Database and tools for metabolic network analysis

Metabolic network analysis has attracted much attention in the area of systems biology. It has a profound role in understanding the key features of organism metabolic networks and has been successfully applied in several fields of systems biology, including in silico gene knockouts, production yield improvement using engineered microbial strains, drug target identification, and phenotype prediction. A variety of metabolic network databases and tools have been developed in order to assist research in these fields. Databases that comprise biochemical data are normally integrated with the use of metabolic network analysis tools in order to give a more comprehensive result. This paper reviews and compares eight databases as well as twenty one recent tools. The aim of this review is to study the different types of tools in terms of the features and usability, as well as the databases in terms of the scope and data provided. These tools can be categorised into three main types: standalone tools; toolbox-based tools; and web-based tools. Furthermore, comparisons of the databases as well as the tools are also provided to help software developers and users gain a clearer insight and a better understanding of metabolic network analysis. Additionally, this review also helps to provide useful information that can be used as guidance in choosing tools and databases for a particular research interest.     

全基因组范围代谢网络的构建和最小基因组研究

全基因组范围代谢网络（genome-scale metabolic network,GSMN）的构建是合成生物学研究的一个重要研究手段。通过整合各种组学数据和借助计算机进行模拟分析,将基因型与表型的关系进行定量关联,从而为从全局的角度探索和揭示生物代谢机制,进而对生物进行合理的重新设计和工程改造提供了有效的框架。该方法在最小基因组研究中也有着突出的优势,通过计算机辅助的基因组最小化模拟与分析,能够系统鉴定微生物基因组基因的必需性。迄今为止,已有近百个基因组范围的代谢网络发表,覆盖的生物包括原核生物、真核生物和古生生物,并广泛应用于医药、能源、环境、工业和农业等多个领域,展现出了广阔的应用前景。将对全基因组范围代谢网络构建的方法、应用,特别是其在最小基因组研究中的应用作简要的综述。     

Navigating cancer network attractors for tumor-specific therapy

Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies.     

The human extended mitochondrial metabolic network: new hubs from lipids

Even if systems thinking is not new in biology, rationalizing the explosively growing amount of knowledge has been the compelling reason for the sudden rise and spreading of systems biology. Based on 'omics' data, several genome-scale metabolic networks have been reconstructed and validated. One of the most striking aspects of complex metabolic networks is the pervasive power-law appearance of metabolite connectivity. However, the combinatorial diversity of some classes of compounds, such as lipids, has been scarcely considered so far. In this work, a lipid-extended human mitochondrial metabolic network has been built and analyzed. It is shown that, considering combinatorial diversity of lipids and multipurpose enzymes, an intimate connection between membrane lipids and oxidative phosphorilation appears. This finding leads to some biomedical considerations on diseases involving mitochondrial enzymes. Moreover, the lipid-extended network still shows power-law features. Power-law distributions are intrinsic to metabolic network organization and evolution. Hubs in the lipid-extended mitochondrial network strongly suggest that the "RNA world" and the "lipid world" hypothesis are both correct.     

A network perspective on metabolism and aging

Aging affects a myriad of genetic, biochemical, and metabolic processes, and efforts to understand the underlying molecular basis of aging are often thwarted by the complexity of the aging process. By taking a systems biology approach, network analysis is well-suited to study the decline in function with age. Network analysis has already been utilized in describing other complex processes such as development, evolution, and robustness. Networks of gene expression and protein-protein interaction have provided valuable insight into the loss of connectivity and network structure throughout lifespan. Here, we advocate the use of metabolic networks to expand the work from genomics and proteomics. As metabolism is the final fingerprint of functionality and has been implicated in multiple theories of aging, metabolomic methods combined with metabolite network analyses should pave the way to investigate how relationships of metabolites change with age and how these interactions affect phenotype and function of the aging individual. The metabolomic network approaches highlighted in this review are fundamental for an understanding of systematic declines and of failure to function with age.     

Influence of metabolic network structure and function on enzyme evolution

Background  

Most studies of molecular evolution are focused on individual genes and proteins. However, understanding the design principles and evolutionary properties of molecular networks requires a system-wide perspective. In the present work we connect molecular evolution on the gene level with system properties of a cellular metabolic network. In contrast to protein interaction networks, where several previous studies investigated the molecular evolution of proteins, metabolic networks have a relatively well-defined global function. The ability to consider fluxes in a metabolic network allows us to relate the functional role of each enzyme in a network to its rate of evolution.     

Analysis of metabolic network based on conservation of molecular structure

Recent work has revealed much about chemical reactions inside hundreds of organisms as well as universal characteristics of metabolic networks, which shed light on the evolution of the networks. However, characteristics of individual metabolites have been neglected. For example, some carbohydrates have structures that are decomposed into small molecules by metabolic reactions, but coenzymes such as ATP are mostly preserved. Such differences in metabolite characteristics are important for understanding the universal characteristics of metabolic networks. To quantify the structure conservation of metabolites, we defined the "structure conservation index" (SCI) for each metabolite as the fraction of metabolite atoms restored to their original positions through metabolic reactions. As expected, coenzymes and coenzyme-like metabolites that have reaction loops in the network show a higher SCI. Using the index, we found that the sum of metabolic fluxes is negatively correlated with the structure preservation of metabolite. Also, we found that each reaction path around high SCI metabolites changes independently, while changes in reaction paths involving low SCI metabolites coincide through evolution processes. These correlations may provide a clue to universal properties of metabolic networks.     

