Robustness analysis of cellular systems using the genetic tug-of-war method

Robustness is one of the principles of design inherent to biological systems. Cellular robustness can be measured as limits of intracellular parameters such as gene expression levels. We have recently developed an experimental approach coined as genetic Tug-Of-War (gTOW), which we used to perform robustness analysis in yeast. Using gTOW, we were able to measure the upper limit of expression of gene targets. In this review, we first elaborate on how the gTOW method compares to current mathematical simulation models prevalently used in the determination of robustness. We then explain the experimental principles underlying gTOW and its associated tools, and we provide concrete examples of robustness analysis using gTOW, i.e. cell cycle and HOG pathway gene expression analysis. Finally, we list a series of Q&As related to the experimental utilization of gTOW and we describe the potential impact of gTOW and its relevance to the understanding of biological systems.     

Pathway and ontology analysis: emerging approaches connecting transcriptome data and clinical endpoints

The increasing use of gene expression profiling offers great promise in clinical research into disease biology and its treatment. Along with the ability to measure changing expression levels in thousands of genes at once, comes the challenge of analyzing and interpreting the vast sets of data generated. Analysis tools are evolving rapidly to meet such challenges. The next step is to interpret observed changes in terms of the biological properties or relationships underlying them. One powerful approach is to make associations between the genes that are under investigation and well-known biochemical or signaling pathways, and further to assess the significance of such associations. Similarly, genes can be mapped to standardized biological categories via an ontology resource. We discuss these approaches and several web-based resources and tools designed to facilitate such analyses. This information can be used to facilitate understanding and to help design more focused experiments for validating the relevance and importance of these biological pathways and processes in human disease and therapeutics.     

Proteomic tools for biomedicine

Proteomic tools measure gene expression, protein activity and interactions of biological events at the protein level. Proteins are the major catalysts of biological functions and contain several dimensions of information that collectively indicate the actual rather than the potential functional state as indicated by mRNA analysis. Measurements can be made in terms of protein quantity, location, and time-point. For the future we see a further integration of existing and new technologies for proteomics from a wide range of areas of biochemistry, chemistry, physics, computing science and molecular biology. This will further advance our knowledge of how biological systems are built up and what mechanisms control these systems. However, the potential of proteomics to comprehensively answer all biological questions is limited as only protein activity is measured. A unification of genomics, proteomics, and other technologies is needed if we are to start to understand the complexity of biological function in the context of disease and health.     

Opportunities in the design and application of RNA for gene expression control

The past decade of synthetic biology research has witnessed numerous advances in the development of tools and frameworks for the design and characterization of biological systems. Researchers have focused on the use of RNA for gene expression control due to its versatility in sensing molecular ligands and the relative ease by which RNA can be modeled and designed compared to proteins. We review the recent progress in the field with respect to RNA-based genetic devices that are controlled through small molecule and protein interactions. We discuss new approaches for generating and characterizing these devices and their underlying components. We also highlight immediate challenges, future directions and recent applications of synthetic RNA devices in engineered biological systems.     

Toolboxes for cyanobacteria: Recent advances and future direction

Photosynthetic cyanobacteria are important primary producers and model organisms for studying photosynthesis and elements cycling on earth. Due to the ability to absorb sunlight and utilize carbon dioxide, cyanobacteria have also been proposed as renewable chassis for carbon-neutral “microbial cell factories”. Recent progresses on cyanobacterial synthetic biology have led to the successful production of more than two dozen of fuels and fine chemicals directly from CO₂, demonstrating their potential for scale-up application in the future. However, compared with popular heterotrophic chassis like Escherichia coli and Saccharomyces cerevisiae, where abundant genetic tools are available for manipulations at levels from single gene, pathway to whole genome, limited genetic tools are accessible to cyanobacteria. Consequently, this significant technical hurdle restricts both the basic biological researches and further development and application of these renewable systems. Though still lagging the heterotrophic chassis, the vital roles of genetic tools in tuning of gene expression, carbon flux re-direction as well as genome-wide manipulations have been increasingly recognized in cyanobacteria. In recent years, significant progresses on developing and introducing new and efficient genetic tools have been made for cyanobacteria, including promoters, riboswitches, ribosome binding site engineering, clustered regularly interspaced short palindromic repeats/CRISPR-associated nuclease (CRISPR/Cas) systems, small RNA regulatory tools and genome-scale modeling strategies. In this review, we critically summarize recent advances on development and applications as well as technical limitations and future directions of the genetic tools in cyanobacteria. In addition, toolboxes feasible for using in large-scale cultivation are also briefly discussed.     

