Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data
© Li et al.; licensee BioMed Central Ltd. 2012
Received: 29 September 2011
Accepted: 23 May 2012
Published: 23 May 2012
Identification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules.
In this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using “Biological Process” annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of “Biological Process”. More importantly, the relationship between protein complexes and functional modules are studied.
The proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at http://netlab.csu.edu.cn/bioinfomatics/limin/DFM-CIN/index.html.
Recent advances in biotechnology have resulted in a large amounts of protein-protein interaction (PPI) data. Modeling and clustering PPI networks with simple graphs makes it possible for us to understand the basic components and organization of cell machinery from the network level. One of the most important challenges in the post-genomic era is to analyze the complex networks of PPIs and detect protein complexes or functional modules from them. Over the past decade, many computational methods have been proposed for clustering PPI networks, such as G-N , MCODE, RNSC, LCMA, DPClus , MoNet , IPCA , COACH , and SPICi .
While significant progress has been made in computational methods, there are two major challenges in clustering PPI networks. One of the challenges is that the conventional clustering methods generally considered the PPI network as a static graph and overlooked the dynamics inherent within these networks. This is mainly because that the widely used large-scale technologies for determining PPIs, such as yeast two-hybrid and TAP-MS, do not provide spatial, temporal or contextual information for the predicted PPIs . In fact, a PPI network is not a static but a dynamic entity, so whether or not a protein is expressed is intrinsically controlled by different regulatory mechanisms through time and space [12, 13]. Recently, studies on network dynamics have begun to attract researchers’ attentions[11, 14]. Of course, biologists have studied dynamics in biological systems for many years. However, their efforts generally focused on individual genes or proteins as well as specific interactions in limited contexts. With the accumulation of PPI and transcriptome data, the integration of gene expression profiles with PPIs provides new way of uncovering the dynamics of PPI networks [15, 16].
Jansen et al.  first investigated the relationship of PPI interactions with mRNA expression levels and scored expression activity in complexes. Tornow and Mewes used the superparamagnetic approach to evaluate the multi-data correlations and constructed a graph of co-expressed genes for detecting functional modules. Han et al.  analyzed the PPI network of yeast, and they uncovered two types of hub proteins: “party” hubs and “date” hubs. Recently, Taylor et al. also proposed another two types of hub proteins: intermodular hubs and intramodular hubs, and they investigated the modularity of human PPI networks in two breast cancer patient groups. Xue et al.  analyzed the dynamic modular structure of the human PPI network in their aging study. Lu et al.  proposed a simple hierarchical clustering algorithm for analyzing the dynamic organization of biological networks by integrating the yeast PPI interaction data, the global subcellular localization data and the integrated expression profile data. Cline et al.  described how to integrate biological networks and gene expression data by using Cytoscape. Maraziotis et al.  presented a method to detect dense subnetworks in a weighted graph that was constructed by using the gene expression information. Cho et al.  also introduced an algorithm based on informative protein selection from a weighted graph where the weight was calculated by using co-expressional profiles. More recently, Luo et al.  explored special kinds of protein complexes by integrating transcription regulation data, gene expression data and PPI data at the systems biology level. Hegde et al.  proposed an approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data. De Lichtenberg U et al. combined the subcellular localization data, gene expression data and PPI network to extract a temporal protein interaction network of the yeast mitotic cell cycle. Komurov and White used gene expression data to classify dynamic proteins which are expressed periodically and static proteins which are expressed all the time, and furthermore identified dynamic modules and static modules on a static PPI network. Similar techniques were also applied to the identification of disease-related genes or modules [19, 29]. All these works have made significant progress in the integration of co-expression information and PPI networks. However, only a few of them focused on the identification of protein complexes or functional modules. Some of them only used gene expression information to construct weighted PPI network which was still static.
