Relations of gut microbes with various metabolic disorders

Gut is a shelter for various microbes where they live symbiotically with the host. In this symbiotic relationship, these microbes utilizes the resources from the host to survive and in return regulates the metabolic machinery of the host. In hosts, exposure to these microbes begins at the time of birth and works as building blocks of infant gut microbiota and overall health in future. Several factors influences the colonization of microbes inside the infant gut, such as, mode of delivery, the diet of infant (selective substrates), treatment of antibiotics, enrichment of selective microbes and external environment.

The gut microbiota contribute to immune tolerance (by eliminating invading pathogens), intestinal homeostasis and healthy metabolism.  http://www.nature.com/nri/journal/v9/n5/execsumm/nri2515.html   http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337124/ 

Dysbiosis, means disruption of the regular gut microbial composition. This is associated with the various disorders such as:

1) Immune diseases: atrophy, asthma, multiple sclerosis, systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), experimental autoimmune encephalomyelitis and type -1 diabetes. For details read http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337124/   

2) Intestinal diseases: irritable bowel syndrome (IBS), inflammatory bowel diseases (IBD) (including ulcerative colitis and Crohn’s disease), necrotizing enterocolitis and colon cancer. For details read http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3667473/ 

3) Metabolic diseases: type-2 diabetes, atherosclerosis and obesity for details read  http://diabetes.diabetesjournals.org/content/62/10/3341.full   http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4483604/ 

4) Cardiovascular diseases: metabolic interaction between host and microbes in the development of cardiovascular diseases is the new field of study. For details read  http://www.nature.com/nm/journal/v18/n8/abs/nm.2895.html 

5) Liver diseases: alcoholic liver disease and hepatocarcinogenesis. For detail read  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4323444/ 

6) Blood diseases: haematopoiesis and hematologic disorders. For detail read  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4504946/   http://www.bloodjournal.org/content/126/3/311?sso-checked=true 

7) Brain diseases: role of gut microbiota in the regulation of anxiety, mood, cognition and pain has been discussed.  http://www.nature.com/nrn/journal/v13/n10/full/nrn3346.html   http://www.ncbi.nlm.nih.gov/pubmed/25390799  

The field is growing rapidly as facilitated by new, faster and cheaper sequencing technologies. Now, researchers are more interested and trying to find out how the specific microbes related with the disease phenotype? Understanding of host-microbes interactions using metabolomics approaches help us to understand the complete mechanism. 

Machine learning for metagenomics

This review covers most of the machine learning methods used till now for the analysis of metagenomic data. You can read the full text from the link below.
http://arxiv.org/pdf/1510.06621v1.pdf

Role of gut microbes in the metabolism of xenobiotics.

A complex and diverse microbial ecosystem associated with the several parts of human body plays an important role in the maintenance of human health. The large number (~trillion) of microbes resides within the gastrointestinal tract and they are significantly related with various metabolic disorders. It has been reported that the gut microbial community consists of ~1000 microbial species, from the bacterial and archaeal domains. Genomes of gut microbiota collectively encode for millions of genes that is more than 100 times of genes found in the human genome and hence, adding up to the increased metabolic variability of the human body. The unique protein-coding genes from the gut microbes can participate in the various metabolic functions. Several studies had proved that these symbiotic metabolic functions are crucial in deciding upon the actual metabolism of xenobiotics including drugs. This is the very important aspect in the field of pharmaceuticals as most of the drugs, administered via oral route are first encountered by gut microbes. These gut microbes can alter the overall activity and toxicity of a drug molecule via metabolizing it in the gastrointestinal tract. For example, A recent study revealed that the activity of a cardiovascular drug is dependent on the gut microbial composition of the individual. Until today, the complete understanding of the gut microbial metabolic potential towards the xeno-metabolism is very limited and seek for the advancement. 

