F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

ABSTRACT: BACKGROUND: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategiesto knock out target reactions. RESULTS: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. CONCLUSIONS: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/     

MetaGen: 从KEGG建模代谢网络的新工具

为便于大规模代谢网络的计算,发展了一款方便实用的工具:MetaGen,对Kyoto Encyclopedia of Genesand Genomes(KEGG)中物种特异的各层次代谢系统进行建模,生成的代谢网络以酶图和通路图的方式表示.利用该工具,对人类代谢系统的bow-tie结构进行了初步研究,并以此为例展示了该工具广阔的应用前景.MetaGen利用KEGGweb服务保证建模数据的可靠性,依靠本地关系数据库加速网络建模过程并提供更多的数据管理和利用方式,并结合高级JAVA技术提高代码的可扩展性.MetaGen完全开源,可直接从http://bnct.sourceforge.net/下载.     

Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

Background  

Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources.     

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric,thermodynamic and physiological constraints

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.     

limmaGUI: a graphical user interface for linear modeling of microarray data

SUMMARY: limmaGUI is a graphical user interface (GUI) based on R-Tcl/Tk for the exploration and linear modeling of data from two-color spotted microarray experiments, especially the assessment of differential expression in complex experiments. limmaGUI provides an interface to the statistical methods of the limma package for R, and is itself implemented as an R package. The software provides point and click access to a range of methods for background correction, graphical display, normalization, and analysis of microarray data. Arbitrarily complex microarray experiments involving multiple RNA sources can be accomodated using linear models and contrasts. Empirical Bayes shrinkage of the gene-wise residual variances is provided to ensure stable results even when the number of arrays is small. Integrated support is provided for quantitative spot quality weights, control spots, within-array replicate spots and multiple testing. limmaGUI is available for most platforms on the which R runs including Windows, Mac and most flavors of Unix. AVAILABILITY: http://bioinf.wehi.edu.au/limmaGUI.     

Metabolite-centric approaches for the discovery of antibacterials using genome-scale metabolic networks

Development of genome-scale metabolic models and various constraints-based flux analyses have enabled more sophisticated examination of metabolism. Recently reported metabolite essentiality studies are also based on the constraints-based modeling, but approaches metabolism from a metabolite-centric perspective, providing synthetic lethal combination of reactions and clues for the rational discovery of antibacterials. In this study, metabolite essentiality analysis was applied to the genome-scale metabolic models of four microorganisms: Escherichia coli, Helicobacter pylori, Mycobacterium tuberculosis and Staphylococcus aureus. Furthermore, chokepoints, metabolites surrounded by enzymes that uniquely consume and/or produce them, were also calculated based on the network properties of the above organisms. A systematic drug targeting strategy was developed by combining information from these two methods. Final drug target metabolites are presented and examined with knowledge from the literature.     

The World Wide Web as a graphical user interface to program macros for molecular graphics,molecular modeling,and structure-based drug design

In this article we describe how the World Wide Web (WWW or Web) has been employed to provide access to computational chemistry software and protein structure data via program macros. We show how the combination of Web technology and macros can automate both the running of chemistry software and the execution of complex operations on protein structures. The current version of the system supports the molecular visualization packages GRASP,¹ RASMOL,² MOLVIEWER-OGL³ and INSIGHT95,⁴ and the ligand design tool GRID⁵ and includes more than 175 in-house protein-ligand complexes. The approach enables in-experienced users to confidently make full use of sophisticated modeling techniques by offering only sensible options, hiding parameter settings, and controlling program invocation and macro excution. Our interface provides both the expert and non-expert alike with powerful tools for protein structure visualization, molecular modeling, and rational drug design.     

affylmGUI: a graphical user interface for linear modeling of single channel microarray data

SUMMARY: affylmGUI is a graphical user interface (GUI) to an integrated workflow for Affymetrix microarray data. The user is able to proceed from raw data (CEL files) to QC and pre-processing, and eventually to analysis of differential expression using linear models with empirical Bayes smoothing. Output of the analysis (tables and figures) can be exported to an HTML report. The GUI provides user-friendly access to state-of-the-art methods embodied in the Bioconductor software repository. AVAILABILITY: affylmGUI is an R package freely available from http://www.bioconductor.org. It requires R version 1.9.0 or later and tcl/tk 8.3 or later and has been successfully tested on Windows 2000, Windows XP, Linux (RedHat and Fedora distributions) and Mac OS/X with X11. Further documentation is available at http://bioinf.wehi.edu.au/affylmGUI CONTACT: keith@wehi.edu.au.     