Another challenge in clustering PPI networks is how to distinguish between protein complexes and functional modules. Up to now, little progress has been made on this point. Most clustering methods based on PPI networks detected both protein complexes and functional modules without distinguishing between them because they disregard interaction dynamics. How closely are functional modules related to protein complexes? What are the differences between them? Spirin and Mirny have argued their differences from the concepts that protein complexes are groups of proteins interacting with each other at the same time, and functional modules, by contrast, are groups of proteins participating in a particular cellular process while binding to each other at a different times. Though Spirin and Mirny believed that it was very important to distinguish between protein complexes and functional modules, they did not distinguish between the two because that they lacked temporal and spatial information on the analyzed PPIs. Recently, Lu et al proposed to make this distinction by integrating PPI data with the added subcellular localization and expression profile data. They investigated the relationship between protein complexes and functional modules and revealed that a functional module generally consists of proteins that participate in a common biological process, and that protein complexes form the intersections of co-localized and co-expressed protein groups that are usually included in the functional modules.
In this paper, we will not go as far as what the conventional clustering algorithms have focused on but rather try to propose a framework to detect and distinguish between protein complexes and functional modules. In other words, we will not only explore protein complexes and functional modules but also study their relationships. Considering the fact that proteins in a complex interact with each other at the same time, we constructed a serial of time-sequenced subnetworks by integrating gene expression data into PPI data. These time-sequenced subnetworks show dynamic changes in the original network. Thus, we call these time-sequenced subnetworks together as a dynamic PPI network. An improved algorithm TSN-PCD, developed from our previous algorithm HC-PIN, is proposed to identify protein complexes from the dynamic PPI network. Applying TSN-PCD to a dynamic PPI network of S.cerevisiae, we found that many proteins were found in a multitude of complexes rather than a single complex. Here, we would like to ask whether two protein complexes interact with each other through their common proteins. Moreover, what is the underlying machine between protein complexes and functional modules. To answer these questions, we constructed a complex-complex interaction network and proposed an algorithm, DFM-CIN, for detecting functional modules from it.
In the case of identifying protein complexes, we found more known protein complexes are recalled after the combination of temporal gene expression data with PPI data. We also found not only the combination of temporal gene expression data with PPI data but also the algorithm TSN-PCD contribute to detecting protein complexes more precisely. A quantitative comparison based on f -measure reveals that our algorithm TSN-PCD outperforms six other previously proposed protein complex discovery algorithms: MCL[32, 33]), MCODE, CPM, COACH, SPICI, and HC-PIN. Furthermore, we evaluated the identified functional modules by using “Biological Process” annotated in GO (Gene Ontology) and found most of them participated in a special biological process. Additionally, we even found the relationship between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes.
A framework for detecting protein complex and functional module
When clustering PPI networks, people seldom distinguish between protein complex and function modules. However, they are not the same thing. The main difference between them is that protein complexes occur at the same time, functional modules, generally function at different times. Spirin and Mirny  have discussed the differences between protein complex and functional module from biological view. According to Spirin and Mirny’s perspective, we defined protein complex and function module as follows: (1)Protein complexes are groups of proteins that interact with each other at the same time and place, forming single multi-molecular machine, such as AP-2 adaptor complex, DNA polymerase epsilon complex, Dig1p/St12p/Dig2p complex, SAS complex. (2)Functional modules, in contrast, consist of proteins that participate in a particular cellular process while binding each other at a different time and place, such as the CDK/cyclin module responsible for cell-cycle progression, the yeast pheromone response pathway, MAP signaling cascades. In this paper, we can not only predict protein complexes and functional modules but also distinguish them.
Preliminary observation of protein complexes and functional modules has indicated that while protein complexes occur at the same time, functional modules, generally function at different times. The former are usually included in the latter [21, 30]). According to the close relationship between protein complexes and functional modules, as well as the obvious difference between them, we propose to discover functional modules based on the identified protein complexes. A complex-complex interaction network is constructed based on analyzing the relationship among the identified protein complexes. In the complex-complex interaction network, each vertex represents a protein complex and each edge represents the relationship of two protein complexes. Then, a clustering algorithm can be applied on the complex-complex interaction network to explore functional modules. Different clustering algorithms can also be used here. To distinguish it from the protein complex discovery algorithm, we mark it as clustering method 2 in Figure 1. Next, we will discuss two specific algorithms for identifying protein complexes and functional modules, respectively.
TSN-PCD: Time-sequenced network-based protein complex discovery algorithm
where N u denotes the set of neighbors of vertex u N v denotes the set of neighbors of vertex v, and I u,v denotes the set of common vertices in N u and N v (i.e. ).
