Abstract
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO’s superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life.
Highlights
The complexity of biological systems is encoded in gene regulatory networks
Interactions between genes are typically represented as a gene regulatory network (GRN) whose nodes correspond to different genes, and a directed edge denotes a direct causal effect of some gene on another gene
BINGO is based on a continuous-time version of the so-called Gaussian process dynamical model (GPDM)
Summary
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. This paper develops a method called BINGO to deal with these issues Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The main focus of this article is on methods that are based on fitting an ordinary differential equation (ODE) model to the observed gene expression time series data. Most ODE-based methods transform the system identification problem into an input–output regression problem where the inputs are the measured gene expression values and outputs are their derivatives that are estimated from the data. A method based on Gaussian process regression, called “Causal structure identification” (CSI)[14,20] was the best performer in a comparison study for network inference from time series data[1]
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