Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks

Abstract

Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady-state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady-state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver.

Graphical continuous Lyapunov models

“…In the preparation of this paper we found that similar ideas were recently considered by Young et al (2019) and Fitch (2019). The work by Fitch (2019) is based on (2) and a learning algorithm was proposed, while Young et al (2019) considered the vector autoregressive model, whose equilibrium covariance matrix solves the discrete Lyapunov equation.…”