FEBS 29314
FEBS Letters 579 (2005) 1821–1827
Minireview
From protein networks to biological systems
Peter Uetza,1, Russell L. Finley Jr.b,*
b
a
Research Center Karlsruhe, Institute of Genetics, P.O. Box 3640, D-76021 Karlsruhe, Germany
Center for Molecular Medicine & Genetics, Wayne State University School of Medicine, 540 East Canfield Avenue, Detroit, MI 48201, USA
Accepted 31 January 2005
Available online 7 February 2005
Edited by Robert Russell and Giulio Superti-Furga
Abstract A system-level understanding of any biological process
requires a map of the relationships among the various molecules
involved. Technologies to detect and predict protein interactions
have begun to produce very large maps of protein interactions,
some including most of an organismÕs proteins. These maps can
be used to study how proteins work together to form molecular
machines and regulatory pathways. They also provide a framework for constructing predictive models of how information and
energy flow through biological networks. In many respects,
protein interaction maps are an entrée into systems biology.
Ó 2005 Federation of European Biochemical Societies. Published
by Elsevier B.V. All rights reserved.
Keywords: Interaction map; Protein network; Pathway; System
1. Introduction
Systems Biology should give us the tools to model how
genes, gene products, and other molecules work together
to mediate biological processes. Use of such tools, and indeed their very development, requires, for each biological
process, lists of the molecules involved and their interconnections. The genes and proteins predicted from genome sequences have provided a long list of parts (genes and gene
products), and new technologies have begun to define lists
of other molecules not directly encoded by the genome that
are present in cells and tissues at particular times. New computational and experimental technologies have begun to produce enormous datasets representing interactions between
the parts. For the moment, most of the interaction data
comes from technologies to detect physical or functional
interactions between genes and proteins. Here, we will review
some of the sources of these data and consider how the
quantity and quality of the available interaction data may
impact systems-level studies.
2. Protein–protein interactions
The prominent role that protein–protein interactions play in
most biological processes, combined with the fact that we
*
Corresponding author. Fax: +1 313 577 5218.
E-mail addresses: peter.uetz@itg.fzk.de (P. Uetz), rfinley@wayne.edu
(R.L. Finley Jr.).
1
Fax: +49 7247 82 3354.
know so little about the functions of most proteins, has inspired efforts to map interactions on a proteome-wide scale
(e.g., for all of the proteins encoded by a genome) [1]. To date,
most of the interactions that have been detected experimentally
have relied on one of two technologies, the yeast two-hybrid
system [2] and mass spectrometry (MS) identification of proteins that co-affinity purify (co-AP) with a bait protein [3].
The two technologies detect complementary types of interactions. Co-AP/MS identifies the constituents of multi-protein
complexes but does not reveal the individual binary contacts
that make up each complex. Without data on the constituent
binary contacts, the possible paths of energy or information
flow through the complex and its relationship to other cellular
components may not be apparent. Yeast two-hybrid data, on
the other hand, identifies likely binary interactions that may
suggest possible paths through a pathway or complex, but cannot reveal the constituents of multiprotein complexes. Thus,
both types of data will be important for understanding protein
and pathway function, and ideally both approaches would be
performed on a proteome-wide scale.
Yeast two-hybrid screens aiming to cover entire proteomes,
or at least very large numbers of proteins, have detected thousands of interactions for a few eukaryotic model organisms
(Table 1), bacteria and phage [4,5] and viruses [6]. By contrast,
proteome-wide co-AP/MS screens have been conducted only in
yeast (Table 1), where most of the proteome could be easily
affinity tagged through the use of homologous recombination.
Co-AP/MS data for other organisms is only just beginning to
emerge through the use of high throughput cloning [7] and the
expression of large sets of tagged proteins in tissue culture cells
(e.g. [8,9]). Thus, it is likely that we will begin to see protein
complex data for humans and other metazoans in similar
quantities as the yeast studies have produced.
3. How complete are current protein interaction datasets?
Despite the volumes of interaction data produced, several
independent analyses have shown that the data from the large
two-hybrid and co-AP/MS screens is far from complete.
