Abstract Nanopore sequencing offers the ability for real-time analysis of long DNA sequences at a low cost, enabling new applications such as early detection of cancer. Due to the complex nature of nanopore measurements and the high cost of obtaining ground-truth datasets, in-silico nanopore simulators play an important role in this field. Existing simulators rely on hand-crafted rules and parameters and do not learn an internal representation that would allow for analyzing underlying biological factors of interest. In this work, we investigate and extend Variational Autoregressive DNA-conditioned Autoencoder (VADA), a purely data-driven method for simulating nanopore signals based on an autoregressive latent variable model. We show that VADA can effectively model DNA-conditioned probability distributions over nanopore current sequences to produce varying current observations. We show that improving the flexibility of the conditional prior with conditional Real NVP flow significantly improves the simulation quality of VADA, a model that we refer to as VADA Normalizing Flow. We empirically demonstrate that our model achieves competitive simulation performance on experimental nanopore data. Moreover, we show that our model learns an informative latent representation that is predictive of the DNA labels. We hypothesize that other biological factors of interest, beyond DNA labels, can potentially be extracted from such a learned latent representation.

Neural networks are emerging as a tool for scalable data-driven simulation of high-dimensional dynamical systems, especially in settings where numerical methods are infeasible or computationally expensive. Notably, it has been shown that incorporating domain symmetries in deterministic neural simulators can substantially improve their accuracy, sample efficiency, and parameter efficiency. However, to incorporate symmetries in probabilistic neural simulators that can simulate stochastic phenomena, we need a model that produces equivariant distributions over trajectories, rather than equivariant function approximations. In this paper, we propose Equivariant Probabilistic Neural Simulation (EPNS), a framework for autoregressive probabilistic modeling of equivariant distributions over system evolutions. We use EPNS to design models for a stochastic n-body system and stochastic cellular dynamics. Our results show that EPNS considerably outperforms existing neural network-based methods for probabilistic simulation. More specifically, we demonstrate that incorporating equivariance in EPNS improves simulation quality, data efficiency, rollout stability, and uncertainty quantification. We conclude that EPNS is a promising method for efficient and effective data-driven probabilistic simulation in a diverse range of domains.