All the edges in TSN i are queued into S q in a non-increasing order in terms of their clustering values. Then, different clusters are constantly reassembled into larger clusters by gradually removing edges from the queue. The basic idea is that the higher clustering value the edge has, the more likely its two vertices are inside the same protein complex. Finally, the clusters which consist of no less than s proteins are produced as protein complexes.
In this step, the previous clustering algorithms used in static PPI networks can also be used here. As our proposed algorithm HC-PIN outweighs other clustering algorithms in most cases. We thus use it to predict protein complexes with the recommended parameter λ=1.0.
DFM-CIN: detecting functional modules from the complex-complex interaction network
For a protein complex C i , let be the set of times that protein complex C i functions in the corresponding TSNs. If two protein complexes function at least in one same time (ie., that, ), we say that these two protein complexes are synchronous. If two protein complexes function in two continuous times, (ie., that, ), we say that these two protein complexes are adjacent to each other.
Let graph G(V,E) denote the complex-complex interaction network (abbreviated to CIN). In graph G, a vertex represents a protein complex, an edge represents a connection between two protein complexes, and the edge weight represents how similar two protein complexes are. There is a one-to-one correspondence between a protein complex C i and a vertex v i of G.
It is clear that the weighted degree d w (v) of a vertex v is equal to the sum of and .
Results and discussion
Datasets and evaluation methods
The original protein-protein interaction data of S.cerevisiae, consisting of 4950 proteins and 21,788 interactions, was downloaded from the DIP database (2009, version12) . The gene-expressing profiles of S.cerevisiae were retrieved from Tu et al., 2005, which contains 6777 gene products and 36 samples in total, with 4,858 genes involved in the yeast PPI network. We integrated gene expression profiles with the PPI network to construct a series of time-sequenced subnetworks (TSNs). In the integration process, the gene products with an expression value lower than 0.7 are filtered.
In an effort to evaluate the proposed algorithms of TSN-PCD and DFM-CIN, we compared them with five previously proposed clustering algorithms: MCL[32, 33]), MCODE, CPM, COACH, and SPICI. MCL is a fast and highly scalable clustering algorithm for networks based on stochastic flow, and its superiority for the extraction of protein complexes has been proven by Brohee et al. MCODE is a typical density-based local search algorithm. CPM is an algorithm for detecting overlapping communities in biological networks , and formed the basis for a famous tool called CFinder . COACH and SPICI are the two most recent algorithms for clustering PPI networks to discover protein complexes and functional modules. The values of the parameters in each algorithm are selected from those recommended by the authors.
Identification of protein complexes in dynamic protein-protein interaction network
First of all, the proposed algorithm TSN-PCD is applied to the dynamic PPI network of S.cerevisiae. There are 865 different protein complexes detected, and ∼60% of the protein complexes are explored in only one TSN and ∼24% are discovered in more than three TSNs. So many protein complexes are only found in one TSN. This may be caused by the strict definition of protein complexes. For the complexes, they will be considered as two different complexes even they have most common proteins. How to deal with the overlapped protein complexes is an important and challenging issue. In future, we will study complexes over time-sequenced networks and investigate the relationship of the proteins in the protein complex. Moreover, the threshold value used to filter gene products at each time point may be another reason. Lower threshold of gene expression causes protein complexes tending to appear in less TSNs.
Examples of protein complexes identified by TSN-PCD more precisely in a dynamic network than that identified by HC-PIN in a static network
Best matched complexes identified
Best matched complexes identified
Known protein complexes
in static network (HC-PIN)
in dynamic network (TSN-PCD)
AP-3 adaptor complex
AP-1 adaptor complex
FBP degradation complex
alpha DNA polymerase:primase complex
Kornberg’s mediator (SRB) complex
where TP (true positive) is the number of the predicted complexes (Pc) matched by the known complexes (Kc), FP (false positive) equals the total number of Pc minus TP, and FN (false negative) is the number of Kc that are not matched by Pc.