Various authors have estimated that the roughly 6000 yeast
proteins are connected by 12 000–40 000 interactions [10–12],
yet the high throughput screens have detected only a small
fraction of those numbers (Table 1). Another clue comes from
the lack of overlap among the different datasets for a particular
proteome. For example, in Table 1, the overlap among the
large two-hybrid screens for yeast was only 6 interactions
0014-5793/$30.00 Ó 2005 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.febslet.2005.02.001
1822
P. Uetz, R.L. Finley Jr. / FEBS Letters 579 (2005) 1821–1827
Table 1
Large protein interaction screens for eukaryotes
Interactionsa
Proteins
Reference
967
4549
420
9421
3878
1004
3278
271
1665
1578
[63]
[13]
[64,65]
[66]
[67]
Yeast two-hybrid
Yeast two-hybrid
20 405
1814
7048
488
[49]
[14]
Yeast two-hybrid
4027
1926
[68]
Organism (genes)
Method
Yeast (6000)
Yeast two-hybrid
Yeast two-hybrid
Yeast two-hybrid
Co-AP/MS
Co-AP/MS
Drosophila (14 000)
Worm (20 000)
a
For two-hybrid screens, the approximate number of unique binary interactions is shown. For co-AP/MS screens, the approximate number of binary
interactions that would result if each bait protein contacted every protein that co-purified with it (the ‘‘hub and spoke’’ model) is shown. Data can be
retrieved from one of the databases cited [42–44].
[13] while the overlap between the screens for Drosophila was a
measly 28, or less than 2% of the smallest data set [14]. The coAP/MS data is not much different. For example, when results
from the two large-scale studies are compared, the number of
interactions common to both datasets is less than 9% of the total in both datasets [15]. Data from the high throughput
screens also fails to overlap significantly with published ‘‘low
throughput’’ studies, which are generally considered to be less
subject to false positives and false negatives. Such analyses
have led some authors to estimate false negative rates as high
as 85% in large yeast two-hybrid screens and 50% in co-AP/
MS screens [16,17]. These results suggest that many more
interactions could be detected by more exhaustive application
of these technologies. In addition, there is a need for improved
or new high throughput technologies to identify interactions
that may be difficult to detect with two-hybrid or co-AP/MS,
such as interactions involving membrane proteins.
4. Physical and functional interactions
Comparison of the data from yeast two-hybrid and co-AP/
MS provides an example of an important distinction between
two types of interaction data: physical interactions (A touches
B) and functional interactions (A functions with B in some biological process). A functional relationship may or may not correspond to a direct physical interaction. Thus, physical and
functional interactions are two distinct though partially overlapping types of interactions and the distinction is likely to
be important for the development of systems-level models of
protein networks and pathways. Yeast two-hybrid is an experimental approach to detect physical interactions. Co-AP/MS
detects group of proteins in stable complexes, implying that
they function together. Another example of a functional but
not necessarily physical interaction is a genetic interaction, in
which the combination of alleles of two different genes has specific phenotypic consequences. This is often taken to suggest
that the two genes function in the same or parallel pathways
affecting a particular biological process. Thus, a genetic interaction is a measured functional interaction that may or may
not correspond to a physical interaction, but that could be usefully represented as a connection between the two genes or
gene products. Ongoing large-scale screens in yeast have
mapped thousands of genetic interactions [18]. Combination
of genetic and physical interaction data is a powerful approach
to mapping pathways [18,19].
5. Predicted and experimentally measured interactions
The increasing use of computational approaches to predict
protein interactions has led to additional large datasets (e.g.
[20]) and to another distinction between two types of interaction data: experimentally measured and predicted. Predicted
interactions can also be classified as either physical or functional. Gene expression profiles have been used, for example,
to infer functional interactions among gene products, based
on the assumption that proteins that function together in the
same pathway or complex should be frequently expressed together; which is supported by data for stable protein complexes [21,22]. Similarly, genes whose coexpression profiles
are conserved through evolution are often functionally related
[23,24], as are genes that are co-conserved from species to species [25–27]. The functional links between proteins in each of
these cases may be direct or very general; they may suggest
roles in the same pathways, or in distinct cellular systems that
are concomitant but that have very few direct molecular connections. Genetic interactions have also been predicted based
on physical interactions, gene expression, protein localization,
and other experimental data [28,29]. Numerous methods for
predicting physical protein–protein interactions have also been
developed [22,30–35]. One very powerful approach takes
advantage of the large number of experimentally measured
interactions available for organisms like yeast and Drosophila
to predict interactions in other organisms [36]. Simply put,
the approach predicts that two proteins will interact if their
orthologs were shown to interact; such conserved interactions
have been referred to as interologs [37–39]. This approach has
been used, for example, to predict 70 000 interactions involving proteins encoded by a third of the human genes [40]. Several other studies have begun to effectively integrate genomic
and proteomic data to make increasingly accurate interaction
predictions [33,41]. The further development and use of in silico approaches to map interactions seems particularly important in light of the shortcomings of high throughout
experimental detection systems.