Useful information for a computational biologist

If you want to be a computational biologist then you must read the article mentioned below. The article has covered the role of computational biologist, the approach before starting a project, use of software's, pipelines and scripts, and the most important thing is: "think like a Scientist not like a programmer/coder.  Interesting and must read.
http://www.nature.com/nbt/journal/v31/n11/full/nbt.2740.html 

Emerging interest towards the understanding of xenobiotics metabolism by gut microbes

From the last few years, researchers started thinking in this direction also. As all of the xenobiotics, we took daily either directly (pharmaceutical drugs) or indirectly (from our regular diet) first encountered by microbes residing in our gut. In our gut, microbes are present in larger number in comparison to our own cells so it is obvious that they might have key enzymes also for the metabolism of xenobiotics. This early stage metabolism (before reaching into blood circulation) might affect overall metabolism by the host. Researcher in this field Carmody and Turnbaugh described in detail that microbial enzymes are involved in the metabolism of xenobiotics compounds either directly or indirectly (active or deactivate host enzyme via microbial metabolite). You can read this paper from the following link http://www.jci.org/articles/view/72335. They highlighted till now known examples of xenobiotics metabolism by gut microbes and effect of this metabolism on overall efficacy and toxicity of the compounds. In 1998 around two decades ago first time Okuda et al discussed probable mechanism responsible for the deaths of eighteen patients treated with soravudine and oral 5-fluorouracil prodrugs. You can read this paper from the following link http://www.ncbi.nlm.nih.gov/pubmed/9808711. They addressed how the metabolite produced by the gut microbes involved in the inhibition of key enzyme (hepatic dihydropyrimidine dehydrogenase) that are responsible for maintaining the level of 5-FU. This is the only one example which I have mentioned here, there could be several others, we have a very limited information of microbial metabolism of xenobiotics. But, now, because of next generation sequencing  and metagenomics, the understanding towards the microbial world is increasing day by day. Now a days this microbial metabolism part is important to study in order to develop better pharmaceuticals (increased efficacy and/or decrease toxicity). Hopefully, Recently completed project like Human microbiome project (http://hmpdacc.org/) could provide new directions to this field.
Here I am sharing a recently published note on “Why xenobiotics, foreign chemicals to our bodies, are the next frontier in health and disease.”

http://www.theglobeandmail.com/life/health-and-fitness/health-advisor/why-xenobiotics-foreign-chemicals-to-our-bodies-are-the-next-frontier-in-health-and-disease/article24302600/

Role of Gut Microbiome in the Regulation of Host Metabolism

Microbes are integral part of our routine life as they live in a constant association with the host to regulate the metabolism. Collection of microbes present at the epithelial surface and cavities of our body such as stomach are known as human associated microbiome/microflora. Bacteria are the predominant part of the microflora, however, some amount of virus, fungi and protozoans also present. The most complex community ever studied is the human distal gut microbiome, which contained 1,000 different microbial species across human populations. These microbes contain millions of different genes, largely more than the total number of host genes. Now a days, several research groups are working on how gut dysbiosis is related with the various metabolic disorders such as obesity, diabetes, IBD, cancer etc. This is quite interesting filed to know how microbes are interacting with each other or with the host in order to attain healthy or diseased phenotype. Since, the microflora is very complex and dynamic, and it is known to have a profound influence on the human metabolism. This vast diversity of the human-associated microbes poses a big challenge in the field of medical science. 

Recently, an interesting report published on the following topic "Modern Life Depletes Your Gut Microbes in a Number of Different Ways". 

http://articles.mercola.com/sites/articles/archive/2015/05/06/modern-life-depletes-gut-microbes.aspx 

Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences

Time to analyse your data using woods "a functional annotator and classifier of genomic and metagenomic sequences"

A recent publication out from our lab. Please explore and write back to (ashok@iiserb.ac.in) in case of any problem. Comments are welcome. 

Available at: http://www.ncbi.nlm.nih.gov/pubmed/25863333

http://metagenomics.iiserb.ac.in/woods/index.php

Databases and tools for understanding the xenobiotics metabolism

Human physiology influenced by human microbiome in several ways, mainly via impact of human gut microbes on the metabolism of xenobiotic compounds. These interactions are quite important as they are very significant in order to improve overall efficacy of therapeutic compounds, but microbial metabolism of xenobiotics still remains unexplored. Here, I have provided the link of important databases and tools for biotransformation/degradation of xenobiotics. 