The f -measure results of TSN-PCD and five other algorithms (MCL, MCODE, CPM, SPICI and COACH) performed on static and dynamic PPI networks are shown in Figure 8. From Figure 8 we can see that the f -measure of TSN-PCD is much higher than that of HC-PIN, MCL, MCODE, CPM, SPICI and COACH on a static PPI network. The f -measure of TSN-PCD is about two times more than that of MCL, CPM, and SPICI, and it is about six times more than that of MCODE performed on the static network. As TSN-PCD is applied in a dynamic network and MCL, MCODE, CPM, SPICI and COACH are applied in a static network, it is difficult to confirm what really contributes to the improvement of f -measure of TSN-PCD, TSN-PCD itself or the dynamic network? Therefore, we also apply another five algorithms (MCL, MCODE, CPM, SPICI and COACH) to the dynamic network. That is, we replace the subroutine HC-PIN of TSN-PCD with MCL, MCODE, CPM, SPICI and COACH, respectively. The comparison of the f -measure results of TSN-PCD with those of the other five algorithms when applied to a dynamic network are also shown in Figure 8. The f -measure values of MCL, MCODE, CPM, SPICI and COACH applied to a dynamic network are improved relative to those obtained when static network was used. From Figure 8 we can also find that the f -measure of TSN-PCD is consistently higher than that of MCL, MCODE, CPM, SPICI, and COACH, even when performed on a dynamic network. The results show that not only the use of a dynamic network, but also the algorithm, TSN-PCD, enhances the accuracy of identifying protein complexes. Algorithm TSN-PCD outperforms all five previous algorithms in the detection of protein complexes.
Evaluating functional modules based on Function Enrichment
It is well known that functional modules are closely related to protein complexes. In fact, most clustering algorithms detect both protein complexes and functional modules without distinguishing between the two. In this paper, we constructed a weighted graph (CCI network) by calculating the similarities among the identified protein complexes and analyzing their relationships in time. Then, the proposed algorithm DFM-CIN was applied to the weighted graph to discover functional modules. The similarity threshold th=0.5 is used here. The effect of its variation will be discussed later. In the following, 0.5 is used as a default value for the algorithm, DFM-CIN, if without special instructions.
Functional enrichments of the identified complexes detected by TSN-PCD and functional modules detected by DFM-CIN, MCL, MCODE, CPM, SPICI, and COACH
% of significant
E-15 to E-10
E-10 to E-5
E-5 to 0.001
Effect of parameter th on the identification of functional modules
Effect of parameter th on the identification of functional modules
Number of modules
% of significant modules
E-15 to E-10
E-10 to E-5
E-5 to 0.001
The number of the identified functional modules increases with the increase of th. This is because the larger value of th leads to fewer edges connecting the protein complexes. That is to say, a sparser graph is constructed by using a larger value of th. As a result, more functional modules will be identified with the same criterion for generating modules. From Table 3, we can see that DFM-CIN is not very sensitive to the input parameter, th, for evaluation of its biological meaning.
Relationship between protein complexes and functional modules
There are about 45% identified functional modules which consist of only one protein complex. For example, module (# 15) and module (# 235) both consists of only one protein complex (The identified functional modules are available from Additional file 4). Module # 15 functions as “nuclear-transcribed mRNA catabolic process, exonucleolytic, ” which includes a Nonsense-mediated mRNA decay pathway complex. In the definition of GO:0000184, the nonsense-mediated decay pathway for nuclear-transcribed mRNAs degrades mRNAs in which an amino-acid codon has changed to a nonsense codon. This prevents the translation of such mRNAs into potentially harmful, truncated proteins. Module # 235, including a FBP degradation complex and a protein “MOH1”, whose function is unknown, participates in the process of “negative regulation of gluconeogenesis”.
An important and challenging task in post-genomic era is to investigate the systematic and dynamic organization of PPI networks and explore biologically significant clusters. This paper introduces a new framework for constructing a dynamic PPI network by integrating gene expression data into PPI data. An important contribution of the framework is that in which protein complexes and functional modules can be distinguished. Few such works have been done before, though many researchers know that protein complexes and functional modules are two different concepts which have different biological meanings. In the proposed framework, the dynamic PPI network is composed of a series of time-sequenced subnetworks, based on the the time that the interactions are activated. Two different clustering algorithms: TSN-PCD and DFM-CIN are proposed for identifying protein complexes and functional modules, respectively.
