B- and E-Class MADS-Box Transcription Factors Regulate the Formation of the Labellum in Cymbidium ensifolium.

Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.

BackgroundRecently, researchers have tried to integrate various dynamic information with static protein-protein interaction (PPI) networks to construct dynamic PPI networks. The shift from static PPI networks to dynamic PPI networks is essential to reveal the cellular function and organization. However, it is still impossible to construct an absolutely reliable dynamic PPI networks due to the noise and incompletion of high-throughput experimental data.ResultsTo deal with uncertain data, some uncertain graph models and theories have been proposed to analyze social networks, electrical networks and biological networks. In this paper, we construct the dynamic uncertain PPI networks to integrate the dynamic information of gene expression and the topology information of high-throughput PPI data. The dynamic uncertain PPI networks can not only provide the dynamic properties of PPI, which are neglected by static PPI networks, but also distinguish the reliability of each protein and PPI by the existence probability. Then, we use the uncertain model to identify dynamic protein complexes in the dynamic uncertain PPI networks.ConclusionWe use gene expression data and different high-throughput PPI data to construct three dynamic uncertain PPI networks. Our approach can achieve the state-of-the-art performance in all three dynamic uncertain PPI networks. The experimental results show that our approach can effectively deal with the uncertain data in dynamic uncertain PPI networks, and improve the performance for protein complex identification.

BackgroundAccurate determination of protein complexes has become a key task of system biology for revealing cellular organization and function. Up to now, the protein complex prediction methods are mostly focused on static protein protein interaction (PPI) networks. However, cellular systems are highly dynamic and responsive to cues from the environment. The shift from static PPI networks to dynamic PPI networks is essential to accurately predict protein complex.ResultsThe gene expression data contains crucial dynamic information of proteins and PPIs, along with high-throughput experimental PPI data, are valuable for protein complex prediction. Firstly, we exploit gene expression data to calculate the active time point and the active probability of each protein and PPI. The dynamic active information is integrated into high-throughput PPI data to construct dynamic PPI networks. Secondly, a novel method for predicting protein complexes from the dynamic PPI networks is proposed based on core-attachment structural feature. Our method can effectively exploit not only the dynamic active information but also the topology structure information based on the dynamic PPI networks.ConclusionsWe construct four dynamic PPI networks, and accurately predict many well-characterized protein complexes. The experimental results show that (i) the dynamic active information significantly improves the performance of protein complex prediction; (ii) our method can effectively make good use of both the dynamic active information and the topology structure information of dynamic PPI networks to achieve state-of-the-art protein complex prediction capabilities.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1101-y) contains supplementary material, which is available to authorized users.

In recent years, researchers have tried to inject dynamic information into static protein interaction networks (PINs). The paper first proposes a three-sigma method to identify active time points of each protein in a cellular cycle, where three-sigma principle is used to compute an active threshold for each gene according to the characteristics of its expression curve. Then a dynamic protein interaction network (DPIN) is constructed, which includes the dynamic changes of protein interactions. To validate the efficiency of DPIN, MCL, CPM, and core attachment algorithms are applied on two different DPINs, the static PIN and the time course PIN (TC-PIN) to detect protein complexes. The performance of each algorithm on DPINs outperforms those on other networks in terms of matching with known complexes, sensitivity, specificity, f-measure, and accuracy. Furthermore, the statistics of three-sigma principle show that 23-45% proteins are active at a time point and most proteins are active in about half of cellular cycle. In addition, we find 94% essential proteins are in the group of proteins that are active at equal or great than 12 timepoints of GSE4987, which indicates the potential existence of feedback mechanisms that can stabilize the expression level of essential proteins and might provide a new insight for predicting essential proteins from dynamic protein networks.

Directed Network Wiring Identifies a Key Protein Interaction in Embryonic Stem Cell Differentiation

Malaria continues to be a major concern in developing countries despite continuous efforts to find a cure for the disease. Understanding the pathogenesis mechanism is necessary to identify more effective drug targets against malaria. Many years of experimental research have generated a large amount of data for the malarial parasite, Plasmodium falciparum. These data are useful to understand the importance of certain parasite proteins, but it often remains unclear how these proteins come together, interact with other proteins and carry out their function. Identification of all proteins involved in pathogenesis is an important step towards understanding the molecular mechanism of pathogenesis. In this study, dynamic stage-specific protein-protein interaction networks were created based on gene expression data during the parasite's intra-erythrocytic stages and static protein-protein interaction data. Using previously known proteins of a biological event as seed proteins, the random walk with restart (RWR) method was used on the dynamic protein-protein interaction networks to identify novel proteins related to that event. Two screening procedures namely, permutation test and GO enrichment test were performed to increase the reliability of the RWR predictions. The proposed method was first validated on Plasmodium falciparum proteins related to invasion, where it could reproduce the existing knowledge from a small set of seed proteins. It was then used to identify novel Maurer's clefts resident proteins, where it could identify 152 parasite proteins. We show that the current approach can annotate conserved proteins with unknown function. The predicted proteins can help build a mechanistic model for disease pathogenesis, which will be useful in identifying new drug targets.