6. Protein interaction maps
The wealth of data from high throughput screening and
other studies has begun to be consolidated into centralized,
standardized databases. Three of the largest public database
repositories for interaction data are BIND, DIP, and IntAct
P. Uetz, R.L. Finley Jr. / FEBS Letters 579 (2005) 1821–1827
1823
Fig. 1. Interaction maps today and tomorrow. (A) Typical representation of a protein–protein interaction map. (B) Proteins are usually shown as
nodes (e.g., circles and boxes) and interactions as edges (lines) connecting them. This organizes information so that attributes of both proteins and
interactions are easily accessible, for example, by hypertext links, but fails to capture structural information. However, additional information could
be made easily accessible through pop-up menus by clicking on the edges (like here) or the nodes. (C) and (D) Ideally, a protein interaction map
visualization tool would allow the structures of proteins and interaction interfaces to be expanded and browsed, and would also provide access to
more global interaction attributes (e.g., conditions under which a certain set of interaction can be found, experimental conditions, dissociation
constants, expression levels, etc.). Based on interactions published by Measday et al. [69], Uetz et al. [63], and reviewed in [70].
[42–44]. These allow researchers online access to browse and
download data in a standardized format [45]. Sets of interaction data can be viewed as graphs or maps in which each
gene/protein is a node and each interaction is a line connecting
two nodes (Fig. 1). The importance of this view has led to use
of the term ‘‘interaction map’’ to refer generically to interaction datasets. The map view provides not only an intuitive
interface for biologists to explore the data, but also a formal
mathematical framework for computational biologists to explore the properties of interaction networks. However, before
interaction maps can be used to represent biological networks,
their limitations must be considered.
In addition to the problem of false negatives discussed previously, most interaction maps and particularly those from
high throughput screens have false positives. Estimates of false
positive rates vary widely, in part because of the difficulty in
definitively demonstrating that any particular interaction does
not have a biological function. Because the false positive rates
may be substantial, the maps from high throughput studies
might be usefully regarded as the results from a first pass filter,
which reduces the possible search space for functionally important interactions. Thus, the question becomes how to identify
the more likely true positives. Several studies have confirmed
the general principle that interactions detected in multiple
screens and by different techniques or in different species are
more likely to be true positives than those only found once
or twice [17]. Due to the high rates of false negatives in high
throughput screens, however, there has been very little overlap
between different datasets, thus, limiting the opportunities for
such experimental cross-validation. Alternatively, a variety of
confidence scoring systems have been developed that calculate
the likelihood of an interaction being a true positive, based on
various parameters, including attributes of the proteins and the
specific assays, whether the interaction was detected by other
technologies or screens, and network topology [20,46–50].
However, thus far most of these scoring systems are specific
to particular datasets or methodologies, and no universal system has yet been effective.
Another limitation of most protein interaction maps is
that each node generally represents some generic version of a
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DNA
Gal1
Gal10
Gal10
Gal7
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P
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Srb10 Srb11
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RNA pol II holoenzyme
General TAFs Gal11
Nucleus
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TRENDS in Cell Biology
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Fig. 2. Integrating protein networks and other biological information. (Bottom) The map of interactions among 1200 yeast proteins represents only
the tip of the information iceberg. Highlighted in blue are proteins involved in galactose regulation, which in the map are found in a topological
cluster (see text). A cluster of cytoskeletal proteins (highlighted in red) is also visible. (Top) Integrating the protein interaction network with the
metabolic network (shown as green compounds and grey enzymes), the gene regulatory network (shown as pink genes and blue transcription factors),
and the signaling network indicated by the Cyclin-dependent kinase Srb10 and its cyclin Srb11 (red). Yellow boxes indicate functional modules that
involve additional protein interactions within complexes and with other proteins (not shown). Modified after Tucker et al. [12] and Rohde et al. [71].
cellular protein, without regard to the various splice variants
or post-translationally modified forms that may exist. Isoforms
could interact differently from the form that was actually used
in the assay, which in many cases is unclear. This is particularly true for assays that use only one or a small number of
the possible alternative transcripts from each gene. Thus, many
P. Uetz, R.L. Finley Jr. / FEBS Letters 579 (2005) 1821–1827
so-called ‘‘protein’’ interactions maps are actually gene or locus interaction maps, which tell us only that one or more of
the proteins encoded by one locus is capable of interacting
with one or more of the proteins encoded by another locus.
Nevertheless, such maps have proven to be useful as starting
points for additional studies, particularly if the caveats are
borne in mind.
