System-level strategies for studying the metabolism of Mycobacterium tuberculosis

Despite decades of research many aspects of the biology of Mycobacterium tuberculosis remain unclear and this is reflected in the antiquated tools available to treat and prevent tuberculosis and consequently this disease remains a serious public health problem. Important discoveries linking M. tuberculosis's metabolism and pathogenesis have renewed interest in this area of research. Previous experimental studies were limited to the analysis of individual genes or enzymes whereas recent advances in computational systems biology and high throughput experimental technologies now allow metabolism to be studied on a genome scale. Here we discuss the progress being made in applying system level approaches to studying the metabolism of this important pathogen. The information from these studies will fundamentally change our approach to tuberculosis research and lead to new targets for therapeutic drugs and vaccines.     

Assessment of dietary intake: NuGO symposium report

Advances in genomics science and associated bioinformatics and technology mean that excellent tools are available for characterising human genotypes. At the same time, approaches for characterising individual phenotypes are developing rapidly. In contrast, there has been much less investment in novel methodology for measuring dietary exposures so that there is now a significant gap in the toolkit for those investigating how diet interacts with genotype to determine phenotype. This symposium reviewed the strengths and limitations of current tools used in assessment of dietary intake and the potential to improve these tools through, for example, the use of statistical techniques that combine information from different sources (such as modelling and calibration methods) to ameliorate measurement error and to provide validity checks. Speakers examined the use of approaches based on technologies such as mobile 'phones, digital cameras and Web-based systems which offer the potential for more acceptable (for study participants) and less laborious (for researchers and participants) routes to more robust data collection. In addition, the application of omics, especially metabolomics, tools to biofluids to identify new biomarkers of intake offers great potential to provide objective measures of food consumption with the advantage that data may be collected in forms that can be integrated readily with other high throughput (nutrigenomic) technologies.     

Virus-host interactomes and global models of virus-infected cells

Novel high-throughput technologies such as yeast two-hybrid and RNA interference (RNAi) screens provide the tools to study interactions between viral proteins and the host on a genomic scale. In this review, we provide an overview of studies in which these technologies were applied and of computational approaches for the analysis of the identified viral interactors in the context of the host cell. The results of these studies illustrate the advantages of integrative systems biology approaches in the investigation of viral pathogens.     

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed 'omics' technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A 'system' approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with 'system approaches' in animal sciences, providing exciting opportunities to predict and modulate animal traits.     

From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

     

Trajectories of Aging: How Systems Biology in Yeast Can Illuminate Mechanisms of Personalized Aging

All organisms age, but the extent to which all organisms age the same way remains a fundamental unanswered question in biology. Across species, it is now clear that at least some aspects of aging are highly conserved and are perhaps universal, but other mechanisms of aging are private to individual species or sets of closely related species. Within the same species, however, it has generally been assumed that the molecular mechanisms of aging are largely invariant from one individual to the next. With the development of new tools for studying aging at the individual cell level in budding yeast, recent data has called this assumption into question. There is emerging evidence that individual yeast mother cells may undergo fundamentally different trajectories of aging. Individual trajectories of aging are difficult to study by traditional population level assays, but through the application of systems biology approaches combined with novel microfluidic technologies, it is now possible to observe and study these phenomena in real time. Understanding the spectrum of mechanisms that determine how different individuals age is a necessary step toward the goal of personalized geroscience, where healthy longevity is optimized for each individual.     

Systems chemical biology

The increasing availability of data related to genes, proteins and their modulation by small molecules has provided a vast amount of biological information leading to the emergence of systems biology and the broad use of simulation tools for data analysis. However, there is a critical need to develop cheminformatics tools that can integrate chemical knowledge with these biological databases and simulation approaches, with the goal of creating systems chemical biology.     

Building biological foundries for next-generation synthetic biology

Synthetic biology is an interdisciplinary field that takes top-down approaches to understand and engineer biological systems through design-build-test cycles. A number of advances in this relatively young field have greatly accelerated such engineering cycles. Specifically, various innovative tools were developed for in silico biosystems design, DNA de novo synthesis and assembly, construct verification, as well as metabolite analysis, which have laid a solid foundation for building biological foundries for rapid prototyping of improved or novel biosystems. This review summarizes the state-of-the-art technologies for synthetic biology and discusses the challenges to establish such biological foundries.     

DraGnET: Software for storing,managing and analyzing annotated draft genome sequence data

Background  

New "next generation" DNA sequencing technologies offer individual researchers the ability to rapidly generate large amounts of genome sequence data at dramatically reduced costs. As a result, a need has arisen for new software tools for storage, management and analysis of genome sequence data. Although bioinformatic tools are available for the analysis and management of genome sequences, limitations still remain. For example, restrictions on the submission of data and use of these tools may be imposed, thereby making them unsuitable for sequencing projects that need to remain in-house or proprietary during their initial stages. Furthermore, the availability and use of next generation sequencing in industrial, governmental and academic environments requires biologist to have access to computational support for the curation and analysis of the data generated; however, this type of support is not always immediately available.     