To test and validate the effectiveness of the proposed framework and clustering algorithms, the identified protein complexes and functional modules are compared with those detected by other clustering algorithms: MCL, MCODE, CPM, SPICI, and COACH. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other five protein complex discovery algorithms. Comparison of the results on static and dynamic PPI networks shows that the combination of temporal gene expression data with PPI data is worthwhile for protein complex discovery.
An evaluation of the identified functional modules involved the function enrichment. The evaluation shows that the identified functional modules discovered by DFM-CIN are statistically significant in terms of “Biological Process”. More importantly, the analysis of the relationship between protein complexes and functional modules reveals that a module generally consists of one or more protein complexes, and the protein complexes contained in the same module participate in the same biological process universally.
In conclusion, the proposed framework and clustering algorithms, TSN-PCD and DFM-CIN, effectively reveals modular organization of the PPI network, and they distinguish well between protein complexes and functional modules.
ML and XW developed and implemented the new framework and algorithms. ML drafted the manuscript under the guidance and supervision of JW and YP. All authors read and approved the final manuscript.
The authors would like to thank Matthew Sartin for useful discussions and carefully proofreading the manuscript. This work is supported in part by the National Natural Science Foundation of China under Grant No.61003124 and No.61073036, the Ph.D. Programs Foundation of Ministry of Education of China No.20090162120073, the Freedom Explore Program of Central South University No.201012200124, the U.S. National Science Foundation under Grants CCF-0514750, CCF-0646102, and CNS-0831634.
- Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Res 2004, 5: 101–114. 10.1038/nrg1272Google Scholar
- Girvan M, Newman ME: Community structure in social and biological networks. Proc Natl Acad Sci 2002, 99: 7821–7826. 10.1073/pnas.122653799PubMed CentralView ArticlePubMedGoogle Scholar
- Bader GD, Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinf 2003, 4: 2. 10.1186/1471-2105-4-2View ArticleGoogle Scholar
- King AD, Przulj N, Jurisica I: Protein complex prediction via cost-based clustering. Bioinformatics 2004, 20(17):3013–3020. 10.1093/bioinformatics/bth351View ArticlePubMedGoogle Scholar
- Li XL, Tan S, Foo C, Ng S: Interaction graph mining for protein complexes using local clique merging. Genome Infor 2005, 16(2):260–269.Google Scholar
- Altaf-Ul-Amin M, Shinbo Y, Mihara K, et al.: Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinf 2006, 7: 207–219. 10.1186/1471-2105-7-207View ArticleGoogle Scholar
- Luo F, Yang Y, Chen CF, et al.: Modular organization of protein interaction networks. Bioinformatics 2007, 23(2):207–214. 10.1093/bioinformatics/btl562View ArticlePubMedGoogle Scholar
- Li M, Chen J, Wang J, et al.: Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. BMC Bioinfo 2008, 9: 398. 10.1186/1471-2105-9-398View ArticleGoogle Scholar
- Wu M, Li XL, Kwoh C, Ng S: A Core-Attachment based Method to Detect Protein Complexes in PPI Networks. BMC Bioinf 2009, 10: 169. 10.1186/1471-2105-10-169View ArticleGoogle Scholar
- Peng J, Singh M: SPICi: a fast clustering algorithm for large biological networks. Bioinformatics 2010, 26(8):1105–1111. 10.1093/bioinformatics/btq078View ArticleGoogle Scholar
- Przytycka TM, Singh M, Slonim DK: Toward the dynamic interactome: it’s about time. Briefings Bioinf 2010, 11(1):15–29. 10.1093/bib/bbp057View ArticleGoogle Scholar