Indentifying protein complexes is essential to understanding the principles of cellular systems. Many computational methods have been developed to identify protein complexes in static protein-protein interaction (PPI) network. However, PPI network changes over time, the important dynamics within PPI network is overlooked by these methods. Therefore, discovering complexes in dynamic PPI networks (DPN) is important. DPN contains a series of time-sequenced subnetworks which represent PPI at different time points of cell cycle. In this paper, we propose a dynamic core-attachment algorithm (DCA) to discover protein complexes in DPN. Based on core-attachment assumption, we first detect cores which are small, dense subgraphs and frequently active in the DPN, and then we form complexes by adding short-lived attachments to cores. We apply our DCA to the data of S.cerevisiae and the experimental result shows that DCA outperforms six other complex discovery algorithms, moreover, it reveals that our DCA not only provides dynamic information but also discovers more accurate protein complexes.

Identifying essential proteins is not only important for understanding cellular activity, but also for detecting human disease genes. A series of centrality measures have been proposed to identify essential proteins based on the protein-protein interaction(PPI) network. However, most of the essential proteins identifying algorithms are based on static PPI networks which cannot reflect the dynamic and transient nature of protein interactions. Meanwhile, studies have shown that essentiality is a product of the protein complex rather than the individual protein. Therefore, we proposed a new method to identify essential proteins method by using protein complexes and biological information on the dynamic protein-protein interaction network(IEP-PCD). Experimental results on four Saccharomyces cerevisiae datasets have shown that IEP-PCD can not only improve prediction accuracy but also outperform the other existing prediction methods, including the most commonly-used centrality measures (DC, SC, BC, NC), topology-based methods (LAC) and biological data integrating methods (PeC, CoEWC, and LBCC).

Accurate annotation of protein functions plays a significant role in understanding life at the molecular level. With accumulation of sequenced genomes, the gap between available sequence data and their functional annotations has been widening. Many computational methods have been proposed to predict protein function from protein-protein interaction (PPI) networks. However, the precision of function prediction still needs to be improved. Taking into account the dynamic nature of PPIs, we construct a dynamic protein interactome network by integrating PPI network and gene expression data. To reduce the negative effect of false positive and false negative on the protein function prediction, we predict and generate some new protein interactions combing with proteins’ domain information and protein complex information and weight all interactions. Based on the weighted dynamic network, we propose a method for predicting protein functions, named PDN. After traversing all the different dynamic networks, a set of candidate neighbors is formed. Then functions derived from the set of candidates are scored and sorted, according to the weighted degree of candidate proteins. Experimental results on four different yeast PPI networks indicate that the accuracy of PDN is 18% higher than other competing methods.

Essential proteins play an important role in various life activities and are considered to be a vital part of the organism. Gene expression data are an important dataset to construct dynamic protein-protein interaction networks (DPIN). The existing methods for the construction of DPINs generally utilize all features (or the features in a cycle) of the gene expression data. However, the features observed from successive time points tend to be highly correlated, and thus there are some redundant and irrelevant features in the gene expression data, which will influence the quality of the constructed network and the predictive performance of essential proteins. To address this problem, we propose a construction method of DPINs by using selected relevant features rather than continuous and periodic features. We adopt an improved unsupervised feature selection method based on Laplacian algorithm to remove irrelevant and redundant features from gene expression data, then integrate the chosen relevant features into the static protein-protein interaction network (SPIN) to construct a more concise and effective DPIN (FS-DPIN). To evaluate the effectiveness of the FS-DPIN, we apply 15 network-based centrality methods on the FS-DPIN and compare the results with those on the SPIN and the existing DPINs. Then the predictive performance of the 15 centrality methods is validated in terms of sensitivity, specificity, positive predictive value, negative predictive value, F-measure, accuracy, Jackknife and AUPRC. The experimental results show that the FS-DPIN is superior to the existing DPINs in the identification accuracy of essential proteins.

