Employing decomposable partially observable Markov decision processes to control gene regulatory networks

A framework to shift basins of attraction of gene regulatory networks through batch reinforcement learning

A major challenge in gene regulatory networks (GRN) of biological systems is to discover when and what interventions should be applied to shift them to healthy phenotypes. A set of gene activity profiles, called basin of attraction (BOA), takes this network to a specific phenotype; therefore, a healthy BOA leads the GRN to a healthy phenotype. However, without the complete observability of the genes, it is not possible to identify whether the current BOA is healthy. In this article we investigate external interventions in GRN with partial observability aiming to bring it to healthy BOAs. We propose a new batch reinforcement learning method (BRL), called mSFQI, to define intervention strategies based on the probabilities of the gene activity profiles being in healthy BOAs, which are calculated from a set of previous observed experiences. BRL uses approximation functions and repeated applications of previous experiences to accelerate learning. Results demonstrate that our proposal can quickly shift a partially observable GRN to healthy BOAs, while reducing the number of interventions. In addition, when observability is poor, mSFQI produces better results when the probabilities for a greater amount of previous observations are available.

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.