- Han D, Bertin N, Hao T, et al.: Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 2004, 430: 88–93. 10.1038/nature02555View ArticlePubMedGoogle Scholar
- Liang H, Li H: MicroRNA regulation of human protein-protein interaction network. RNA 2007, 13: 1402–1408. 10.1261/rna.634607PubMed CentralView ArticlePubMedGoogle Scholar
- Przytycka TM, Kim Y: Network integration meets network dynamics. BMC Biol 2010, 8: 48. 10.1186/1741-7007-8-48PubMed CentralView ArticlePubMedGoogle Scholar
- Lin C, Hsiang J, Wu C, et al.: Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy. BMC Syst Biol 2010, 4: 138. 10.1186/1752-0509-4-138PubMed CentralView ArticlePubMedGoogle Scholar
- Tang X, Wang J, Liu B, et al.: A comparison of the functional modules identified from time course and static PPI network data. BMC Bioinf 2011, 12: 339. 10.1186/1471-2105-12-339View ArticleGoogle Scholar
- Jansen R, Greenbaum D, Gerstein M: Relating whole-genome expression data with protein-protein interactions. Genome Res 2002, 12: 37–46. 10.1101/gr.205602PubMed CentralView ArticlePubMedGoogle Scholar
- Tornow S, Mewes HW: Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Res 2003, 31: 6283–6289. 10.1093/nar/gkg838PubMed CentralView ArticlePubMedGoogle Scholar
- Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL: Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 2009, 27: 199–204. 10.1038/nbt.1522View ArticlePubMedGoogle Scholar
- Xue H, Xian B, Dong D, Xia K, Zhu S, Zhang Z, Hou L, Zhang Q, Zhang Y, Han JD: A modular network model of aging. Mol Syst Biol 2007, 3: 147.PubMed CentralView ArticlePubMedGoogle Scholar
- Lu H, Shi B, Wu G, et al.: Integrated analysis of multiple data sources reveals modular structure of biological networks. Biochem Biophys Res Commun 2006, 345(1):302–309. 10.1016/j.bbrc.2006.04.088View ArticlePubMedGoogle Scholar
- Cline MS, Smoot M, Cerami E, et al.: Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2007, 2(10):2366–2382. 10.1038/nprot.2007.324PubMed CentralView ArticlePubMedGoogle Scholar
- Maraziotis IA, Dimitrakopoulou K, Bezerianos A: Growing functional modules from a seed protein via integration of protein interaction and gene expression data. BMC Bioinf 2007, 8: 408. 10.1186/1471-2105-8-408View ArticleGoogle Scholar
- Cho Y, Hwang W, Zhang A: Efficient modularization of weighted protein interaction networks using k-Hop graph reduction. In . IEEE Computer Society, Arlington, Virginia; 2006:289–298.Google Scholar
- Luo F, Liu J, Li J: Discovering conditional co-regulated protein complexes by integrating diverse data sources. BMC Syst Biol 2010, 4(Suppl 2):S4. 10.1186/1752-0509-4-S2-S4PubMed CentralView ArticlePubMedGoogle Scholar
- Hegde SR, Manimaran P, Mande SC: Dynamic changes in protein functional linkage networks revealed by integration with gene expression data. PLoS Comput Biol 2008, 4(11):e1000237. [doi:10.1371/journal.pcbi.1000237] [doi:10.1371/journal.pcbi.1000237] 10.1371/journal.pcbi.1000237PubMed CentralView ArticlePubMedGoogle Scholar
- De Lichtenberg U, Jensen LJ, Brunak S, Bork P: Dynamic complex formation during the yeast cell cycle. Science 2005, 307: 724–727. 10.1126/science.1105103View ArticlePubMedGoogle Scholar
- Komurov K, White M: Revealing static and dynamic modular architecture of the eukaryotic protein interaction network. Mol Syst Biol 2007, 3: 110.PubMed CentralView ArticlePubMedGoogle Scholar
- Camargo A, Azuaje F: Linking gene expression and functional network data in human heart failure. PLoS ONE 2007, 2: e1347. 10.1371/journal.pone.0001347PubMed CentralView ArticlePubMedGoogle Scholar
- Spirin V, Mirny LA: Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci 2003, 100(21):12123–12128. 10.1073/pnas.2032324100PubMed CentralView ArticlePubMedGoogle Scholar
- Wang J, Li M, Chen J, Pan Y: A fast hierarchical clustering algorithm for functional modules discovery in protein interaction networks. IEEE/ACM Trans Comput Biol Bioinf 2011, 8(3):607–620.View ArticleGoogle Scholar
