Canonical Pathways Collection

Pathway diagrams from PubMed and World Wide Web contain valuable highly curated information difficult to reach without specialized tools. There is currently no search engine or tool that can analyse pathway images, extract their pathway components (genes, proteins, cells, organs, etc.), and indicate their relationships.

We present a resource of pathway diagrams retrieved from article and web-page images through optical character recognition (OCR), in conjunction with data-mining and data integration methods. The recognized pathways are integrated into the BiologicalNetworks research environment linking them to a wealth of data available in the IntegromeDB knowledgebase, which integrates data from >100 public data sources and the biomedical literature. Multiple search and analytical tools are available that allow the recognized cellular pathways, molecular networks and cell/tissue/organ diagrams to be studied in the context of integrated knowledge, experimental data and the literature.

We scanned a collection of >150 journals, 50,000 articles, and 150,000 figures (new articles are downloaded daily) available in PubMed Central and World Wide Web. The downloaded figures are stored on a remote server and the Lucene open-source search engine is used to index, retrieve, and rank the image text descriptions (using the default statistical ranking). In case of publication, the image description is the image legend, whereas in the case of a web page, the specifically designed algorithm retrieves the most appropriate description from the web page text surrounding the image. Image publication date and source journal are stored as separate fields that can also be used to sort the results. The constantly growing ‘Imaging Pathways’ repository currently contains 1,025 pathways, which is more than in any existing public repositories, e.g. BioCarta contains 354 and the KEGG contains 345 reference pathways. Taking into account that BiologicalNetworks’ back-end database IntegromeDB integrates Reactome, KEGG, BioCarta, NCI-Nature pathways, WikiPathways and HumanCyc this makes the BiologicalNetworks the richest compendium of currently available pathways.

Biological Articles:

BiologicalNetworks Software

BiologicalNetworks is a Systems Biology software platform for biological pathways analysis, querying and visualization of gene regulation and protein interaction networks, metabolic and signaling pathways. It is equipped with filtering and visualization tools, to provide high quality, easily understood scientific presentation of your pathway analysis results.

The system includes a general-purpose scalable warehouse of biological information, which integrates over 20 curated and publicly contributed data sources, biological experimental and PubMed data for the 8 representative genomes (S. cerevisiae, D.melanogaster, etc.). BiologicalNetworks is also supported with curated pathways from a number of public databases like KEGG and different scientific studies.

BiologicalNetworks identifies relationships among genes, proteins, small molecules and other cellular objects, and draws pathways, linked to the original sources of information.

The system is equipped with enhanced graph manipulation and a query language, data mining and filtering tools, a storage mechanism and a generic data-importing mechanism through schema-mapping.


Pathways Analysis

  • Import and export your data
  • Create, save, edit your pathways and produce high quality diagrams for your publications
  • Meta-Network (network in which a node, Meta-Node, itself has its internal network structure): multi-scale visualization of bio-networks, ideal for network of functional modules.
  • Find relationships among genes/proteins, cell processes and other objects
  • Optimize the view by filtering, pathway expansion, and protein classification.
  • Perform graphic drawing and layout optimization

Navigation and Tools

  • Build Molecular Interaction Networks for gene lists imported from microarray and other experiments
  • Specify and visualize upstream and downstream events
  • Find a path between two or several molecules
  • Detect common targets or/and regulators for a group of proteins.

Pathways and Interaction Networks from public Databases and Literature

  • Browse pathway database system compiled from over 20 databases
  • Access more than 140,000 facts of regulation, interaction and modification
  • Get the original sentence or paper abstract to validate the facts of interaction and biological phenomenon.

BiologicalNetworks pathways analysis software supports Windows® 2000 and XP, Linux and Macintosh operating systems.


Biological Articles:

BiologicalNetworks v. 2.0 betta

BiologicalNetworks v. 2.0 betta

01 April 2010

  • Several additional functions and search options introduced into BuildPathwayWizard for extensive network navigation.