Toward systems metabolic engineering of Aspergillus and Pichia species for the production of chemicals and biofuels

Recently genome sequence data have become available for Aspergillus and Pichia species of industrial interest. This has stimulated the use of systems biology approaches for large-scale analysis of the molecular and metabolic responses of Aspergillus and Pichia under defined conditions, which has resulted in much new biological information. Case-specific contextualization of this information has been performed using comparative and functional genomic tools. Genomics data are also the basis for constructing genome-scale metabolic models, and these models have helped in the contextualization of knowledge on the fundamental biology of Aspergillus and Pichia species. Furthermore, with the availability of these models, the engineering of Aspergillus and Pichia is moving from traditional approaches, such as random mutagenesis, to a systems metabolic engineering approach. Here we review the recent trends in systems biology of Aspergillus and Pichia species, highlighting the relevance of these developments for systems metabolic engineering of these organisms for the production of hydrolytic enzymes, biofuels and chemicals from biomass.     

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.     

Proteomics: a technology-driven and technology-limited discovery science

An emerging field for the analysis of biological systems is the study of the complete protein complement of the genome, the 'proteome'. There are several complementary tools available for proteome analysis including 2D protein electrophoresis and mass spectrometry. Emerging technologies for proteome analysis include spotted-array-based methods and microfluidic devices. Taken together, these technologies provide a wealth of information that is useful in discovery-based science. However, there are some key limitations of these approaches and new technology is required to be able to fully integrate proteomic information with information obtained about DNA sequence, mRNA profiles and metabolite concentrations into effective models of biological systems.     

Chemical biology approaches in plant stress research

In addition to their importance as essential agrochemicals and life-saving drugs, small molecules serve as powerful research tools to address questions at all levels of biological complexity from protein function to plant biotic interactions. In certain contexts, chemical tools are complementary or even preferred to genetic analysis, since not all experimental systems are amenable for genetic dissection. For example, mutants impaired in oxygen sensing cannot easily be recovered. Pharmacological and chemical genetics approaches have come to the rescue of biologists in unraveling such genetically intractable systems. In this review, I have discussed my own efforts to analyze oxygen deprivation signaling in plants to illustrate the validity of small molecular approaches in elucidating an essential pathway such as oxygen sensing. Chemical biology is also a potent approach to tease out genetically redundant biological processes. The recent breakthrough in identifying the elusive abscisic acid receptors has clearly demonstrated the power of chemical tools in dissecting redundant pathways and led to the blossoming of this area as a distinct discipline of plant biology research. I present a summary of this work and conclude the review with potential challenges in using chemical tools.     

合成生物学技术在环境保护中的应用

合成生物学是一个基于生物学和工程学原理的科学领域,其目的是重新设计和重组微生物,以优化或创建具有增强功能的新生物系统。该领域利用分子工具、系统生物学和遗传框架的重编程,从而构建合成途径以获得具有替代功能的微生物。传统上,合成生物学方法通常旨在开发具有成本效益的微生物细胞工厂进而从可再生资源中生产化学物质。然而,近年来合成生物学技术开始在环境保护中发挥着更直接的作用。本综述介绍了基因工程中的合成生物学工具,讨论了基于基因工程的微生物修复策略,强调了合成生物学技术可以通过响应特定污染物进行生物修复来保护环境。其中,规律间隔成簇短回文重复序列(Clustered Regularly Interspersed Short Palindromic Repeats, CRISPR)技术在基因工程细菌和古细菌的生物修复中得到了广泛应用,生物修复领域也出现了很多新的先进技术,包括生物膜工程、人工微生物群落的构建、基因驱动、酶和蛋白质工程等。有了这些新的技术和工具,生物修复将成为当今最好和最有效的污染物去除方式之一。     

Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms.

Massively parallel signature sequencing (MPSS) is one of the newest tools available for conducting in-depth expression profiling. MPSS is an open-ended platform that analyses the level of expression of virtually all genes in a sample by counting the number of individual mRNA molecules produced from each gene. There is no requirement that genes be identified and characterised prior to conducting an experiment. MPSS has a routine sensitivity at a level of a few molecules of mRNA per cell, and the datasets are in a digital format that simplifies the management and analysis of the data. Therefore, of the various microarray and non-microarray technologies currently available, MPSS provides many advantages for generating the type of complete datasets that will help to facilitate hypothesis-driven experiments in the era of digital biology.