- van Dongen HG: Graph clustering by flow simulation. PhD thesis. University of Utrecht; 2000.Google Scholar
- Enright AJ, Van Dongen S, Ouzounis CA: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 2002, 30(7):1575–1584. 10.1093/nar/30.7.1575PubMed CentralView ArticlePubMedGoogle Scholar
- Palla G, Dernyi I, Farkas I, Vicsek T: Uncovering the overlapping community structure of complex networks in nature and society. Nature 2005, 435(7043):814–818. 10.1038/nature03607View ArticlePubMedGoogle Scholar
- Xenarios I, et al.: DIP: the Database of Interaction Proteins: a research tool for studying cellular networks of protien interactions. Nucleic Acids Res 2002, 30: 303–305. 10.1093/nar/30.1.303PubMed CentralView ArticlePubMedGoogle Scholar
- Tu BP, Kudlicki A, Rowicka M, McKnight SL: Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes. Science 2005, 310: 1152–1158. 10.1126/science.1120499View ArticlePubMedGoogle Scholar
- Brohee S, van Helden J: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinf 2006, 7: 488. 10.1186/1471-2105-7-488View ArticleGoogle Scholar
- Adamcsek B, Palla G, Farkas IJ, et al.: CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 2006, 22(8):1021–1023. 10.1093/bioinformatics/btl039View ArticlePubMedGoogle Scholar
- Pu S, Wong J, Turner B, et al.: Up-to-date catalogues of yeast protein complexes. Nucleic Acids Res 2008, 37(3):825–831.PubMed CentralView ArticlePubMedGoogle Scholar
- Gavin AC, et al.: Proteome survey reveals modularity of the yeast cell machinery. Nature 2006, 440: 631–636. 10.1038/nature04532View ArticlePubMedGoogle Scholar
- Cherry JM, et al.: SGD: Saccharomyces Genome Database. Nucleic Acids Res 1998, 26: 73–79. 10.1093/nar/26.1.73PubMed CentralView ArticlePubMedGoogle Scholar
- Asur S, Ucar D, Parthasarathy S: An ensemble framework for clustering protein-protein interaction networks. ISMB/ECCB 2007. Bioin 2007, 23: i29-i40.Google Scholar
- Wang J, Li M, Deng Y, Pan Y: Recent advances in clustering methods for protein interaction networks. BMC Genomics 2010, 11(Suppl 3):S10. 10.1186/1471-2164-11-S3-S10View ArticleGoogle Scholar
- Carey M, Peterson CL, Smale ST: Transcriptional regulation in eukaryotes: concepts, strategies, and techniques. Cold Spring Harbor Laboratory Press, ; 2009.Google Scholar
- Samara NL, Wolberger C: A new chapter in the transcription SAGA. Curr Opin Struct Biol 2011, 21(6):767–774. 10.1016/j.sbi.2011.09.004PubMed CentralView ArticlePubMedGoogle Scholar
- Lee TI, Causton HC, Holstege FC, et al.: Redundant roles for the TFIID and SAGA complexes in global transcription. Nature 2000, 405: 701–704. 10.1038/35015104View ArticlePubMedGoogle Scholar
- Utley RT, Lacoste N, Jobin-Robitaille O, et al.: Regulation of NuA4 histone acetyltransferase activity in transcription and DNA repair by phosphorylation of histone H4. Mol Cell Biol 2005, 25(18):8179–8190. 10.1128/MCB.25.18.8179-8190.2005PubMed CentralView ArticlePubMedGoogle Scholar
- Okamoto T, Yamamoto S, Watanabe Y, et al.: Analysis of the role of TFIIE in transcriptional regulation through structure-function studies of the TFIIEbeta subunit. J Biol Chem 1998, 273(31):19866–19876. 10.1074/jbc.273.31.19866View ArticlePubMedGoogle Scholar
- Morel A-P, Sentis S, Bianchin C, et al.: BTG2 antiproliferative protein interacts with the human CCR4 complex existing in vivo in three cell-cycle-regulated forms. J Cell Sci 2003, 116: 2929–2936. 10.1242/jcs.00480View ArticlePubMedGoogle Scholar
- Campbell RN, Michael K, et al.: Metabolic control of transcription: paradigms and lessons from Saccharomyces cerevisiae. Biochem J 2008, 414: 177–187. 10.1042/BJ20080923View ArticlePubMedGoogle Scholar
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