  • In addition to GeneOntology annotations multiple additional ontology annotations were added:
    • Diseases
    • Cell Types
    • Tissues
    • Mammal Phenotypes
    • Human Anatomy
    • Mouse Anatomy
    • KEGG Pathways
    • Chemicals

Read More

Biological Articles:

BiologicalNetworks Features

BiologicalNetworks Features

  1. Build Pathways
  2. File Formats
  3. Explore Pathways
  4. Visual Data Abstraction
  5. Network Analysis and Statistics
  6. Import Gene/Protein Lists
  7. Use your Microarray Gene Expression Data for Pathway Analisys

Build Pathways

  • Assemble your pathway using multiple search algorithms:
    expend pathways,
    find shortest path between two genes or all shortest paths covering group of genes,
    find only direct interactions,
    find common targets or find common regulators,
    find intersections with known curated pathways (for example with KEGG pathways)
  • Use filtering to include in your pathway only specific types of biological objects, such as genes, proteins, complexes, small molecules, cellular processes and others.
  • Use filtering to include in your pathway only specific types of biological interactions such as binding interactions, post-translational regulation, genetic interaction, expression regulation, enzymatic activity, molecular synthesis, transport, and others



File Formats

  • Export of your pathway in several text (SIF, GML, XML, tab delimited) based formats
  • SBML compatible: Level 1&2
  • Export of your pathway in several graphical based formats

Explore Pathways

  • Move, drag and drop biological components on the workspace, apply different intelligent layout algorithms, Change and customize the shape and color of visual components
  • Automatic graphical optimization allows large pathways to be resized for optimal viewing
  • Add, modify or redefine interactions to further validate associations
  • Add Notes and Annotation to the model workspace.
  • Cut, Copy, Paste and Delete biological components.
  • Undo and Redo operations. Up to 10 operations can be undone and redone using the icons in the toolbar.
  • Zoom in /out/fit and Zoom in selected region to investigate types of associations and reference source descriptions. This feature becomes useful when the model size becomes big.
  • Direct links to PubMed abstract and exact source sentence where findings are referenced
  • View a variety of preloaded curated common pathways such as MAPK, p53, apoptosis, Wnt, Cell Cycle and all KEGG pathways; or specific networks and pathways from different scientific studies.
  • Export publication quality graphics in a variety of file formats

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Visual Data Abstraction

  • Meta-Network, in which a node (Meta-Node) itself has its internal network structure, supports multi-scale visualization of bio-networks, ideal for network of functional modules.
  • Meta-Nodes and Meta-Edges make effective analysis of cellular processes and dealing with complexity better.
  • Double click the Meta-Nodes to collapse them to simplify your network. Double click the Meta-Nodes again recover original network.
  • Meta-Edges are automatically recalculated based on the connections of the nodes inside Meta-Nodes.

Network Analysis and Statistics

  • Network statistics
  • Finding cycles
  • Pathway Homology
  • Node and Connector Tables

Import Gene/Protein Lists

  • Import Lists of Genes, Proteins and other biological entities from Yeast, Fly
  • Supports multiple ID types including: Unigene IDs, Accession Numbers, LocusLink, Swiss-Prot, Affymetrix GeneChip IDs, Gene Names and Gene Symbols
  • Creates gene lists that are exportable for manipulation in other software packages

Use your Microarray Gene Expression Data for Pathway Analisys

  • Import your microarray gene expression data sets
  • Filter, Normalize, make Data Transformations with your expression data
  • Sort and search over your expression data
  • Apply different clustering methods to your experimental data.
  • Make clutering analysis in context of biological pathways.
  • Overlay Expression Experiment results onto an existing pathway diagram
  • Derive microarray expression correlation networks
  • Extract pathways from expression data.
  • Find up or down regulated genes through various biological states and identify key regulators or targets of interest
  • Build Pathways for selected expression values
  • Work with groups/clusters and pathways in conjunction with expression experiments.



