcut connection to produce realistic noisy LDCT images. To ensure satisfactory results, a conditional batch normalization layer is incorporated into the bottleneck between the encoder and the decoder. After the model is trained, a Gaussian noise generator serves as the latent variable controlling the noise in generated CT images. With the Mayo Low-dose CT Challenge dataset, the proposed network was trained on image patches, and then produced full-size low-dose CT images of different noise distributions at various noise levels. The network-generated low-dose CT images can be used to test the robustness of the current low-dose CT denoising models and also help perform other imaging tasks such as optimization of radiation dose to patients and evaluation of model observers.