A comparison of the functional modules identified from time course and static PPI network data

<p>Abstract</p> <p>Background</p> <p>Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems.</p> <p>Results</p> <p>In this paper, Time Course Protein Interaction Networks (TC-PINs) are reconstructed by incorporating time series gene expression into PPI networks. Then, a clustering algorithm is used to create functional modules from three kinds of networks: the TC-PINs, a static PPI network and a pseudorandom network. For the functional modules from the TC-PINs, repetitive modules and modules contained within bigger modules are removed. Finally, matching and GO enrichment analyses are performed to compare the functional modules detected from those networks.</p> <p>Conclusions</p> <p>The comparative analyses show that the functional modules from the TC-PINs have much more significant biological meaning than those from static PPI networks. Moreover, it implies that many studies on static PPI networks can be done on the TC-PINs and accordingly, the experimental results are much more satisfactory. The 36 PPI networks corresponding to 36 time points, identified as part of this study, and other materials are available at <url>http://bioinfo.csu.edu.cn/txw/TC-PINs.</url></p

Detecting Conserved Protein Complexes Using a Dividing-and-Matching Algorithm and Unequally Lenient Criteria for Network Comparison

The increase of protein–protein interaction (PPI) data of different species makes it possible to identify common subnetworks (conserved protein complexes) across species via local alignment of their PPI networks, which benefits us to study biological evolution. Local alignment algorithms compare PPI network of different species at both protein sequence and network structure levels. For computational and biological reasons, it is hard to find common subnetworks with strict similar topology from two input PPI networks. Consequently some methods introduce less strict criteria for topological similarity. However those methods fail to consider the differences of the two input networks and adopt equally lenient criteria on them. In this work, a new dividing-and-matching-based method, namely UEDAMAlign is proposed to detect conserved protein complexes. This method firstly uses known protein complexes or computational methods to divide one of the two input PPI networks into subnetworks and then maps the proteins in these subnetworks to the other PPI network to get their homologous proteins. After that, UEDAMAlign conducts unequally lenient criteria on the two input networks to find common connected components from the proteins in the subnetworks and their homologous proteins in the other network. We carry out network alignments between S. cerevisiae and D. melanogaster, H. sapiens and D. melanogaster, respectively. Comparisons are made between other six existing methods and UEDAMAlign. The experimental results show that UEDAMAlign outperforms other existing methods in recovering conserved protein complexes that both match well with known protein complexes and have similar functions

Providing SSPCO Algorithm to Construct Static Protein-Protein Interaction (PPI) Networks

Protein-Protein Inter-action Networks are dynamic in reality; i.e. Inter-actions among different proteins may be ineffective in different circumstances and times. One of the most crucial parameters in the conversion of a static network into a temporal graph is the well-tuning of transformation threshold. In this part of the article, using additional data, like gene expression data in different times and circumstances and well-known protein complexes, it is tried to determine an appropriate threshold. To accomplish this task, we transform the problem into an optimization one and then we solve it using a meta-heuristic algorithm, named Particle Swarm Optimization (SSPCO). One of the most important parts in our work is the determination of interestingness function in the SSPCO. It is defined as a function of standard complexes and gene co-expression data. After producing a threshold per each gene, in the following section we will discuss how using these thresholds, active proteins are determined and then temporal graph is created. For final assessment of the produced graph quality, we use graph clustering algorithms and protein complexes determination algorithms. For accomplishing this task, we use MCL, Cluster One, MCODE algorithms. Due to high number of the obtained clusters, the obtained results, if they have some special conditions, will filter out or be merged with each other. Standard performance criteria like Recal, Precision, and F-measure are employed. There is a new proposed criterion named Smoothness. Our experimental results show that the graphs produced by the proposed method outperform the previous methods

Protein function prediction based on protein – protein interaction networks.

This research has expanded the knowledge in Bioinformatics and Data mining. It makes an influential contribution to the future research in this area

Identifying protein complexes and disease genes from biomolecular networks

With advances in high-throughput measurement techniques, large-scale biological data, such as protein-protein interaction (PPI) data, gene expression data, gene-disease association data, cellular pathway data, and so on, have been and will continue to be produced. Those data contain insightful information for understanding the mechanisms of biological systems and have been proved useful for developing new methods in disease diagnosis, disease treatment and drug design. This study focuses on two main research topics: (1) identifying protein complexes and (2) identifying disease genes from biomolecular networks. Firstly, protein complexes are groups of proteins that interact with each other at the same time and place within living cells. They are molecular entities that carry out cellular processes. The identification of protein complexes plays a primary role for understanding the organization of proteins and the mechanisms of biological systems. Many previous algorithms are designed based on the assumption that protein complexes are densely connected sub-graphs in PPI networks. In this research, a dense sub-graph detection algorithm is first developed following this assumption by using clique seeds and graph entropy. Although the proposed algorithm generates a large number of reasonable predictions and its f-score is better than many previous algorithms, it still cannot identify many known protein complexes. After that, we analyze characteristics of known yeast protein complexes and find that not all of the complexes exhibit dense structures in PPI networks. Many of them have a star-like structure, which is a very special case of the core-attachment structure and it cannot be identified by many previous core-attachment-structure-based algorithms. To increase the prediction accuracy of protein complex identification, a multiple-topological-structure-based algorithm is proposed to identify protein complexes from PPI networks. Four single-topological-structure-based algorithms are first employed to detect raw predictions with clique, dense, core-attachment and star-like structures, respectively. A merging and trimming step is then adopted to generate final predictions based on topological information or GO annotations of predictions. A comprehensive review about the identification of protein complexes from static PPI networks to dynamic PPI networks is also given in this study. Secondly, genetic diseases often involve the dysfunction of multiple genes. Various types of evidence have shown that similar disease genes tend to lie close to one another in various biomolecular networks. The identification of disease genes via multiple data integration is indispensable towards the understanding of the genetic mechanisms of many genetic diseases. However, the number of known disease genes related to similar genetic diseases is often small. It is not easy to capture the intricate gene-disease associations from such a small number of known samples. Moreover, different kinds of biological data are heterogeneous and no widely acceptable criterion is available to standardize them to the same scale. In this study, a flexible and reliable multiple data integration algorithm is first proposed to identify disease genes based on the theory of Markov random fields (MRF) and the method of Bayesian analysis. A novel global-characteristic-based parameter estimation method and an improved Gibbs sampling strategy are introduced, such that the proposed algorithm has the capability to tune parameters of different data sources automatically. However, the Markovianity characteristic of the proposed algorithm means it only considers information of direct neighbors to formulate the relationship among genes, ignoring the contribution of indirect neighbors in biomolecular networks. To overcome this drawback, a kernel-based MRF algorithm is further proposed to take advantage of the global characteristics of biological data via graph kernels. The kernel-based MRF algorithm generates predictions better than many previous disease gene identification algorithms in terms of the area under the receiver operating characteristic curve (AUC score). However, it is very time-consuming, since the Gibbs sampling process of the algorithm has to maintain a long Markov chain for every single gene. Finally, to reduce the computational time of the MRF-based algorithm, a fast and high performance logistic-regression-based algorithm is developed for identifying disease genes from biomolecular networks. Numerical experiments show that the proposed algorithm outperforms many existing methods in terms of the AUC score and running time. To summarize, this study has developed several computational algorithms for identifying protein complexes and disease genes from biomolecular networks, respectively. These proposed algorithms are better than many other existing algorithms in the literature