1. The Transformer database: biotransformation of xenobiotics

Available at: http://bioinformatics.charite.de/transformer/index.php?site=faq

http://nar.oxfordjournals.org/content/42/D1/D1113.full

2. UM-BBD -- University of Minnesota Biocatalysis/Biodegradation Database

Available at: http://www.hsls.pitt.edu/obrc/index.php?page=URL1100188151

http://www.ncbi.nlm.nih.gov/pubmed/19767608

http://www.microbialinformaticsj.com/content/2/1/1   

3. Eawag: Biocatalytic/Biodegradation database, to identify the metabolic enzyme for candidate drug

http://eawag-bbd.ethz.ch/predict/

4. Gut Pharmacomicrobiomics: the tip of an iceberg of complex interactions between drugs and gut-associated microbes

http://pharmacomicrobiomics.com/

http://www.gutpathogens.com/content/4/1/16

5. DrugBank 4.0: shedding new light on drug metabolism

http://www.drugbank.ca/

http://nar.oxfordjournals.org/content/early/2013/11/06/nar.gkt1068.full

Gut Pharmacomicrobiomics:

To study the influence of gut microbes on xenobiotics metabolism.

Pharmacomicrobiomics database:

The PharmacoMicrobiomics web portal is a part of an initiative to explore the interactions between human-associated microbes (human microbiome) and drugs by building a knowledgebase that allows interested students and investigators "to predict the behavior of untested members of drug classes or unstudied microbial species, and to design laboratory experiments for testing these predictions.

Available at: http://www.biomedcentral.com/1471-2105/12/S7/A10

http://pharmacomicrobiomics.com/

Composition based methods for taxonomic classification

Taxonomic classification of 16S rRNA or metagenomic sequencing reads is one of the most important steps in order to understand the diversity of microbes within a microbial community. Methods for taxonomic classification have been divided into two major categories, based on their algorithms, composition based methods and similarity-based methods. Here, I have discussed in detail about composition based methods. Most probably in my next post, I would like to focus on similarity-based approaches. 

1. TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences.

ETRA provides a statistical analysis of tetranucleotide usage patterns in genomic fragments, either via a web-service or a stand-alone program.

Available at: http://www.megx.net/tetra

http://www.biomedcentral.com/1471-2105/5/163

2. PhyloPathia: Accurate phylogenetic classification of variable-length DNA fragments.

PhyloPythia, a composition-based classifier that combines higher-level generic clades from a set of 340 completed genomes with sample-derived population models. Extensive analyses on synthetic and real metagenome data sets showed that PhyloPythia allows the accurate classification of most sequence fragments across all considered taxonomic ranks, even for unknown organisms.
Available at: http://cbcsrv.watson.ibm.com/phylopythia.html

http://www.nature.com/nmeth/journal/v4/n1/full/nmeth976.html

3. PhyloPathiaS: The PhyloPythiaS Web Server for Taxonomic Assignment of Metagenome Sequences.

PhyloPythiaS is a fast and accurate sequence composition-based classifier that utilizes the hierarchical relationships between clade. PhyloPythiaS is freely available for non-commercial users and can be installed on a Linux-based machine.

Available at: http://phylopythias.cs.uni-duesseldorf.de/index.php?phase=wait

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0038581

4. TACOA: Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach

The classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning. It is an accurate multi-class taxonomic classifier for environmental genomic fragments. TACOA can predict with high reliability the taxonomic origin of genomic fragments as short as 800 bp.

Available at:  http://www.cebitec.uni-bielefeld.de/brf/tacoa/tacoa.html 

http://www.biomedcentral.com/1471-2105/10/56#B20

5. RAIphy: Phylogenetic classification of metagenomics samples using iterative refinement of relative abundance index profiles

RAIphy is a composition-based semisupervised binning algorithm that uses a novel sequence similarity metric with iterative refinement of taxonomic models and functions effectively. RAIphy has been implemented as a simple, compact standalone desktop application, which is fast compared to similarity-search-based applications. While achieving competitive binning accuracies for the DNA sequencing read length range (100-1000 bp), the method also performs accurately for longer environmental contigs.

