Cellular behavior is determined not by a single molecule but by many molecules that interact strongly with one another and form a complex network. It is unclear whether cellular behavior can be controlled by regulating certain molecular components in the network. By analyzing a variety of biomolecular regulatory networks, we discovered that only a small fraction of the network components need to be regulated to govern the network dynamics and control cellular behavior. We defined a minimal set of network components that must be regulated to make the cell reach a desired stable state as the control kernel and developed a general algorithm for identifying it. We found that the size of the control kernel was related to both the topological and logical characteristics of a network. Intriguingly, the control kernel of the human signaling network included many drug targets and chemical-binding interactions, suggesting therapeutic application of the control kernel.

Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided.

Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes and fail to detect potential causal relationships effectively. Here, we propose a novel approach ‘TENET’ to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data.

Understanding the specific survival of the rare chronic myelogenous leukaemia (CML) stem cell population could provide a target for therapeutics aimed at eradicating these cells. However, little is known about how survival signalling is regulated in CML stem cells. In this study, we survey global metabolic differences between murine normal haematopoietic stem cells (HSCs) and CML stem cells using metabolomics techniques. Strikingly, we show that CML stem cells accumulate significantly higher levels of certain dipeptide species than normal HSCs. Once internalized, these dipeptide species activate amino-acid signalling via a pathway involving p38MAPK and the stemness transcription factor Smad3, which promotes CML stem cell maintenance. Importantly, pharmacological inhibition of dipeptide uptake inhibits CML stem cell activity in vivo. Our results demonstrate that dipeptide species support CML stem cell maintenance by activating p38MAPK–Smad3 signalling in vivo, and thus point towards a potential therapeutic target for CML treatment.

The co-regulation of transcription factors (TFs) has been widely observed in various species. Why is such a co-regulation mechanism needed for transcriptional regulation? To answer this question, the following experiments and analyses were performed. First, examination of the human gene regulatory network (GRN) indicated that co-regulation was significantly enriched in the human GRN. Second, mathematical simulation of an artificial regulatory network showed that the co-regulation mechanism was related to the biphasic dose–response patterns of TFs. Third, the relationship between the co-regulation mechanism and the biphasic dose–response pattern was confirmed using microarray experiments examining different time points and different doses of the toxicant tetrachlorodibenzodioxin. Finally, two mathematical models were constructed to mimic highly co-regulated networks (HCNs) and little co-regulated networks (LCNs), and we found that HCNs were more robust to parameter perturbation than LCNs, whereas LCNs were faster in adaptation to environmental changes than HCNs.