A road map of yeast interactions
© BioMed Central Ltd 2005
Published: 1 June 2005
Analysis of a yeast network that integrates five interaction datasets reveals the presence of large topological structures reflecting biological themes.
Integrating interaction maps
Cellular processes can be explored by investigating interactions between biological components. The complex system of the cell is a network of interconnections – proteins interact with other proteins or with DNA, and genes can interact functionally with one another. Large-scale projects have attempted to define the entire list of genetic components (the genome), their expression patterns (the transcriptome), their protein products (the proteome) and the interaction between them (the interactome). A key challenge is to integrate these different maps so as to develop a conceptual model for dynamic cellular behavior.
"Protein interaction mapping projects have emerged as an extremely powerful resource for understanding, and ultimately modeling, cell function on a genome-wide scale," comments bioinformatics researcher Trey Ideker from the University of California, San Diego. "Although protein-protein interactions were some of the first to be measured at high-throughput, a variety of other interaction types are also being cataloged, such as genetic (synthetic-lethal) and protein-DNA interactions," he says, adding that the Roth study extends previous work by considering all of these different interaction types together. "The attempt to unify networks composed of heterologous components is certainly forward-looking," agrees Zoltan Oltvai from the University of Pittsburgh School of Medicine, Pennsylvania.
Navigating towards motifs and themes
The yeast network produced by Roth and colleagues  contains 5,831 nodes (genes or proteins) linked together by a staggering 154,759 interactions ('edges' in network jargon). But building these networks is a lot easier than figuring out what they mean. To explore their map, Roth and colleagues were inspired by ideas from the field of network theory and the seminal work of Uri Alon at the Weizmann Institute of Science, Rehovot, Israel. Alon's group characterized the architecture of complex systems and defined basic network components called 'motifs' [5, 6]. "When Alon and colleagues published the concept of elementary interaction patterns in cellular (and other) networks, it was important not only for our further understanding of network topology, but also because they could develop certain predictions regarding network behavior," explains Oltvai.
"Alon was the first to show that protein-protein interaction networks encode particular sub-circuits (motifs), such as feed-back and feed-forward loops," notes Ideker. These concepts were welcomed by researchers in the nascent field of systems biology, who construct complex network models. "Motif analysis is increasingly being used to understand the properties of integrated networks," comments Ernest Fraenkel from the Whitehead Institute in Cambridge, USA. "For example, network motifs were recently used systematically to assess the relationship between the transcription regulatory network and chromosomal organization in Escherichia coli and in budding yeast , yielding significant biological insight."
Both Alon's group and Oltvai's group (in collaboration with Barabási) had previously shown that motifs sometimes appear in clusters [5, 8, 9]. "We demonstrated that motifs mostly do not exist in isolation, but that they aggregate into larger structures and this is a natural consequence of the networks' global topological organization," notes Oltvai. Roth also found that most motifs were componenets of higher-order structures, and coined the term 'network themes' to describe the recurrent examples of higher-order structures. Themes can be made up of multiple occurrences of the same motif (Figure 1b) or several different types of motif (Figure 1d).
"Roth shows that the types of molecular sub-circuits encoded by biology are exponentially richer than was previously thought. This complements work by others that is also directed at finding the commonality between networks of different types," says Ideker. A recent study of protein interactions from Ideker's group proposes a specific computational model of how physical and genetic interaction networks relate to each other to delineate redundant and/or synergistic molecular machinery . "Roth's group go beyond the motif analysis by providing a higher-level organizing principle," says Fraenkel. "The biological relevance of a network theme is often much clearer than the relevance of the underlying motifs. Network themes should also be less sensitive to the noise in individual data sources."
Complexes and cliques
The characterization of network themes led Roth and colleagues  to propose one further step: the construction of thematic maps, which chart a simplified landscape by showing only the larger structures and the links between them. He compares them to sub-graph structures in other complex networks. "For example, you could have social networks with certain groups of people, by whatever classification scheme that you wanted to impose, who were more likely to interact with each other. So, social networks have cliques just as protein networks have complexes. And there might be pairs of complexes that have a lot of synthetic-lethal interactions, just as there might be pairs of social cliques with a lot of interactions. Many of the same ideas apply." Roth adds that his group has previously used ideas that come straight out of communications theory to analyze protein interaction networks.
The motivation for computational modelling is to generate hypotheses that can then be tested experimentally. "In my view, one justification for looking at network motifs as interesting objects, aside from the fact that they form clusters, is that each motif (in transcription networks at least) can be assigned defined functions," comments Alon. "These functions can then be tested experimentally in living cells using measurements on motifs embedded inside the entire network." Indeed, laboratory results have supported many of the predictions made by Alon's group in fields as diverse as the E. coli flagellum and sporulation in Bacillus subtilis. Roth is keen to make further predictions about genetic links between the thematic groups in yeast.
Researchers agree that this approach will be enhanced by more data about genetic interactions. "I like the extensive analysis of multi-colored networks of diverse interactions," says Alon. "I think that the Roth paper is original and will have significant impact as we gain more and more data on integrated networks of interactions." Some experts in the field have raised questions about whether the different types of 'interactions' are all comparable. But analysis of these complex networks will indicate how reliable the links are, and how useful the concepts of motifs and themes are in predicting biologically relevant functions. The study by Roth and colleagues has laid down a methodology for large-scale integration of maps and multi-color network analysis. They are keen to see how similar approaches proceed in other organisms, and whether the general thematic maps are conserved. "I think that better use of topological patterns could help predict all sorts of interactions," concludes Roth.
This article is dedicated to the memory of Professor Lee A Segel (Weizmann Institute of Science, Rehovot, Israel), a pioneer of integrating mathematical and experimental approaches to biology.
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