Detecting temporal protein complexes from dynamic protein-protein interaction networks

Background

Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results

In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions

Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users.     

A +TIP for a smooth trip

Is there a cellular mechanism for preventing a depolymerizing microtubule track from “slipping out from under” its cargo? A recent study in budding yeast indicates that when a chromosome is transported to the minus end of a spindle microtubule, its kinetochore-bound microtubule plus end–tracking protein (+TIP) Stu2 may move to the plus end to promote rescue; i.e., to switch the depolymerizing end to a polymerizing end. The possibility that other +TIPs may play a similar role in sustaining a microtubule track during vesicular transport deserves investigation.Microtubule motor proteins such as dynein and kinesins are responsible for transporting cellular cargos along microtubule tracks (Vale, 2003). The net direction and speed of cargo movement, however, are likely to be regulated in a very complicated fashion, especially when a cargo is bound to multiple motors with opposite directionalities (Vale 2003; Mallik and Gross 2004; Levi et al., 2006). The fact that the microtubule track is not very stable further complicates matters. The plus ends of microtubules, which face the cell periphery in most cell types, are highly dynamic, exhibiting alternating periods of polymerization (growth) and depolymerization (shrinkage; Desai and Mitchison, 1997). Such plus end dynamics may be useful for searching and capturing relatively stationary cargos near the cell periphery that need to be transported inward (Vaughan et al., 2002). However, the dynamic nature of the track can also create an obvious problem for the transport process. If a microtubule''s rate of shrinkage is greater than the rate of cargo transport, then the microtubule may shrink past an attached cargo, causing its dissociation from the track. Does this happen in cells, or do cells have a mechanism to prevent it?Although this question has never been directly addressed, a recent study on budding yeast chromosome segregation has shed new light on the issue (Tanaka et al., 2005). In this study, the authors took advantage of a strategy that allowed them to specifically shut off the function of a single kinetochore, thereby preventing it from attaching to a spindle microtubule while, at the same time, permitting other kinetochores to attach to the spindle. After the function of this single kinetochore was switched back on, the behavior of its associated chromosome on a spindle microtubule was subjected to a detailed image analysis. Several important insights from this study on chromosome–microtubule interactions during mitosis have been recently reviewed (Bloom 2005), and, thus, only those observations that pertain to cargo transport will be highlighted here. The chromosome was first seen to undergo a lateral interaction with the microtubule followed by minus end–directed transport toward the pole. The mechanism of the minus end–directed transport is not entirely clear, although a member of the kinesin-14 family, Kar3, may be one of the players in this process (Tanaka et al., 2005). During transport, the attached microtubule can undergo shrinkage with a rate higher than that of the minus end–directed chromosome movement (Tanaka et al., 2005); however, it never shrank beyond the position of the cargo. Such exquisite control over the extent of shrinkage appears to rely on a conversation between the cargo and the plus end of the microtubule that is mediated by the microtubule plus end–tracking protein Stu2 (Bloom 2005; Tanaka et al., 2005).Microtubule plus end–tracking proteins (+TIPs) are a class of proteins that use different structural motifs or specific targeting mechanisms to localize to