May 13, 2010

Pathway discovery in metabolic networks by subgraph extraction

Filed under: Bioinformatics — Biointelligence: Education,Training & Consultancy Services @ 9:30 am
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Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds, etc.). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to extract relevant pathways from metabolic networks. Although these approaches have been adapted to metabolic networks, they are generic enough to be adjusted to other biological networks as well.

Results: We comparatively evaluated seven sub-network extraction approaches on 71 known metabolic pathways from Saccharomyces cerevisiae and a metabolic network obtained from MetaCyc. The best performing approach is a novel hybrid strategy, which combines a random walk-based reduction of the graph with a shortest paths-based algorithm, and which recovers the reference pathways with an accuracy of 77%.

Availability: Most of the presented algorithms are available as part of the network analysis tool set (NeAT). The kWalks method is released under the GPL3 license.

December 14, 2009

Applications of Systems Biology in Drug Discovery

Filed under: Bioinformatics,Chemoinformatics,Systems Biology — Biointelligence: Education,Training & Consultancy Services @ 4:33 am
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Till date we have made a lot of posts on Systems Biology, its applications and it scope. Indeed, Systems Biology has brought a big revolution in cell biology and pathway analysis. When seen in combination with treatment of diseases and drug discovery, it proves even more handy. Here we discuss Systems Biology in combination with drug discovery.

The goal of modern systems biology is to understand physiology and disease from the level of molecular pathways, regulatory networks, cells, tissues, organs and ultimately the whole organism. As currently employed, the term ‘systems biology’ encompasses many different approaches and models for probing and understanding biological complexity, and studies of many organisms from bacteria to man. Much of the academic focus is on developing fundamental computational and informatics tools required to integrate large amounts of reductionist data (global gene expression, proteomic and metabolomic data) into models of regulatory networks and cell behavior. Because biological complexity is an exponential function of the number of system components and the interactions between them, and escalates at each additional level of organization.

There are basically three advances in the practical applications of systems biology to drug discovery. These are:

1. Informatic integration of ‘omics’ data sets (a bottom-up approach)

Omics approaches to systems biology focus on the building blocks of complex systems (genes, proteins and metabolites). These approaches have been adopted wholeheartedly by the drug industry to complement traditional approaches to target identification and validation, for generating hypotheses and for experimental analysis in traditional hypothesis-based methods.

2. Computer modeling of disease or organ system physiology from cell and organ response level information available in the literature (a top-down approach to target selection, clinical indication and clinical trial design).
The goal of modeling in systems biology is to provide a framework for hypothesis generation and prediction based on in silico simulation of human disease biology across the multiple distance and time scales of an organism. More detailed understanding of the systems behavior of intercellular signaling pathways, such as the identification of key nodes or regulatory points in networks or better understanding of crosstalk between pathways, can also help predict drug target effects and their translation to organ and organism level physiology.

3.  The use of complex human cell systems themselves to interpret and predict the biological activities of drugs and gene targets (a direct experimental approach to cataloguing complex disease-relevant biological responses).

Pathway modeling as yet remains too disconnected from systemic disease biology to have a significant impact on drug discovery. Top-down modeling at the cell-to-organ and organism scale shows promise, but is extremely dependent on contextual cell response data. Moreover, to bridge the gap between omics and modeling, we need to collect a different type of cell biology data—data that incorporate the complexity and emergent properties of cell regulatory systems and yet ideally are reproducible and amenable to storing in databases, sharing and quantitative analysis.

This is how Systems Biology has aided in Drug Discovery Research and paved its path to cure many vital diseases.

Read our other posts on Systems Biology –

November 12, 2009

KEGGConverter: Tool for modelling Metabolic Networks

Filed under: Bioinformatics,Systems Biology — Biointelligence: Education,Training & Consultancy Services @ 7:47 am
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The Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY database is a valuable comprehensive collection of manually curated pathway maps for metabolism, genetic information processing and other functions. It is an integrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information as shown below. Genomic and chemical information represents the molecular building blocks of life in the genomic and chemical spaces, respectively, and systems information represents functional aspects of the biological systems, such as the cell and the organism, that are built from the building blocks. KEGG has been widely used as a reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.

