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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>NRNB: About</title>
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<div class="belt white">
<div class="container">
<div class="section">
<h3>What is Network Biology?</h3>
<p>Analysis of biological networks has exploded in recent years. A
wide variety of technologies have been introduced for mapping
networks of gene and protein interactions, including yeast-two-hybrid
assays, affinity purification coupled to mass spectrometry, chromatin
immunoprecipitation measurements, synthetic-lethal and suppressor
networks, expression QTLs, and many others. These technologies have
the potential to revolutionize the study of human health, by enabling
the construction of large pathway maps which can pinpoint the
molecular mechanisms underlying normal and disease states of
biological systems. However, the enormous variety and number of new
molecular interaction measurements necessitate new algorithms,
conceptual frameworks, and software to integrate, query, visualize,
and interpret the resulting network data.</p>
</div>
<ul class="nav nav-tabs" id="tabs">
<li class="active"><a href="#research-tab" data-toggle="tab">Research</a></li>
<li><a href="#community-tab" data-toggle="tab">Community</a></li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="research-tab">
<p>A wide range of computational techniques are being developed
in order to analyze biological network data to address specific
problems with relevance to basic biology and human health:</p>
<p>
<b>Gene function prediction.</b> This is a basic but important
problem, since the majority of genes in most genomes have no known
function. Examining genes in a network context can help answer this
question. For example, a protein of unknown function connected to a
set of proteins involved in the same biological process is likely
to also function in that process.
</p>
<p>
<b>Detection of protein complexes and other modular structures.</b>
Although interaction networks are based on raw pair-wise
interactions, there is clear evidence in these networks for
modularity and principles of higher-order organization. Can we
determine how networks are organized and create abstractions that
serve as effective descriptions of network features? A number of
groups have tried to answer this question through analysis of the
molecular network topology . For example, molecular interaction
networks have been found to cluster into regions that represent
complexes and statistically over-represented motifs, such as
feedback loops, have been discovered some of which have been
thoroughly analyzed.
</p>
<p>
<b>Network evolution.</b> Increasingly, interaction networks are
being used to address questions of evolution, such as 'Which
biological processes are conserved between species, and which are
distinct?' A number of techniques have been developed that identify
common network structures and organizational principles that are
conserved across species. For example, our PathBLAST and
NetworkBLAST tools align protein-protein interaction networks to
identify conserved interaction paths and clusters, respectively.
</p>
<p>
<b>Prediction of new interactions and functional associations.</b>
Statistically significant domain-domain correlations in a protein
interaction network suggest that certain domains (and domain pairs)
mediate protein binding, allowing prediction of new via. Machine
learning has also been applied to predict protein-protein or
genetic interactions through integration of diverse types of
evidence for interaction
</p>
<p>Beyond these established cases, Network Biology is playing an
increasing role in the study of human disease, including:</p>
<p>
<b>Identification of disease subnetworks.</b> A recent and powerful
trend is to identify neighborhoods of a biological network (i.e.,
subnetworks) that are transcriptionally active in disease Such
neighborhoods suggest key pathway components in disease progression
and provide leads for further study and potential new therapeutic
targets.
</p>
<p>
<b>Subnetwork-based diagnosis.</b> It has become clear that
subnetworks also provide a rich source of biomarkers for disease
classification. Several groups have demonstrated that mRNA profiles
can be integrated with protein networks to identify subnetwork
biomarkers, i.e. subnetworks of interconnected genes whose
aggregate expression levels are predictive of disease state.
</p>
<p>
<b>Subnetwork-based gene association.</b> Despite the growing
number of gene association studies based on Single Nucleotide
Polymorphism (SNP) and Copy Number Variation (CNV) profiling, most
human genetic variation remains uncharacterized, both in biological
mechanism and in impact on disease pathology. We anticipate that
molecular networks will provide a powerful framework for mapping
the common pathway mechanisms impacted by a collection of
genotypes, and several proofs-of-principle have recently been
developed in this area.
</p>
</div>
<div class="tab-pane" id="community-tab">
<div class="row">
<div class="span9">
<p>The Network Biology community has grown rapidly over the past
few years. As an illustration of how pervasive networks have
become, the U.S. National Institutes of Health currently funds 3076
active research grants covering the topic "protein protein
interactions" with 794 of these implementing the specific technique
of "yeast two hybrid system". Network research is also actively
supported by a variety of initiatives from other major national and
supra-national funding agencies, such as the NSF, DOE, EU, and
private foundations. As of 2009, approximately one-third of papers
presented at the major Bioinformatics conferences (e.g., ISMB,
RECOMB, ECCB, ICSB) focus on novel methodologies related to
analysis or inference of biological networks- this number is up
from less than one tenth at the beginning of the decade. Network
Biology is also the focus of several major conferences per year,
most recently including the CSHL / Welcome Trust Network Biology
meeting (18 March 2009, Cold Spring Harbor) and Network Biology 2.0
(20 May 2009, Broad Institute). This number is greater still if one
expands to include conferences in the highly overlapping field of
Systems Biology.</p>
</div>
<div class="span3">
<a href="images/F2.png" target="_blank">
<div class="img-polaroid">
<img src="images/F2.png" alt="Fig 1" width="250"/><br/>
Figure 1: Usage Statistics
</div>
</a>
</div>
</div>
<div class="row belt">
<div class="span3">
<a href="images/F3.png" target="_blank">
<div class="img-polaroid pull-right">
<img src="images/F3.png" alt="Fig 2" width="250"/><br/>
Figure 2: Users
by Grantees
</div>
</a>
</div>
<div class="span9">
<p>Another metric of the growing community of network research
is to track usage of software tools. For instance, use of Cytoscape
has increased at a rate of ~50% per year since 2003, including
nearly 30,000 downloads so far in 2009 and ~40,000 unique visits
and ~50,000 page views per month currently (Figure 1).
Cumulatively, the Cytoscape web page has been visited approximately
one million times and the application has been downloaded ~90,000
times. The original Cytoscape paper and the more recently released
Cline, et al. paper have been cited over 1000 times and used
uncited in many more (we are aware of many publications, including
recent high-profile examples that use Cytoscape for visualization
(i.e. generation of figures) and analysis but do not cite the
Cytoscape publications). Figure 2 shows how a subset of these works
has been supported by a collection of different NIH institutes as
well as other funding agencies. These increases in software usage
and development closely parallel the increases in availability of
large-scale network information and its potential impacts in
biomedicine.</p>
<p>Thus, the importance of Network Biology is very much
appreciated by basic scientists and clinicians, many of whom use
interaction databases and network analysis tools in their
day-to-day research. On the other hand, many other investigators
(basic, clinical, and pharmaceutical) as of yet have little
knowledge of recent developments in network biology and would
benefit greatly from outreach and education centered on
network-based technologies. With creation of a NRNB National
Resource- focused on technology development supporting research,
collaboration, education, and dissemination- the Network Biology
community could potentially become a great deal larger.</p>
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