Comparative genome-scale metabolic modeling of actinomycetes: the topology of essential core metabolism

Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of actinomycetes. Based on in silico knockouts we generated topological and genomic maps for each organism. Combining the collection of genome-wide models, we constructed a global enzyme association network to identify both a conserved "core network" and an "essential core network" of the entire group. As has been reported for low-degree metabolites in several organisms, low-degree enzymes (in linear pathways) turn out to be generally more essential than high-degree enzymes (in metabolic hubs).     

A-Cell: graphical user interface for the construction of biochemical reaction models

SUMMARY: A-Cell is a tool for constructing models of complex and complicated biochemical reactions. An important feature of A-Cell is its graphical user interface for constructing biochemical reactions. In addition, it has a capability of importing previously constructed models, combining them, and constructing a comprehensive model. The simulation program for the model is automatically generated by A-cell.     

Identification of functional differences in metabolic networks using comparative genomics and constraint-based models

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.     

neuroConstruct: a tool for modeling networks of neurons in 3D space

Conductance-based neuronal network models can help us understand how synaptic and cellular mechanisms underlie brain function. However, these complex models are difficult to develop and are inaccessible to most neuroscientists. Moreover, even the most biologically realistic network models disregard many 3D anatomical features of the brain. Here, we describe a new software application, neuroConstruct, that facilitates the creation, visualization, and analysis of networks of multicompartmental neurons in 3D space. A graphical user interface allows model generation and modification without programming. Models within neuroConstruct are based on new simulator-independent NeuroML standards, allowing automatic generation of code for NEURON or GENESIS simulators. neuroConstruct was tested by reproducing published models and its simulator independence verified by comparing the same model on two simulators. We show how more anatomically realistic network models can be created and their properties compared with experimental measurements by extending a published 1D cerebellar granule cell layer model to 3D.     

Mathematical modeling as a tool for investigating cell cycle control networks

Although not a traditional experimental "method," mathematical modeling can provide a powerful approach for investigating complex cell signaling networks, such as those that regulate the eukaryotic cell division cycle. We describe here one modeling approach based on expressing the rates of biochemical reactions in terms of nonlinear ordinary differential equations. We discuss the steps and challenges in assigning numerical values to model parameters and the importance of experimental testing of a mathematical model. We illustrate this approach throughout with the simple and well-characterized example of mitotic cell cycles in frog egg extracts. To facilitate new modeling efforts, we describe several publicly available modeling environments, each with a collection of integrated programs for mathematical modeling. This review is intended to justify the place of mathematical modeling as a standard method for studying molecular regulatory networks and to guide the non-expert to initiate modeling projects in order to gain a systems-level perspective for complex control systems.     

The COG database: a tool for genome-scale analysis of protein functions and evolution

Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The database of Clusters of Orthologous Groups of proteins (COGs) is an attempt on a phylogenetic classification of the proteins encoded in 21 complete genomes of bacteria, archaea and eukaryotes (http://www. ncbi.nlm. nih.gov/COG). The COGs were constructed by applying the criterion of consistency of genome-specific best hits to the results of an exhaustive comparison of all protein sequences from these genomes. The database comprises 2091 COGs that include 56-83% of the gene products from each of the complete bacterial and archaeal genomes and approximately 35% of those from the yeast Saccharomyces cerevisiae genome. The COG database is accompanied by the COGNITOR program that is used to fit new proteins into the COGs and can be applied to functional and phylogenetic annotation of newly sequenced genomes.     

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

Background  

The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.     

From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks

Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic models, which we aim to highlight here. We discuss and illustrate how the integration of thermodynamic and kinetic properties of the yeast metabolic networks in network analyses can help in understanding and utilizing this organism more successfully in the areas of metabolic engineering, synthetic biology and disease treatment.