Available at: http://bioinfo.unl.edu/raiphy.php

http://www.biomedcentral.com/1471-2105/12/41

6. NBC: the Naive Bayes Classification tool webserver for taxonomic classification of metagenomic reads.

A webserver that implements the naïve Bayes classifier (NBC) to classify all metagenomic reads to their best taxonomic match. Results indicate that NBC can assign next-generation sequencing reads to their taxonomic classification and can find significant populations of genera that other classifiers may miss.

Available at: http://nbc.ece.drexel.edu

http://www.ncbi.nlm.nih.gov/pubmed/21062764

7. Phymm and PhymmBL: Metagenomic Phylogenetic Classification with Interpolated Markov Models

Phymm, a classifier for metagenomic data, that has been trained on 539 complete, curated genomes and can accurately classify reads as short as 100 bp, representing a substantial leap forward over previous composition-based classification methods. They also describe how combining Phymm with sequence alignment algorithms, further improves accuracy.

Available at: http://www.cbcb.umd.edu/software/phymm/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2762791/

8. GSTaxClassifier: a genomic signature based taxonomic classifier for metagenomic data analysis.

GSTaxClassifier takes input nucleotide sequences and using a modified Bayesian model evaluates the genomic signatures between metagenomic query sequences and reference genome databases. The simulation studies of a numerical data sets showed that GSTaxClassifier could serve as a useful program for metagenomics studies.

Available at: http://helix2.biotech.ufl.edu:26878/metagenomics/

http://www.ncbi.nlm.nih.gov/pubmed/20011152

9. SPHINX: an algorithm for taxonomic binning of metagenomic sequences.

A hybrid binning approach (SPHINX) that achieves high binning efficiency by utilizing the principles of both 'composition'- and 'alignment'-based binning algorithms.

Available at: http://metagenomics.atc.tcs.com/SPHINX/

http://www.ncbi.nlm.nih.gov/pubmed/21030462

10. TAC-ELM: Metagenomic taxonomic classification using extreme learning machines.

A new sequence composition-based taxonomic classifier using extreme learning machines referred to as TAC-ELM for metagenomic analysis. TAC-ELM uses the framework of extreme learning machines to quickly and accurately learn the weights for a neural network model. The input features consist of GC content and oligonucleotides.

Available at: http://www.cs.gmu.edu/~mlbio/TAC-ELM/

http://www.ncbi.nlm.nih.gov/pubmed/22849369

11. AKE - the Accelerated k-mer Exploration web-tool for rapid taxonomic classification and visualization

Acceleration in AKE’s taxonomic assignments is achieved by a special machine learning architecture, which is well suited to model data collections that are intrinsically hierarchical. 

Available at: https://ani.cebitec.uni-bielefeld.de/ake/login.html

http://www.biomedcentral.com/1471-2105/15/384

12. TAXSOM: Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics.

Biodiversity is usually targeted by classifying 16S ribosomal RNA genes, while metagenomic approaches target metabolic genes. However, both approaches remain isolated, as long as the taxonomic and functional information cannot be interrelated. Techniques like self-organizing maps (SOMs) have been applied to cluster metagenomes into taxon-specific bins in order to link biodiversity with functions.

Available at: http://soma.arb-silva.de/

http://www.ncbi.nlm.nih.gov/pubmed/21160538

13. Kraken: ultrafast metagenomic sequence classification using exact alignments

Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. 

Available at: http://ccb.jhu.edu/software/kraken/

http://genomebiology.com/2014/15/3/R46

14. RDP Classifier: Naive Bayesian classifier for rapid assignment of rRNAsequences into the new bacterial taxonomy.

The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryote. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene. The RDPClassifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences.

Available at: http://rdp.cme.msu.edu/

http://www.ncbi.nlm.nih.gov/pmc/articles/pmid/17586664/

15. 16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level.

Available at: http://metagenomics.iiserb.ac.in/16Sclassifier

http://metabiosys.iiserb.ac.in/16Sclassifier/application.php

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0116106

Tools and methods for Flux Balance Analysis

Here I have discussed various tools and methods for Flux Balance Analysis (FBA) of metabolic networks.

1. OptFlux: an open-source software platform for in silico metabolic engineering by rocha et. al.

OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes.