BackgroundRecently, high-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data which can construct large complex PPI networks for numerous organisms. System biology attempts to understand cellular organization and function by analyzing these PPI networks. However, most studies still focus on static PPI networks which neglect the dynamic information of PPI.ResultsThe gene expression data under different time points and conditions can reveal the dynamic information of proteins. In this study, we used an active probability-based method to distinguish the active level of proteins at different active time points. We constructed dynamic probabilistic protein networks (DPPN) to integrate dynamic information of protein into static PPI networks. Based on DPPN, we subsequently proposed a novel method to identify protein complexes, which could effectively exploit topological structure as well as dynamic information of DPPN. We used three different yeast PPI datasets and gene expression data to construct three DPPNs. When applied to three DPPNs, many well-characterized protein complexes were accurately identified by this method.ConclusionThe shift from static PPI networks to dynamic PPI networks is essential to accurately identify protein complex. This method not only can be applied to identify protein complex, but also establish a framework to integrate dynamic information into static networks for other applications, such as pathway analysis.

Detecting functional modules in Protein-Protein Interaction (PPI) networks is essential to understand gene function, biological pathways and cellular organisation. Majority of methods predict functional modules via the static PPI networks. However, cellular systems are highly dynamic and regulated by the biological networks. Considering the dynamic inherent within these networks, we build the time course PPI networks in terms of the gene expression profiles. And then a novel framework for identifying functional modules with core-attachment structure has been proposed in accordance with the dynamic PPI networks. Our algorithm generates the cores by mining co-expression neighbourhood graphs with an aggregation degree over a threshold and expands them to form functional modules. The method is compared with other competing algorithms based on two different yeast PPI networks. The results show that the proposed framework outperforms state-of-the-art methods.

Functional module detection in Protein-Protein Interaction (PPI) networks is essential to understanding the organization, evolution and interaction of the cellular systems. In recent years, most of the researches have focused on detecting the functional modules from the static PPI networks. However, sometimes the structure of the PPI networks changes in response to stimuli resulting in the changes of both the composition and functionality of these modules. These changes occur gradually and can be thought of as an evolution of the functional modules. In our opinions the evolutionary analysis of functional modules is a key to form important insights of the functional modules' underlying behaviors, particularly when targeting complex living systems. In this paper, we propose a novel computational framework which integrates a PPI network with multiple dynamic gene coexpression networks to categorize and track the evolutionary pattern of functional modules over consecutive time-stamps. We first propose a method to construct dynamic PPI networks, and then design a new functional influence based algorithm to detect the functional modules from these dynamic PPI networks. Based on the results of this approach, we provide a simple but effective method to characterize and track the evolutionary patterns of dynamic modules, which involves detecting evolutionary events between modules found at consecutive timestamps. Extensive experiments on the fermentation process dataset of S. cerevisiae show that the proposed framework not only outperforms previous functional module detection methods, but also efficiently tracks the evolutionary patterns of functional modules.

Critical node detection in dynamic networks is of great value in many areas, such as the evolving of friendship in social networks, the development of epidemics, molecular pathogenesis of diseases and so on. As for detecting critical nodes in dynamic Protein-Protein Interaction Networks (PPINs), there are mainly two challenges: the first is to construct the dynamic PPINs that are not available directly from biological experiments in laboratories; and the second is how to identify the most critical units that are responsible for the dynamic processes. This paper proposes effective framework to tackle these two problems. First of all, this paper proposes to construct the dynamic PPINs by simultaneously modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. As result, more comprehensive dynamic PPINs are built. Besides, a novel critical protein detection method that integrates multiple PPI networks into a Deep Belief Network model (referred to as MIDBN) is developed. The integrated model is trained to get hierarchical common representations of multiple sources which are used to reconstruct the original data. The variabilities of the reconstruction errors across the time courses are ranked to finally get the top proteins that have significantly different evolving structural patterns than the other nodes in the dynamic networks. We evaluated our network construction method by comparing the functional representations of the derived networks with that of two other traditional construction methods, and our method achieved superior function analysis results. The ranking results of critical proteins from MIDBN were compared with results from two baseline methods and the comparison results showed that MIDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.

Human aging is the main social and medicinal challenge where it is one of the factors lead to disease. Understanding the process will be advantageous in the drugs and medicinal fields. In this study, we analyze the process through dynamic protein-protein interaction (PPI) networks by observing the topological changes of the network across ages. The dynamic networks consist of 14 ages, they are within 80 – 99 years, where we call aging-related protein networks. In each of the network, we analyzed the property of assortativity as proposed by recent study. Assortativity in PPI network is a measure to quantify the tendency of proteins to communicate with other similar proteins in the network. However, recent study proposes local assortativity for individual nodes in the network. Consequently, we are able to analyze the characteristics of aging network; i) Aging-related protein networks are disassortative with positive non-linear local assortativity, ii) there are strong and weak assortative key-players, iii) assortative key players are not close to all proteins in aging network, and iv) assortative hubs decrease the robustness of the networks.