7. Using the interaction maps for systems biology
A complete systems-level understanding of any biological
process may require more input data than current technologies
can offer. Intuitively, we imagine that we could model a system
best only after knowing all of the molecules involved, their
concentrations, how they fit together, the effect of each individual part on its neighbors, and dynamic parameters such as how
concentrations, interactions, and mechanics change over time.
But this seems unrealistic given the fact that the high throughput technologies for measuring many of these parameters are
still on the drawing board, if they exist at all. Do we really need
to know all of the details of a process to be able to develop a
useful systems-wide understanding or to have a predictive
model? Analysis of protein interaction maps has suggested that
even sparse data can be used to derive initial, rudimentary
models of biological networks.
Topological analyses, for example, initially of metabolic
pathways and subsequently of protein interaction maps, began
to reveal some common properties of biological networks
[51,52]. These initial studies suggested the exciting possibility
that cellular networks may be organized according to some
general principles that could be understood without a detailed
knowledge of all the constituent proteins and interactions
[53,54]. Moreover, analysis of network topology can provide
insights into protein and pathway function. For example, protein networks contain highly connected hub proteins, which
have been shown to correlate with evolutionarily conserved
proteins, and in yeast with proteins encoded by essential genes
[51,55,56]. Thus, a proteinÕs relative position in a network has
implications for its function and importance. Analysis of
topology also reveals clusters of highly interconnected proteins
that correlate with conserved functional modules (Fig. 2), such
as protein complexes or signaling pathways [57–59]. Thus,
even the currently available noisy protein interaction maps
can be used to explore the hierarchical organization of biological networks and to reveal interconnected modules that control specific biological processes. As these modules are
defined and further elaborated, understanding them and their
higher order organization will increasingly rely on advances
in information technology.
1825
a map of interactions [60–62]. These programs allow exploration and ad hoc analyses of interaction data but they rarely
incorporate all of the useful available information about the
molecules and interactions they represent (e.g., see Figs. 1
and 2). Moreover, they usually fail to capture the essential dynamic properties of biological networks. Animated cartoons,
on the other hand, can provide at least a qualitative representation of the dynamics of a process (see, for example, Fig. 3).
However, such oversimplification does not capture the details
of the system or facilitate quantitative modeling. In a way,
visualization of molecular networks is where word processing
was in the early 1980s.
To help us model biological processes, and to visualize and
manipulate those models, we need programs to generate more
dynamic and realistic representations of biological events and
structures. We need what might be called ‘‘interactive CellTV’’ to visualize and manipulate models of cellular events
and behavior. Importantly, iCell-TV must operate across several scales of time and space to allow biologists to navigate all
available relevant information. Such a system, for example,
might allow users to explore the changes in the molecular
structure resulting from a post-translational modification,
zoom out to witness the subsequent changes in network and
pathway dynamics, and then change time scales to observe
organelle movement or cell behavior. The number and complexity of the experiments that must be done to test hypotheses
8. Perspectives: iCell-TV
How can biologists access and integrate the deluge of proteomics data to help them understand biology? While this
information should help drive the generation of hypotheses
and hypothesis-testing research, we may be generating data
faster than we are learning how to use it. Tools for accessing
and analyzing molecular interaction data have just begun to
emerge over the past few years. Several ‘‘visualization’’ tools
and graphing programs, for example, allow users to construct
Fig. 3. Visualizing the dynamics of protein interaction and signaling
networks. The pheromone signaling pathway in yeast is a highly
dynamic process that involves numerous protein interactions, phosphorylation events, and small-molecule interactions involving ATP
and GTP. Typical textbook (i.e., static) representations like this do not
reflect the dynamics of this process. A more realistic representation is
available through animation, as shown at http://www.bioveo.com/
MAPK/MAPk.htm. Simplified from an animation by Tom Dallman,
by permission of the author.
1826
coming from network analyses are likely to be costly and inefficient. The next generation of biological information management systems must, therefore, allow us to do biology truly in
silico. For this to be possible, they must enable the development and manipulation of quantitative models, which are often initially based on a qualitative understanding. However,
it is often the case that about the time we understand a system
well enough to be able to model it, it becomes too hard to
understand in a qualitative sense. A system for navigating
qualitative information based on quantitative data would give
users the ability not only to understand the complexity of biological processes but also to manipulate those processes, to
construct new models, and to test new hypotheses. Zoom in,
change a Kd or a Vmax, then zoom out and watch what happens
to the system. This would be systems biology for the rest of us,
and would open biological inquiry to a vast resource of
creativity.
Acknowledgments: We thank Tom Dallman for permission to use his
pheromone pathway animation.
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