Available at: http://www.optflux.org/

2. MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis by lee et .al.

MetaFluxNet is a program package for managing information on the metabolic reaction network and for quantitatively analyzing metabolic fluxes in an interactive and customized way. It allows users to interpret and examine metabolic behavior in response to genetic and/or environmental modifications. As a result, quantitative in silico simulations of metabolic pathways can be carried out to understand the metabolic status and to design the metabolic engineering strategies. The main features of the program include a well-developed model construction environment, user-friendly interface for metabolic flux analysis (MFA), comparative MFA of strains having different genotypes under various environmental conditions, and automated pathway layout creation.

Availbale at: http://metafluxnet.kaist.ac.kr/

3. BioOpt:

BioOpt is a software application running on Windows command prompt. The program focuses on the flux balance analysis, using linear programming as the mathematical support. Given a biological system model, which includes a set of metabolic reactions, the program is able to calculate all internal mass balance fluxes, reduced costs and shadow prices depending on the constraints and objective defined by the user. Running BioOpt with different parameters allows the user to obtain several kinds of outputs that can help in the analysis of the system.

Available at: http://129.16.106.142/tools.php?c=bioopt

4. SurreyFBA: A command line tool and graphics user interface for constraint based modelling of genome scale metabolic reaction networks.

SurreyFBA, which provides constraint-based simulations and network map visualization in a free, stand-alone software. It is based on a command line interface to the GLPK solver distributed as binary and source code for the three major operating systems. SurreyFBA includes JyMet, a graphics user interface allowing spreadsheet based model presentation, visualization of numerical results on metabolic networks represented in the Petri net convention, as well as in charts and plots.

Available at: http://sysbio3.fhms.surrey.ac.uk/SurreyFBA.zip

5. FASIMU: FBA simulation software for metabolomics, fluxomics, and biotechnology

FASIMU, a command line oriented software implementing the most frequently applied FBA algorithms. Moreover, it offers the first freely available implementation of (i) weighted flux minimization, (ii) fitness maximization for partially inhibited enzymes, and (iii) the concentration-based thermodynamic feasibility constraint. It allows heterogenous computation series suited for network pruning, leak analysis, FVA, and systematic probing of metabolic objectives for network curation controlled by an intuitive description file. The metabolic network can be supplied in SBML, CellNetAnalyzer, and plain text format. FASIMU uses the optimization capabilities of free (lp solve and GLPK) and commercial solvers (CPLEX, LINDO). The results can be visualized in Cytoscape or BiNA using newly developed plugins.

Available at: http://www.bioinformatics.org/fasimu/downloads/

6. GEMSiRV: A software platform for GEnome-scale Metabolic model Simulation, Reconstruction and Visualization

GEMSiRV comes with downloadable, ready-to-use public-domain metabolic models, reference metabolite/reaction databases, and metabolic network maps, all of which can be input into GEMSiRV as the starting materials for network construction or simulation analyses. Furthermore, all of the GEMSiRV-generated metabolic models and analysis results, including projects in progress, can be easily exchanged in the research community. GEMSiRV is a powerful integrative resource that may facilitate the development of systems biology studies.

Available at: http://sb.nhri.org.tw/GEMSiRV/en/GEMSiRV

7. CellNetAnalyzer: Structural and Functional Analysis of Cellular Networks
CellNetAnalyzer (CNA) is a MATLAB toolbox providing a graphical user interface and various (partially unique) computational methods and algorithms for exploring structural and functional properties of metabolic, signaling, and regulatory networks.

Metabolic networks are formalized and analyzed by stoichiometric and constraint-based modeling techniques, including flux balance analysis (FBA), metabolic flux analysis, elementary-modes analysis, minimal cut set analysis, and many more. Several algorithms are provided for computational strain design / metabolic engineering.

Available at: http://www2.mpi-magdeburg.mpg.de/projects/cna/cna.html

8. SNA--a toolbox for the stoichiometric analysis of metabolic networks.

SNA is a Mathematica toolbox for stoichiometric network analysis. Among other things, it supports flux balance analysis and the enumeration of the elementary vectors of the flux and the conversion cone.