the dynamic plus ends of microtubules (Carvalho et al., 2003; Akhmanova and Hoogenraad, 2005). Although most +TIPs associate with only the growing ends of microtubules, several +TIPs also localize to the shrinking ends (Carvalho et al., 2003; 2004; Akhmanova and Hoogenraad, 2005; Mennella et al., 2005; Sproul et al., 2005; Molk et al., 2006; Wu et al., 2006). Many +TIPs have been found to impact microtubules by either promoting their growth or promoting dynamic behavior. Stu2 is a member of the XMAP215/TOG/Dis1/DdCP224 family of proteins that have been shown to affect microtubule dynamics in multiple ways depending on different experimental conditions (Ohkura et al., 2001; Popov and Karsenti 2003; Holmfeldt et al., 2004; Akhmanova and Hoogenraad, 2005). In vitro, Stu2 binds to the plus ends of preformed microtubules and promotes catastrophe, which is a switch from growth to shrinkage (van Breugel et al., 2003). In vivo studies using mutants of Stu2, however, indicate that Stu2 promotes microtubule growth (Severin et al., 2001) and the dynamics of both kinetochore and cytoplasmic microtubules (Kosco et al., 2001; Pearson et al., 2003). During anaphase B spindle elongation, Stu2 may antagonize the function of Kip3 (a kinesin-13 family member) to promote the plus end polymerization of overlapping microtubules (Severin et al., 2001). Although it is not fully understood how or why Stu2 is so versatile, it is well recognized that the in vivo interactions among +TIPs are very complicated, and the loss of function of a +TIP in vivo may decrease or increase the accumulation of other +TIPs that also regulate microtubule dynamics (Carvalho et al., 2003; 2004; Lansbergen et al., 2004; Akhmanova and Hoogenraad, 2005; Galjart 2005; Komarova et al., 2005). Tanaka et al. (2005) identified Stu2 as a rescue (a switch from shrinkage to growth) factor based not on phenotypic studies of Stu2 mutants but, instead, on a direct observation of the relationship between microtubule plus end behavior and Stu2 localization. They found that Stu2 was localized at the plus ends of microtubules emanating from the spindle pole body, and, during periods of microtubule shrinkage, Stu2 levels at the plus ends were decreased. Interestingly, Stu2 was also localized at the unbound kinetochore. When the kinetochore subsequently attached laterally to a spindle microtubule and underwent minus end–directed transport, the Stu2 proteins were transported from the kinetochore to the microtubule plus end. The arrival of Stu2 at the plus end closely correlated to the rescue of the shrinking microtubule (Tanaka et al., 2005). These observations strongly suggest that the Stu2 carried by the kinetochore may serve as a rescue factor for the microtubule track, preventing it from vanishing before the migrating chromosome.Could such a scenario exist during microtubule-dependent transport of nonchromosomal cargoes during interphase? We do not yet know the answer. However, based on published studies, it seems reasonable to hypothesize that other +TIPs, especially the cytoplasmic linker protein CLIP-170, may function in a manner similar to yeast Stu2 to ensure a safe trip for a minus end–directed cargo. CLIP-170 contains CAP-Gly microtubule-binding motifs at its NH₂ terminus and was initially identified as a protein required for linking endocytic vesicles to microtubules in vitro (Pierre et al., 1992; Rickard and Kreis 1996). Later, CLIP-170 was identified as a founding member of the microtubule plus end–tracking proteins (Perez et al., 1999). The connection between CLIP-170''s in vitro endosome–microtubule linking property and its in vivo plus end tracking behavior has not been clearly made. Could an endocytic vesicle use its bound CLIP-170 as a rescue factor to prevent the disappearance of the track on which it is traveling?CLIP-170 is indeed considered to be a rescue factor in mammalian cells (Komarova