RNAs are known to play essential roles in diverse cellular functions, extending well-beyond transfer of information from genes to proteins. RNA function is closely linked to its ability to fold into and convert between specific complex structures. Determining RNA structure has thus become a crucial step in understanding its function. Structure profiling experiments provide single nucleotide information on RNA structure. Recent advances in chemistry combined with application of new high-throughput sequencing techniques have enabled RNA structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. In this paper, we review current practices in analysis of structure profiling data, with emphasis on comparative and integrative analysis, as well as highlight emerging questions.

 

The controllable kinetics of strand displacement reaction, along with the programmability of DNA sequence, make DNA an excellent material for the fabrication of molecular machines and complex circuit, and may potentially be used in the disease diagnosis. In this review, we discuss the applications of toehold-mediated strand displacement in the constructions of exquisite molecular devices, complex functional circuits and reaction networks, and biomedical applications such as SNP discrimination and gene-induced disease detection and gene regulation.

 

A bacteriophage (phage) is a virus that infects and replicates within bacteria, often leading to bacterial lysis and death. Phages that are able to efficiently kill specific bacterial pathogens have long been identified as having therapeutic potential in the context of treating bacterial disease in animals and humans; however the efficacies of phage therapies have rarely reached those of antibiotic drugs. This article aims to summarise recent developments in the field of phage engineering, where the tools of molecular biology and synthetic biology are being used to modify phages in ways that enhance or alter their natural antimicrobial function.

 

Recently it has been shown that synthetic microbial consortia can use intercellular signaling pathways to create transcriptional regulatory topologies that mimic genetic circuits. However, if the strains within the consortium compete for resources, an added layer of complexity emerges. Here, we use computational modeling to explore the behavior of a two strain, transcriptionally co-repressive microbial consortium. We find that, unlike its genetic counterpart the bistable toggle switch, the co-repressive consortium can exhibit oscillatory behavior if the strains’ growth rates depend on protein production.

 

The CRISPR-Cas system is a widespread evolutionary adaptation in prokaryotes including archaea and bacteria, defending against invasive nucleic acids, such as plasmids or viruses. We aim to visualize and characterize how a CRISPR system acts within E. coli cells to destroy a phage invader at the single-cell level. By fluorescently labeling and tracking phage lambda DNA after infection using microscopy, we find that CRISPR rapidly degrades phage DNA to allow the cell to live on, and discover some parameters accounting for the cell-to-cell variability of the CRISPR functions, providing insights on how CRISPR systems protect bacteria.

 

We have quantitatively explored the potential for engineering living cells to assemble and program synthetic gene networks in artificial protocells. We envision engineering living cells that control the assembly of linear DNA on a microparticle scaffold. Synthetic circuits could be encoded in this linear DNA. Artificial cells could then be created by encapsulating these scaffolds with a transcription-translation, cell-free expression reaction. Quantitative models of this process show that protocell expression could be tuned by altering the gene network motifs within the engineered living cells. These results demonstrate the potential for engineering ecosystems of living and artificial cells.

 

Machine learning models can learn knowledge from a given dataset and make predictions on unknown data. These technologies are widely used in artificial intelligence and made tremendous progress, triggering the coming era of “Industry 4.0”. In life sciences, introduction of such methods has greatly promoted the development of the discipline, especially modeling in bioinformatics and systems biology. As a powerful machine learning method suitable for small sample learning, support vector machine (SVM) was introduced into the field of promoter strength prediction. The good performance of SVM models demonstrates a promising application prospect of this method in prediction of promoter strength.

Background: A comprehensive metabolism network of engineered E. coli is very important in systems biology and metabolomics studies. Many tools focus on two-dimensional space to display pathways in metabolic network. However, the usage of three-dimensional visualization may help to understand better the intricate topology of metabolic and regulatory networks.

Methods: We manually curated large amount of experimental data (including pathways, reactions and metabolites) from literature related with different types of engineered E. coli and then utilized a novel technology of three dimensional visualization to develop a comprehensive metabolic network named SynBioEcoli.

Results: SynBioEoli contains 740 biosynthetic pathways, 3,889 metabolic reactions, 2,255 chemical compoundsmanually curated from about 11,000 metabolism publications related with different types of engineered E. coli. Furthermore, SynBioEcoli integrates with various informatics techniques.

Conclusions: SynBioEcoli could be regarded as a comprehensive knowledgebase of engineered E. coli and represents the next generation cellular metabolism network visualization technology. It could be accessed via web browsers (such as Google Chrome) supporting WebGL, at http://www.rxnfinder.org/synbioecoli/.

A comprehensive metabolism network of engineered E. coli is very important in systems biology and metabolomics studies. The usage of three-dimensional visualization may help to understand better metabolic and regulatory networks than many tools which focus on two-dimensional space. Based on the large amount of experimental data manually curated from publications related with different types of engineered E. coli and novel technology of three dimensional visualization, SynBioEcoli was developed, which also integrates with various informatics techniques. It could be regarded as a comprehensive knowledgebase of engineered E. coli and represents the next generation cellular metabolism network visualization technology.