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 order to construct such metabolic pathways, the KEGGConvertor has been implemented. It is a tool implemented in JAVA. KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. It permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy.
KEGG Convertor is available here:

October 7, 2009

BioSytems: A New Database for Biological Systems

Filed under: Bioinformatics,Systems Biology — Biointelligence: Education,Training & Consultancy Services @ 1:08 pm
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Biological Systems are basically formed when a group of molecules interact together. A type of Biological Systems is a biological pathway. Basically a biological pathway comparises of interacting genes, proteins, and small molecules.An understanding of the components, products, and biological effects of biosystems can lead to better understanding of biological processes in normal and disease states, elucidation of possible drug effects and side effects, and other insights to complex processes that have implications for health and medicine.

NCBI has designed a BioSystems database which has a centralized access to existing pathway databases.

Current source databases supported by Biosystems database are:

1. KEGG: Kyoto Encyclopedia of Genes and Genomes ( by the Kanehisa Laboratory of the Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.

2. BioCyc ( is a collection of organism-specific pathway/genome databases (PGDBs), and the EcoCyc ( subset of BioCyc is included in the NCBI BioSystems database.

3. Reactome ( is a curated knowledge base of biological pathways, and the human subset of Reactome is included in the NCBI BioSystems database. More about the Biosystems database can be read here:

September 24, 2009

Pathway Databases – A broader view

Filed under: Bioinformatics,Systems Biology — Biointelligence: Education,Training & Consultancy Services @ 7:58 am
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Studying Reactome, actually led me to explore some more databases of pathways and reactions. While browing I eventually landed on a paper “Pathway databases and tools for their exploitation: benefits, current limitations and challenges” authored by Anna Bauer-Mehren, Laura I Furlong & Ferran Sanz. So, my todays post gives an abstract of what this paper is talking about.

Cell signalling studies have been going on from over a decade. This process basically refers to the biochemical processes using which cells respond to cues in their internal or external environment. This eventually led to the creation of chain of reactions and development of databases to store them in a compiled manner. Several databases containing information on cell signalling pathways have now been developed in conjunction with methodologies to access and analyse the data. At present, there are several repositories of information on cell signalling pathways that cover a wide range of signal transduction mechanisms and include high quality data in terms of annotation and cross references to biological databases.

Some of the online pathway databases have been nicely listed here:

This table basically lists Reactome, KEGG, Wikipathways, Nature interaction databases, pathway commons and many more….

The paper also explains the main standards for representation of biological networks, BioPAX and SBML. Furthermore, the advantages and drawbacks of current methods for pathway retrieval and integration, using the EGFR signalling as an illustrative example, have been discussed.

The paper is available here:

September 23, 2009

Reactome: A database for pathways and Reactions

Filed under: Systems Biology — Biointelligence: Education,Training & Consultancy Services @ 7:20 am
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While studying about Biological pathways and databases, I landed on the home the Reactome Database, Indeed its a great creation. Here is a small introduction to “Reactome”.

Reactome is a free, online, open-source, curated resource of core pathways and reactions in human biology.It is a database which is maintained by the Reactome editorial staff and cross-referenced to the NCBI Entrez Gene, Ensembl and UniProt databases, the UCSC and HapMap Genome Browsers, the KEGG Compound and ChEBI small molecule databases, PubMed, and GO.curated human data are used to infer orthologous events in 22 non-human species including mouse, rat, chicken, puffer fish, worm, fly, yeast, two plants and E.coli.

The Reactome website ( can be browsed like an online textbook. The website’s front page features a large ‘reaction map’ that summarizes all of the currently curated or inferred pathways, and a table of contents that describes each of the top-level pathways in the database. In the reaction map, each reaction is represented as a small arrow, and arrows are joined end to end to indicate that the output of one reaction becomes the input of the next. The reactions are organized in distinctive patterns to allow researchers to become familiar with the different parts of the reaction network.

Here is a article which talk about Reactome in detail:

Reactome can be accessed from here:

Reactome also hosts some tools for data analysis. These are Skypainter and Boiomart. Most probably, my next post would be on these tools. So, keep visiting…!!!