et al., 2002a). Komarova et al. (2002b) have found that in cultured CHO and NRK cells, microtubule dynamics seem to be controlled spatially; catastrophe and rescue occur frequently only near the cell periphery. Although the mechanisms behind catastrophe and rescue are not fully understood, protein factors are required for regulating both events in vivo (Desai and Mitchison, 1997). In CHO cells, a dominant-negative form of CLIP-170 that displaces the endogenous CLIP-170 from microtubule plus ends severely reduces the rescue frequency so that microtubules are more likely to shrink all the way back to the microtubule-organizing center (Komarova et al., 2002a). Moreover, both in vivo and in vitro studies suggest that the rescue activity of CLIP-170 is localized to the NH₂ terminus containing the CAP-Gly motifs (Komarova et al., 2002a; Arnal et al., 2004). How CLIP-170 rescues a shrinking end is not clear. CLIP-170 can promote tubulin oligomerization (Diamantopoulos et al., 1999; Arnal et al., 2004), and it is likely that this property serves to increase the local concentration of tubulin substrate, thereby lowering the entropic barrier for the polymerization reaction. CLIP-170 in mammalian cells has only been found at growing plus ends, most likely as a result of copolymerization with tubulin subunits followed by its release from older segments (Diamantopoulos et al., 1999; Perez et al., 1999; Folker et al., 2005). When a microtubule end shrinks, CLIP-170 falls off. Is there a mechanism to get CLIP-170 close to the depolymerizing end and facilitate its function as a rescue factor? Given the proposed function of Stu2 as a rescue factor for spindle microtubules, one may easily imagine a similar scenario in which vesicle-bound CLIP-170 may be transported to the approaching microtubule end to rescue it from further shrinkage.If vesicle-bound CLIP-170 is transported to the plus end in a manner similar to Stu2, could such transport be mediated by plus end–directed kinesins? Although the kinesin involved in transporting Stu2 toward the microtubule plus end still needs to be identified, detailed image analyses have revealed a role for the Kip2/Tea2 kinesins (members of the kinesin-7 family) in transporting CLIP-170 homologues in fungi (Busch et al., 2004; Carvalho et al., 2004). Bik1 and Tip1 are the CLIP-170 homologues in budding and fission yeasts, respectively, and these proteins are found at microtubule plus ends, where they act as growth-promoting factors or anticatastrophe factors (Berlin et al., 1990; Brunner and Nurse 2000; Carvalho et al., 2004). In both yeasts, the Kip2/Tea2 kinesins bind to and comigrate with the CLIP-170 homologues along the microtubule toward the plus end (Busch et al., 2004; Carvalho et al., 2004). Kinesins have also been implicated in targeting other +TIPs to microtubule plus ends (Jimbo et al., 2002; Maekawa et al., 2003; Zhang et al., 2003; Wu et al., 2006). For example, the mammalian tumor suppressor protein APC (adenomatous polyposis coli) may be targeted to the plus end by KIF3A/KIF3B (a heterotrimeric kinesin II in the kinesin-2 family) as well as by other mechanisms (Jimbo et al., 2002; Nathke 2004; Slep et al., 2005). It will be interesting to see whether a similar transport process for CLIP-170 exists in higher eukaryotic cells. It is possible that such a mechanism would deliver just enough CLIP-170 to the shrinking plus end to initiate rescue. When microtubule growth is resumed, CLIP-170''s intrinsic higher affinity for tubulin subunits and lower affinity for the microtubule wall may allow these proteins to “treadmill” on the growing end (Perez et al., 1999; Folker et al., 2005).The regulation of CLIP-170 activity appears to be rather complex. CLIP-170 is most likely phosphorylated by multiple kinases, including FKBP12–rapamycin-associated protein (mTOR; Choi et al., 2002). Although phosphorylation by mTOR/FKBP 