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.



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