Available at: http://www.webcitation.org/mainframe.php

9. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox.

The COBRA Toolbox is a set of MATLAB scripts for constraint-based modeling that are run from within the MATLAB environment.  These scripts depend on external libraries for reading and writing SBML-formatted models and for simulations.  Additionally, some functions may require additional MATLAB Toolboxes that must be purchased from the MathWorks.

Available at: http://opencobra.sourceforge.net/openCOBRA/Install.html

10. FBA-SimVis: interactive visualization of constraint-based metabolic models.

FBA-SimVis is a VANTED Plug-in for the constraint-based analysis of metabolic models with special focus on the dynamic and visual exploration of metabolic flux data resulting from model analysis. The program provides a user-friendly environment for model reconstruction, constraint-based model analysis and dynamic visualisation of the simulation results. With the ability to quantitatively analyse metabolic fluxes in an interactive and visual manner, FBA-SimVis supports a comprehensive understanding of constraint-based metabolic flux models in both overview and detail.

Available at: http://fbasimvis.ipk-gatersleben.de/

11. MetaFlux: Construction and completion of flux balance models from pathway databases.

A multiple gap-filling method to accelerate the development of FBA models using a new tool, called MetaFlux, based on mixed integer linear programming (MILP). he method suggests corrections to the sets of reactions, biomass metabolites, nutrients and secretions. The method generates FBA models directly from Pathway/Genome Databases. Thus, FBA models developed in this framework are easily queried and visualized using the Pathway Tools software.

Available at: http://biocyc.org/download.shtml

12. CycSim—an online tool for exploring and experimenting with genome-scale metabolic models

CycSim is a web application dedicated to in silico experiments with genome-scale metabolic models coupled to the exploration of knowledge from BioCyc and KEGG. Specifically, CycSim supports the design of knockout experiments: simulation of growth phenotypes of single or multiple gene deletions mutants on specified media, comparison of these predictions with experimental phenotypes and direct visualization of both on metabolic maps. The web interface is designed for simplicity, putting constraint-based modelling techniques within easier reach of biologists. CycSim also functions as an online repository of genome-scale metabolic models.

Available at: http://www.genoscope.cns.fr/cycsim/org.nemostudio.web.gwt.App/App.html   (Standalone version not available)

13. WEbcoli: an interactive and asynchronous web application for in silico design and analysis of genome-scale E.coli model.

WEbcoli is a WEb application for in silico designing, analyzing and engineering Escherichia coli metabolism. It is devised and implemented using advanced web technologies, thereby leading to enhanced usability and dynamic web accessibility. As a main feature, the WEbcoli system provides a user-friendly rich web interface, allowing users to virtually design and synthesize mutant strains derived from the genome-scale wild-type E.coli model and to customize pathways of interest through a graph editor. In addition, constraints-based flux analysis can be conducted for quantifying metabolic fluxes and charactering the physiological and metabolic states under various genetic and/or environmental conditions.

Available at: http://webcoli.org   (Standalone version not available)

14. RAST/Model SEED genome-scale metabolic reconstruction pipeline:

RAST and the Model SEED framework were developed as a means of automatically producing annotations and draft genome-scale metabolic models. They break down the model reconstruction process into eight steps: submitting a genome sequence to RAST, annotating the genome, curating the annotation, submitting the annotation to Model SEED, reconstructing the core model, generating the draft biomass reaction, auto-completing the model, and curating the model. Each of these eight steps is documented in detail.

Availbale at: http://seed-viewer.theseed.org/seedviewer.cgi?page=ModelView (Standalone version not available)

15. MicrobesFlux: a web platform for drafting metabolic models from the KEGG database:

MicrobesFlux is an installation-free and open-source platform that enables biologists without prior programming knowledge to develop metabolic models for annotated microorganisms in the KEGG database. Our system facilitates users to reconstruct metabolic networks of organisms based on experimental information. Through human-computer interaction, MicrobesFlux provides users with reasonable predictions of microbial metabolism via flux balance analysis. This prototype platform can be a springboard for advanced and broad-scope modeling of complex biological systems by integrating other “omics” data or 13 C- metabolic flux analysis results. 