12–rapamycin-associated protein may stimulate CLIP-170''s microtubule binding, phosphorylation by other kinases may cause CLIP-170 to dissociate from microtubules (Rickard and Kreis 1996; Choi et al., 2002). In vivo, CLIP-170 has a closed conformation that is presumably inactive and an open conformation that may interact with microtubules and dynein regulators such as dynactin (Schroer 2004) and LIS1 (Morris et al., 1998; Lansbergen et al., 2004). It is possible that phosphorylation may regulate the conversion between these two forms, but the specific mechanism and the spatial regulation for this conversion have yet to be resolved. If CLIP-170 is indeed released from a membranous cargo to move to the plus end in order to serve as a rescue factor, it would be interesting to know when and/or where such a conformational switch occurs. Finally, other proteins may play redundant roles with CLIP-170 in vesicular trafficking, which may explain why a dramatic defect in vesicle/organelle distribution is not detected when the CLIP-170 level is lowered or when the gene is knocked out (Lansbergen et al., 2004; Akhmanova et al., 2005).+TIPs other than CLIP-170 may play a similar role in rescuing shrinking microtubule tracks. For example, the dynactin complex that links dynein to membranous cargoes and promotes the processive motion of dynein (Schroer 2004) may act as a rescue factor. The p150^Glued subunit of dynactin and CLIP-170 both contain CAP-Gly microtubule-binding motifs at their NH2 termini, although p150^Glued contains one, whereas CLIP-170 contains two such motifs. Dynactin has been shown to behave as a +TIP facilitating the capture of vesicular cargo for minus end–directed transport (Vaughan et al., 1999; 2002). The head domain of the p150^Glued subunit containing the CAP-Gly motif has been shown to promote rescue in vivo in the absence of endogenous CLIP-170, although the effect was much weaker than that caused by the exogenous CLIP-170 head domain (Kamarova et al., 2002a). In vitro studies showed that dynactin may promote nucleation during microtubule assembly (Ligon et al., 2003), which is consistent with it being a potential rescue factor. As shown with CLIP-170, this capacity to bring multiple tubulins together may help to overcome the entropic barrier of the polymerization reaction. Finally, cargo-bound dynactin may also use kinesin to get to the plus end. The p150^Glued subunit of dynactin has been shown to interact directly with the COOH terminus of KAP3, a subunit of the heterotrimeric kinesin II (a member of the kinesin-2 family) that also binds to APC (Jimbo et al., 2002; Deacon et al., 2003; Dell 2003). Although this binding is implicated in dynactin''s role as a cargo adaptor for kinesin II, it is possible, in theory, that a small amount of dynactin may use this connection to move to the plus end.The proposed hypothesis that +TIPs may be released from a membranous cargo to rescue a shrinking microtubule track may apply to both minus and plus end–directed transport. In addition, it is important to point out that this hypothesis does not exclude other mechanisms for rescuing long microtubule tracks. Rescue may occur stochastically, and, sometimes, +TIPs may participate in other ways such as mediating microtubule capture by the actin-rich cortex to stabilize the track (Wen et al., 2004; Galjart 2005). In some situations, microtubule dynamics are modulated by the direct binding of membranous cargo to the growing or shrinking plus ends of microtubules (Waterman-Storer and Salmon, 1998).Currently, the ability of CLIP-170 or other +TIPs to be released from a membranous cargo and to act as a rescue factor for a shrinking microtubule is just a hypothesis. Nevertheless, searching for proteins involved in the communication between a cargo and the approaching shrinking end of its microtubule track is clearly an endeavor worth pursuing.     

