GFlowCausal: Generative Flow Networks for Causal Discovery

Causal discovery aims to uncover causal structure among a set of variables. Score-based approaches mainly focus on searching for the best Directed Acyclic Graph (DAG) based on a predefined score function. However, most of them are not applicable on a large scale due to the limited searchability. Inspired by the active learning in generative flow networks, we propose a novel approach to learning a DAG from observational data called GFlowCausal. It converts the graph search problem to a generation problem, in which direct edges are added gradually. GFlowCausal aims to learn the best policy to generate high-reward DAGs by sequential actions with probabilities proportional to predefined rewards. We propose a plug-and-play module based on transitive closure to ensure efficient sampling. Theoretical analysis shows that this module could guarantee acyclicity properties effectively and the consistency between final states and fully-connected graphs. We conduct extensive experiments on both synthetic and real datasets, and results show the proposed approach to be superior and also performs well in a large-scale setting

A New Method for Conflict Resoluton Based on Multi-Agent Reinforcement Learning Algorithms

Conflict resolution is a research topic for game theory (GT) and conflict analysis. A decision support system (DSS) is very helpful for conflict decision making. Reinforcement learning (RL) is an efficient method to learn knowledge by agents themselves. Although successful applications of RL have been reported in single-agent domain, a lot of work should be done in the case of multi-agent domain. Nash Q-learning is a famous learning algorithm for multi-agent RL. Based on the Nash Q-learning, a novel DSS: multi-agent RL based DSS (MRLDSS) is proposed in this paper and is tested by using several typical examples of conflict resolution. Experimental results show that the proposed architecture and algorithm can solve conflict resolution problems correctly and efficiently

Yeast increases glycolytic flux to support higher growth rates accompanied by decreased metabolite regulation and lower protein phosphorylation

Supply of Gibbs free energy and precursors are vital for cellular function and cell metabolism have evolved to be tightly regulated to balance their supply and consumption. Precursors and Gibbs free energy are generated in the central carbon metabolism (CCM), and fluxes through these pathways are precisely regulated. However, how fluxes through CCM pathways are affected by posttranslational modification and allosteric regulation remains poorly understood. Here, we integrated multi-omics data collected under nine different chemostat conditions to explore how fluxes in the CCM are regulated in the yeast Saccharomyces cerevisiae. We deduced a pathway- and metabolism-specific CCM flux regulation mechanism using hierarchical analysis combined with mathematical modeling. We found that increased glycolytic flux associated with an increased specific growth rate was accompanied by a decrease in flux regulation by metabolite concentrations, including the concentration of allosteric effectors, and a decrease in the phosphorylation level of glycolytic enzymes