Available at: http://tanglab.engineering.wustl.edu/static/MicrobesFlux.html  (Standalone version not available)

16. FAME the Flux Analysis and Modeling Environment:

The Flux Analysis and Modeling Environment (FAME) is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps.

Available at: http://f-a-m-e.org/ajax/page1.php (Standalone version not available)

Databases, Software and Tools for metabolic pathway/network reconstructions

Here I am providing the details about the available tools and methods for the metabolic pathway reconstruction. 

1. The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model   for Penicillium chrysogenum:

RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. 

Available at: http://129.16.106.142/downloads.php

2. RAST/Model SEED genome-scale metabolic reconstruction pipeline:

RAST and the Model SEED framework were developed as a means of automatically producing annotations and draft genome-scale metabolic models. They break down the model reconstruction process into eight steps: submitting a genome sequence to RAST, annotating the genome, curating the annotation, submitting the annotation to Model SEED, reconstructing the core model, generating the draft biomass reaction, auto-completing the model, and curating the model. Each of these eight steps is documented in detail.

Availbale at: http://seed-viewer.theseed.org/seedviewer.cgi?page=ModelView (Standalone version not available)

3. MicrobesFlux: a web platform for drafting metabolic models from the KEGG database:

MicrobesFlux is an installation-free and open-source platform that enables biologists without prior programming knowledge to develop metabolic models for annotated microorganisms in the KEGG database. Our system facilitates users to reconstruct metabolic networks of organisms based on experimental information. Through human-computer interaction, MicrobesFlux provides users with reasonable predictions of microbial metabolism via flux balance analysis. This prototype platform can be a springboard for advanced and broad-scope modeling of complex biological systems by integrating other “omics” data or 13 C- metabolic flux analysis results. 

Available at: http://tanglab.engineering.wustl.edu/static/MicrobesFlux.html  (Standalone version not available)

4. FAME the Flux Analysis and Modeling Environment:

The Flux Analysis and Modeling Environment (FAME) is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps.

Available at: http://f-a-m-e.org/ajax/page1.php (Standalone version not available)

5. Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology:

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism.

Available at: http://bioinformatics.ai.sri.com/ptools/

6. BioModels Database:

BioModels Database serves as a huge repository of computational models of genomes and different biological processes. It hosts models described in peer-reviewed scientific literature and automatically generated models from pathway resources (Path2Models). Models collected from literature are manually curated and semantically enriched with cross-references from external data resources. The database resource allows scientific community to store, search and retrieve mathematical models of their interest. In addition, features such as generation of sub-models, online simulation, conversion of models into different representational formats, and programmatic access via web services, are also provided.

Available at: http://www.ebi.ac.uk/biomodels-main/

7. GEMSiRV: A software platform for GEnome-scale Metabolic model Simulation, Reconstruction and Visualization

GEMSiRV comes with downloadable, ready-to-use public-domain metabolic models, reference metabolite/reaction databases, and metabolic network maps, all of which can be input into GEMSiRV as the starting materials for network construction or simulation analyses. Furthermore, all of the GEMSiRV-generated metabolic models and analysis results, including projects in progress, can be easily exchanged in the research community. GEMSiRV is a powerful integrative resource that may facilitate the development of systems biology studies.

Available at: http://sb.nhri.org.tw/GEMSiRV/en/GEMSiRV

8. Metashark: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella.

The metabolic SearcH And Reconstruction Kit (metaSHARK) is a new fully automated software package for the detection of enzyme-encoding genes within unannotated genome data and their visualization in the context of the surrounding metabolic network.

Available at:

9. The SuBliMinaL Toolbox:  automating steps in the reconstruction of metabolic networks. 

The SuBliMinaL Toolbox (http://www.mcisb.org/subliminal/) facilitates the reconstruction process by providing a number of independent modules to perform common tasks, such as generating draft reconstructions, determining metabolite protonation state, mass and charge balancing reactions, suggesting intracellular compartmentalisation, adding transport reactions and a biomass function, and formatting the reconstruction to be used in third-party analysis packages. 

Available at: http://www.mcisb.org/resources/subliminal/