Biological Articles:

Users Testimonials

David S. Reiner

Medical Specialist,

Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden

I wanted to personally thank you, for the completion of the first round of our analysis of in silico network generated in BiologicalNetworks of basal body proteins in Giardia lamblia.

Using proteomic data from isolated basal bodies and transcriptomic data from a Serial Analysis of Gene Expression (SAGE) sampled during various time points of the parasites in vitro lifecycle we generated a protein-protein interaction network between proteins encoded by Giardia genes upregulated during differentiation (encystation and excystation). The resulting network, containing 376 genes and 721 edges that are supported by at least two evidences:

  1. one evidence of experimentally verified protein-protein interaction from PathSys and

  2. exhibiting co-expression patterns.

Although the network analysis is still in an active state of flux, when completed it will provide a rich source of hypotheses for experimental validation.
This is especially important in the diplomonad Giardia lamblia, where conventional genetic methods of analysis are unavailable due its tetraploid cellular state.


Frances D. Gillin, Ph. D

Professor of Pathology Department of Pathology,

Division of Infectious Diseases Member, Center for Molecular Genetics, UCSD

Thank you very much for your assistance, it’s good to have the perspective of skilled systems biologists. We look forward to further refinements of the network data extracted from BiologicalNetworks system and continuing collaborations in the future.

Dr. John Rachlin,

Sanofi-Aventis / Scientific Computing Group

your software is very nice. I’m interested in using advanced functionalities available in PathSys.

Adam Godzik, PhD

Professor and Program Director,

Bioinformatics and Systems Biology Program Burnham Institute for Medical Research, Center for Research in Biological Systems (CRBS), La Jolla, USA

We were looking for a network visualization package that would allow us to use thumbnails of real 3D protein structures to be displayed as nodes in the network, then after zooming in the thumbnails should expand into full size protein structure visualization.
I think BiologicalNetworks greatly accomplishes this task!

Daniel Wong

The Centre d’Immunologie de Marseille-Luminy (CIML), Marseille, France
Marvellous piece of programming.

I am already loving it!


Victoria Perreau, Ph.D.

Neuroproteomics and Neurogenomics Platform, Melbourne, Australia

We have built a data base of all proteins/molecules known to physically interact with Amyloid or Abeta. The data base is curated by a group of 15 specialist scientist from the literature, and currently numbers over 150 interactions. Many more than are currently included in any of the other interaction databases. Importantly each interaction is annotated with many different fields of useful information.
We are also in the process of using Pathway studio to interrogate the interactions. Our reservations about the utility of pathway studio include its cost, and it appears less flexable for incorporation of the type of detail we wish to include. Ultimately we would like to be able to filter the interactions on characteristics such as binding region and cellular site of interaction.

As BiologicalNetworks and PathSys system enables the incorporation of both interaction type and interaction properties this is an optimal way to represent our database. And make it freely available for researchers in the field for biological discovery.
We appreciate your assistance in using the software and customising the edge annotations fields to allow us to mount the database. I believe the distribution of the database in this way would benefit both of our groups.

Yulia Manenkova, Ph.D

Research Scientist,

Diazyme Laboratories, La Jolla, CA

Biologists see the importance of looking at the circuitry rather than just simple wiring. BiologicalNetworks interactions are so much more powerful as they have directionality, show effect and mechanism of interaction all validated with papers one click away. During the past year, we have used BiologicalNetworks in analyzing our quantitative proteomic data on ischemic rodent brains. BiologicalNetworks allows us to efficiently handle multiple sets of high throughput proteomic data with statistics and visual presentation, as well as to identify pathways and networks that are unique to different ischemic conditions of the brain.

Thank you very much for developing this software! I am looking forward to having more features in this program.