Hybrid semi-parametric mathematical systems: bridging the gap between systems biology and process engineering

Systems biology is an integrative science that aims at the global characterization of biological systems. Huge amounts of data regarding gene expression, proteins activity and metabolite concentrations are collected by designing systematic genetic or environmental perturbations. Then the challenge is to integrate such data in a global model in order to provide a global picture of the cell. The analysis of these data is largely dominated by nonparametric modelling tools. In contrast, classical bioprocess engineering has been primarily founded on first principles models, but it has systematically overlooked the details of the embedded biological system. The full complexity of biological systems is currently assumed by systems biology and this knowledge can now be taken by engineers to decide how to optimally design and operate their processes. This paper discusses possible methodologies for the integration of systems biology and bioprocess engineering with emphasis on applications involving animal cell cultures. At the mathematical systems level, the discussion is focused on hybrid semi-parametric systems as a way to bridge systems biology and bioprocess engineering.     

Studying Gene Expression System Regulation at the Program Level

Understanding how gene expression systems influence biological outcomes is an important goal for diverse areas of research. Gene expression profiling allows for the simultaneous measurement of expression levels for thousands of genes and the opportunity to use this information to increase biological understanding. Yet, the best way to relate this immense amount of information to biological outcomes is far from clear. Here, a novel approach to gene expression systems research is presented that focuses on understanding gene expression systems at the level of gene expression program regulation. It is suggested that such an approach has important advantages over current techniques and may provide novel insights into how gene expression systems are regulated to shape biological outcomes such as the development of disease or response to treatment.     

An integrated approach to identify bimodal genes associated with prognosis in câncer

Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access section.     

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%–40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.     

Tools for visually exploring biological networks

Many tools exist for visually exploring biological networks including well-known examples such as Cytoscape, VisANT, Pathway Studio and Patika. These systems play a key role in the development of integrative biology, systems biology and integrative bioinformatics. The trend in the development of these tools is to go beyond 'static' representations of cellular state, towards a more dynamic model of cellular processes through the incorporation of gene expression data, subcellular localization information and time-dependent behavior. We provide a comprehensive review of the relative advantages and disadvantages of existing systems with two goals in mind: to aid researchers in efficiently identifying the appropriate existing tools for data visualization; to describe the necessary and realistic goals for the next generation of visualization tools. In view of the first goal, we provide in the Supplementary Material a systematic comparison of more than 35 existing tools in terms of over 25 different features. Supplementary information: Supplementary data are available at Bioinformatics online.     

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for <Emphasis Type="Italic">Arabidopsis</Emphasis>

Background  

To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis.     

Rapid establishment of high-producing cell lines using dicistronic vectors with glutamine synthetase as the selection marker

Recombinant proteins are useful tools in biological research, drug development, and drug screening. Specially designed expression vectors have been developed to introduce cDNA for recombinant protein expression in mammalian cells. We have combined a discistronic mRNA design for expression of the recombinant protein, using glutamine synthetase (GS) for selection. A soluble form of human interleukin-4 receptor alpha chain was used as the model protein. The dicistronic vectors were compared to a standard expression vector in CHO-K1 cells in parallel experiments. Our data showed that a dicistronic vector containing an internal ribosome entry site (IRES) of the encephalomyocarditis virus (ECMV) was superior to a conventional expression vector in both levels of protein expression and amplification efficiency. The productivity of these clones was stable without selection pressure for an extended period of time. The GS selection system within a dicistronic vector design can achieve rapid and efficient gene amplification for protein production.     

DNA芯片技术研究进展及其在分子营养上的应用

DNA芯片技术是近年发展起来的又一新分子生物学研究工具,可使研究者得以自动化、快速、平行地对大量的生物信息加以分析,在基因组水平上研究基因表达。这种技术为从基因组水平研究基因表达水平与生理反应及生理状况的改变之间的关系提供了强有力的手段。通过比较不同营养水平或不同环境条件下的组织细胞基因达到表达谱差异,可以从基因组水平阐明各种营养成分或环境因素对动物机体的基因表达的影响,从而进一步揭示营养生理的机制和环境对动物影响的机理。DNA芯片技术为分子营养的研究开辟了一条崭新的道路,在从DNA芯片的原理、种类、实验设计、统计方法及在分子营养上的应用作一综述。