Global nature of dynamic protein-chromatin interactions in vivo: three-dimensional genome scanning and dynamic interaction networks of chromatin proteins

Genome structure and gene expression depend on a multitude of chromatin-binding proteins. The binding properties of these proteins to native chromatin in intact cells are largely unknown. Here, we describe an approach based on combined in vivo photobleaching microscopy and kinetic modeling to analyze globally the dynamics of binding of chromatin-associated proteins in living cells. We have quantitatively determined basic biophysical properties, such as off rate constants, residence time, and bound fraction, of a wide range of chromatin proteins of diverse functions in vivo. We demonstrate that most chromatin proteins have a high turnover on chromatin with a residence time on the order of seconds, that the major fraction of each protein is bound to chromatin at steady state, and that transient binding is a common property of chromatin-associated proteins. Our results indicate that chromatin-binding proteins find their binding sites by three-dimensional scanning of the genome space and our data are consistent with a model in which chromatin-associated proteins form dynamic interaction networks in vivo. We suggest that these properties are crucial for generating high plasticity in genome expression.     

Protein interaction networks

The study of protein interactions is playing an ever increasing role in our attempts to understand cells and diseases on a system-wide level. This article reviews several experimental approaches that are currently being used to measure protein-protein, protein-DNA and gene-gene interactions. These techniques have now been scaled up to produce extensive genome-wide data sets that are providing us with a first glimpse of global interaction networks. Complementing these experimental approaches, several computational methodologies to predict protein interactions are also reviewed. Existing databases that serve as repositories for protein interaction information and how such databases are used to analyze high-throughput data from a pathway perspective is also addressed. Finally, current efforts to combine multiple data types to obtain more accurate and comprehensive models of protein interactions are discussed. It is clear that the evolution of these experimental and computational approaches is rapidly changing our view of biology, and promises to provide us with an unprecedented ability to model cells and organisms at a system-wide level.     

Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles

Background

Protein interaction networks (PINs) are known to be useful to detect protein complexes. However, most available PINs are static, which cannot reflect the dynamic changes in real networks. At present, some researchers have tried to construct dynamic networks by incorporating time-course (dynamic) gene expression data with PINs. However, the inevitable background noise exists in the gene expression array, which could degrade the quality of dynamic networkds. Therefore, it is needed to filter out contaminated gene expression data before further data integration and analysis.

Results

Firstly, we adopt a dynamic model-based method to filter noisy data from dynamic expression profiles. Then a new method is proposed for identifying active proteins from dynamic gene expression profiles. An active protein at a time point is defined as the protein the expression level of whose corresponding gene at that time point is higher than a threshold determined by a standard variance involved threshold function. Furthermore, a noise-filtered active protein interaction network (NF-APIN) is constructed. To demonstrate the efficiency of our method, we detect protein complexes from the NF-APIN, compared with those from other dynamic PINs.

Conclusion

A dynamic model based method can effectively filter out noises in dynamic gene expression data. Our method to compute a threshold for determining the active time points of noise-filtered genes can make the dynamic construction more accuracy and provide a high quality framework for network analysis, such as protein complex prediction.

     

Gene networks inference using dynamic Bayesian networks

This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.     

Analyzing protein-protein interaction networks

The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.     

Evidence for differing modes of interaction of acriflavine with ultraviolet-induced lesions in an Hcr + bacterial strain

Summary In buffer suspensions of UV-irradiated Escherichia coli B/r WP2 Hcr⁺ (auxotrophic for tryptophan) acriflavine binds to DNA, but this treatment has little effect on killing and results in the appearance of fewer prototrophs on tryptophan-supplemented minimal agar. If plates contain a broth supplement, however, the buffer-acriflavine treatment greatly increases the yield of UV-induced prototrophs; but this increase does not depend on complete binding of acriflavine to the DNA as a whole, since it is observed with contact times too short for this to occur (as short as 20 seconds). The incorporation of acriflavine in both kinds of plating medium increases the yields of prototrophs. The maximum yield is observed when irradiated bacteria are exposed to acriflavine in buffer before they are plated on medium containing both acriflavine and a broth supplement. Thus post-irradiation effects of acriflavine cannot be accounted for in terms of a single mechanism of action. Our results support the suggestion that phenomena classed together as mutation frequency decline may not represent a single specific repair system.     

Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.     

Discovering functional interaction patterns in protein-protein interaction networks

Background  

In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI) network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution of PPI networks and identification of conserved subnetworks across different species, discovery of modules of interaction, use of PPI networks for functional annotation of uncharacterized proteins, and improvement of the accuracy of currently available networks.     

Parsimonious test of dynamic interaction

In recent years, there have been significant advances in the technology used to collect data on the movement and activity patterns of humans and animals. GPS units, which form the primary source of location data, have become cheaper, more accurate, lighter and less power‐hungry, and their accuracy has been further improved with the addition of inertial measurement units. The consequence is a glut of geospatial time series data, recorded at rates that range from one position fix every several hours (to maximize system lifetime) to ten fixes per second (in high dynamic situations). Since data of this quality and volume have only recently become available, the analytical methods to extract behavioral information from raw position data are at an early stage of development. An instance of this lies in the analysis of animal movement patterns. When investigating solitary animals, the timing and location of instances of avoidance and association are important behavioral markers. In this paper, a novel analytical method to detect avoidance and association between individuals is proposed; unlike existing methods, assumptions about the shape of the territories or the nature of individual movement are not needed. Simulations demonstrate that false positives (type I error) are rare (1%–3%), which means that the test rarely suggests that there is an association if there is none.     