Viveka Mayya

Graduate student

U Conn Health Center, Farmington, CT

I am student of Dr. Leslie Loew at U Conn Health Center, the home of Virtual Cell modeling environment. I am interested in using BiologicalNetworks due to a variety of features that it offers. For one of the utilities, I am building my own network from scratch with a lot of literature annotations. Secondly, I am loading proteins from the human database against which I can query in BiologicalNetworks to searched the PathSys that has about has about 10000 entries regarding human pathways for Src, a mammalian non-receptor tyrosine kinase, related pathways.

I also would like to thank BiologicalNetworks for the prompt changes in the software and customizing features I requested.

Charles Schmitt, Ph.D.

Senior Researcher – Data Mining Manager,

Collaboration Development for Biosciences,

Renaissance Computing Institute

My name is Charles Schmitt and I’m a researcher at the Renaissance Computing Institute working primarily in the Biosciences area. We are very interested in using and potentially developing some plugins for BiologicalNetworks and Pathsys. We hope to use BiologicalNetworks system and PathSys to integrate several researcher-curated versions (in SBML, PSI-MI, and Biopax) with protein-protein interaction data. We have the content, so importing the different formats is very important as well as export for sharing purposes. The import-edit-save-load some portion-export process should not lose content and maintaining the source is important feature of BiologicalNetworks comparing to other systems biology tools.

Ability to add new node, edge, and attribute types in order to represent new knowledge and to add instances with these new types to a graph and include them in subsequent queries and visualizations are very important features for gaining information in BiologicalNetworks. In terms of the APIs, the main thing that would be helpful is programmatic query for nodes and edges (based on ids, types, and attributes), expanding the graph (1 level deep without restrictions is fine), editing of the resulting graph in memory, and saving it back to the database. As course, this is getting into the whole future directions with ontologies, but having this type of ‘light-weight’ knowledge base capability would be a good step towards building a more comprehensive ontology-driven system.

I’m also expecting collaborative data integration, although this would be nice longer term.

Eugene Katruha, Ph.D

Research Scientist,

Chemical Diversity, La Jolla, CA

We see BiologcalNetworks as a further step into systems biology, a novel discipline which holds much promise for pharmaceutical and biochemical research. I would like to see a feature of connecting your metabolic and signaling pathways to biochemical data and drugs. It would be very helpful for our drug discovery studies to navigate from drug targets to respective pathways and genetic data.

Mario Harvey

Laval University, Quebec, Canada

We looked at several systems biology packages and found that BiologicalNetorks suited our needs the best. The system is flexible for customization, and the BiologicalNetworks team is open minded for collaborative development. We needed a flexible partner that was willing to integrate with our current microarray technology.

Maria Elena Ochagavia

Center of Genetic Engineering and Biotechnology, City of Havana, Cuba

We will be using BiologicalNetworks for a number of projects investigating the mechanisms of signal transduction downstream of GPCR and tyrosine kinase receptors. This software will help us to understand the complex physiological regulation of reproduction and glucose metabolism.

Matthew Suderman

Postdoctoral fellow

McGill Centre for Bioinformatics, Montreal, Québec, Canada

I noticed BiologicalNetworks advertises an SQL-like query language and provides a few examples. I am interested in using BiologicalNetworks query capabilities specifically using an SQL-like query language and query engine. The ability to type a query on such a broad diversity of biological data and get a quick answer seems amazing!

Alexander Karnowski

Postdoctoral fellow

Immunology Division, Institute of Medical Research, Victoria, Australia

I recently discovered Biological Networks and using it for the analysis of our array data. We are using the Illumina Sentrix-6 mouse beadarrays for mRNA expression profiling of murine lymphocytes. It’s very helpful to study our array data with the proper annotation and ability to navigate to the PubMed publications supporting the interaction data. We would like to further explore BiologicalNetworks functionalities and see more nice features implemented.