Metabolic network modeling and simulation for drug targeting and discovery

Systems biology has greatly contributed toward the analysis and understanding of biological systems under various genotypic and environmental conditions on a much larger scale than ever before. One of the applications of systems biology can be seen in unraveling and understanding complicated human diseases where the primary causes for a disease are often not clear. The in silico genome-scale metabolic network models can be employed for the analysis of diseases and for the discovery of novel drug targets suitable for treating the disease. Also, new antimicrobial targets can be discovered by analyzing, at the systems level, the genome-scale metabolic network of pathogenic microorganisms. Such applications are possible as these genome-scale metabolic network models contain extensive stoichiometric relationships among the metabolites constituting the organism's metabolism and information on the associated biophysical constraints. In this review, we highlight applications of genome-scale metabolic network modeling and simulations in predicting drug targets and designing potential strategies in combating pathogenic infection. Also, the use of metabolic network models in the systematic analysis of several human diseases is examined. Other computational and experimental approaches are discussed to complement the use of metabolic network models in the analysis of biological systems and to facilitate the drug discovery pipeline.     

A metabolic network approach for the identification and prioritization of antimicrobial drug targets

For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents.     

Metabolic network discovery through reverse engineering of metabolome data

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data types based on biological/environmental variability around steady state were analyzed to compare the relative information content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions. We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong to metabolites connected with weak interaction strength.     

Identification of functional modules using network topology and high-throughput data

Background  

With the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data.     

Deciphering network community structure by surprise

The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthetic networks. Particularly, S qualitatively outperforms the most commonly used criterion to define communities, Newman and Girvan's modularity (Q). Applying S maximization to real networks often provides natural, well-supported partitions, but also sometimes counterintuitive solutions that expose the limitations of our previous knowledge. These results indicate that it is possible to define an effective global criterion for community structure and open new routes for the understanding of complex networks.     

MrBac: a web server for draft metabolic network reconstructions for bacteria

Genome-scale metabolic network reconstruction can be used for simulating cellular behaviors by simultaneously monitoring thousands of biochemical reactions, and is therefore important for systems biology studies in microbes. However, the labor-intensive and time-consuming reconstruction process has hindered the progress of this important field. Here we present a web server, MrBac (Metabolic network Reconstructions for Bacteria), to streamline the network reconstruction process for draft genome-scale metabolic networks and to provide annotation information from multiple databases for further curation of the draft reconstructions. MrBac integrates comparative genomics, retrieval of genome annotations, and generation of standard systems biology file format ready for network analyses. We also used MrBac to automatically generate a draft metabolic model of Salmonella enteric serovar Typhimurium LT2. The high similarity between this automatic model and the experimentally validated models further supports the usefulness and accuracy of MrBac. The high efficiency and accuracy of MrBac may accelerate the advances of systems biology studies on microbiology. MrBac is freely available at http://sb.nhri.org.tw/MrBac.     

Metabolic flux analysis as a tool in metabolic engineering of plants

Methods of metabolic flux analysis (MFA) provide insights into the theoretical capabilities of metabolic networks and allow probing the in vivo performance of cellular metabolism. In recent years, an increasing awareness has developed that network analysis methods within the systems biology toolbox are serving to improve our understanding and ability to manipulate metabolism. In this minireview the potential of MFA to increase the chances of success in metabolic engineering of plants is presented, recent progress related to engineering and flux analysis in central metabolism of plants is discussed, and some recent advances in flux analysis methodology are highlighted.     

Metabolic systems cost-benefit analysis for interpreting network structure and regulation

MOTIVATION: Interpretation of bioinformatics data in terms of cellular function is a major challenge facing systems biology. This question is complicated by robust metabolic networks filled with structural features like parallel pathways and isozymes. Under conditions of nutrient sufficiency, metabolic networks are well known to be regulated for thermodynamic efficiency however; efficient biochemical pathways are anabolically expensive to construct. While parameters like thermodynamic efficiency have been extensively studied, a systems-based analysis of anabolic proteome synthesis 'costs' and the cellular function implications of these costs has not been reported. RESULTS: A cost-benefit analysis of an in silico Escherichia coli network revealed the relationship between metabolic pathway proteome synthesis requirements, DNA-coding sequence length, thermodynamic efficiency and substrate affinity. The results highlight basic metabolic network design principles. Pathway proteome synthesis requirements appear to have shaped biochemical network structure and regulation. Under conditions of nutrient scarcity and other general stresses, E. coli expresses pathways with relatively inexpensive proteome synthesis requirements instead of more efficient but also anabolically more expensive pathways. This evolutionary strategy provides a cellular function-based explanation for common network motifs like isozymes and parallel pathways and possibly explains 'overflow' metabolisms observed during nutrient scarcity. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.