Suppression of microtubule dynamic instability by the +TIP protein EB1 and its modulation by the CAP-Gly domain of p150glued

The EB1+TIP protein family and its binding partners track growing plus ends of microtubules in cells and are thought to regulate their dynamics. Here we determined the effects of EB1 and the N-terminal CAP-Gly domain (p150n) of one of its major binding partners, p150Glued, both separately and together, on the dynamic instability parameters at plus ends of purified steady-state microtubules. With EB1 alone, the shortening rate, the extent of shortening, and the catastrophe frequency were suppressed in the absence of significant effects on the growth rate or rescue frequency. The effects of EB1 on dynamics were significantly different when p150n was added together with EB1. The rate and extent of shortening and the catastrophe frequency were suppressed 3-4 times more strongly than with EB1 alone. In addition, the EB1-p150n complex increased the rescue frequency and the mean length the microtubules grew, parameters that were not significantly affected by EB1 alone. Similarly, deletion of EB1's C-terminal tail, which is a crucial binding region for p150n, significantly increased the ability of EB1 to suppress shortening dynamics. EB1 by itself bound along the length of the microtubules with 1 mol of EB1 dimer bound per approximately 12 mol of tubulin dimer. Approximately twice the amount of EB1 was recruited to the microtubules in the presence of p150n. Our results indicate that inactivation of EB1's flexible C-terminal tail significantly changes EB1's ability to modulate microtubule dynamics. They further suggest that p150Glued may activate and thereby facilitate the recruitment of EB1 to the tips of microtubules to regulate their dynamics.     

Binary versus weighted interaction networks

Interaction networks (IN) have been used in ecology to model different kinds of interactions in ecological communities. Historically there are two basic ways to construct an IN: binary networks (BN) that represent unweighted links among species in the web, and weighted networks (WN) that weight each interaction among species by its relative or absolute frequency in the web. We call binary reduction the transition from WN to BN which obviously entails loss of information. We performed an analysis with 69 WN on which we worked the binary reduction. For both WN and BN we computed: the coefficient of variation, skewness, kurtosis, Shannon entropy and the Gini coefficient on the population statistics. We also computed the dependence asymmetry, the pairwise Jaccard distance and two different measures of nestedness, (W)NODF and τ-temperature, for the WN and BN. From correlations between the values for WN and BN we concluded that, for most of the indices, the loss of information due to the binary reduction is not significant. Using a statistical evaluation, for most indices, BN give similar results to their corresponding WN.     

Protein interaction networks in plants

Protein–protein interactions are fundamental to virtually every aspect of cellular functions. With the development of high-throughput technologies of both the yeast two-hybrid system and tandem mass spectrometry, genome-wide protein-linkage mapping has become a major objective in post-genomic research. While at least partial “interactome” networks of several model organisms are already available, in the plant field, progress in this respect is slow. However, even with comprehensive protein interaction data still missing, substantial recent advance in the graph-theoretical functional interpretation of complex network architectures might pave the way for novel approaches in plant research. This article reviews current progress and discussions in network biology. Emphasis is put on the question of what can be learned about protein functions and cellular processes by studying the topology of complex protein interaction networks and the evolutionary mechanisms underlying their development. Particularly the intermediate and local levels of network organization—the modules, motifs and cliques—are increasingly recognized as the operational units of biological functions. As demonstrated by some recent results from systematic analyses of plant protein families, protein interaction networks promise to be a valuable tool for a molecular understanding of functional specificities and for identifying novel regulatory components and pathways.