Andy Munkacsi,

Graduate Student,

Columbia University

I am trying to model genetic and physical interactions of a yeast genes using PathSys. There are less than a handful of genetic and physical interactions known for this gene. I have results of synthetic genetic array (SGA) experiments and gene chip experiments. I have looked at these, using the GUI Biological Networks. A combination of these data is likely more powerful than either one independently. These experiments were done when my gene was deleted; these results suggest some kind of interaction (either directly, downstream, upstream, or other) between my gene and these other genes, based on differences in growth in double mutants versus single mutants (SGA data) and differences in expression levels of genes in a strain lacking my gene versus that of a wild type strain. I am using PathSys to make sense of these results, to group these results in pathways or functions. I think PathSys is better than a program like Osprey, since BiologicalNetowkrs program can predict neighborhoods while Osprey relies on direct interactions.

In addition I would like to see some more features available in BiologicalNetworks: I would like to incorporate my data with the data existing in your databases.

Allyson Lister,

Research Associate

Centre for Integrated Systems Biology of Ageing and Nutrition,

Newcastle University, United Kingdom

I have read your PathSys paper, and decided to start using your system.

I am primarily interested in the glycolysis and other well characterized pathways for S.cerevisiae to study phosphofructokinase activities and PFK genes coding this enzyme. I am using the SBML files for pathways representation and exploring this data with BiologicalNetworks and would like to see more features on dynamical pathways modeling.

Svetlana Zhenilo, Ph.D

Research Scientist,

Laboratory of Genetic Engineering of Mammalian Cells,

Bioengineering Center, Russian Academy of Science, Moscow, Russia

We chose BiologicalNetworks as it has a comprehensive, detailed database of metabolism and signaling, and is also a one-stop shop for our systems analysis needs. Experimental data about methyl modification of DNA by bound proteins are inherently complex and multi-factorial. We also have the data from both human and model animals. In BiologicalNetworks, we can upload and compare all these data on the same networks and pathways and then analyze the networks with a variety of tools.

Jeanne A. Garbarino,

Insitute of Human Nutrition,

Columbia University, New York, NY

We evaluated BiologicalNetworks and decided that it was a good for our purposes of microarray data analysis in context of pathways. We have a series of microarray data sets that we are analyzing using the Biological Networks software. BiologicalNetworks enables us to generate global pathways and smaller targeted networks, and in addition, extending pathways and networks with BiologicalNetworks is a very attractive feature for gaining information.

Elena Fernandez Arenas

Facultad de Farmacia,

Universidad Complutense de Madrid, Madrid, Spain

I am using BiologicalNetwokrs software your software in order to analyze my Microarrays data in context of metabolic pathways. My primary interest is in finding metabolic pathways perturbed by a set of microarray genetic data. I would like to thank the BiologicalNetworks team for quick customization of this feature to my analysis.

Mandeep Kaur,

South African National Bioinformatics Institute,

University of the Western Cape, Bellville, South Africa

We are exploring sets of human genes with fold change expression values at various time points. I would like to thank BiologicalNetworks team for their efforts in assisting me on different stages of my analysis.

I like this software and want to make full use of it!

Nurzhas Nurpeissov

Ph.D student

Heidelberg, Germany

I am pleased to bring BiologicalNetwork into my research. It is essential to exploit curation of scientific findings given the large body of knowledge relevant to therapeutic research. BiologicalNetworks is providing us with an important view of current knowledge of mammalian signaling, metabolism, drugs and diseases through the PathSys platform.

Avantika Agrawal

National Institute of Pharmaceutical Education and Research, India

I have downloaded the BiologicalNetworks free ware. Its working fine: loading of my microarray data, building a network/pathway, from the option within the expression viewer toolbar, it can search database and then displaying results. Also for building found pathway, using the palette items, using the node editor the modification of the node name and editing pathways, gene properties is possible.

I would like to thank biological networks for making this free ware and nice tutorials. I also would like to see more features for querying and navigating from pathways to database and back available.

Thanks a lot!

Biological Articles: