Dynamics of Protein-Protein Interaction Network in Plasmodium Falciparum

Integration of organism-wide protein interactome data with information on expression of genes, cellular localization of proteins and their functions has proved extremely useful in developing biologically intuitive interaction networks. This chapter highlights the dynamics in protein interaction network across different stages in the lifecycle of Plasmodium falciparum, a malarial parasite, and the implication of the network dynamics in different physiological processes. The main focus of the chapter is the integration of information on experimentally derived interactions of P.falciparum proteins with expression data and analysis of the implications of interactions in different cellular processes. Extensive analysis has been made to quantify the interaction dynamics across various stages, as well as correlating it with the dynamics of the cellular pathways involving the interacting proteins. The authors’ analysis demonstrates the power of strategic integration of genome-wide datasets in extracting information on dynamics of biological pathways and processes.

Evolutionary Analyses of Protein Interaction Networks

This chapter introduces evolutionary analyses of protein interaction networks and of proteins as components of the networks. The authors show relationships between proteins in the networks and their evolutionary rates. For understanding protein-protein interaction (PPI) divergence, duplicated genes are often compared because they are derived from a common ancestral gene. In order to reveal evolutionary mechanisms acting on the interactome it is necessary to compare PPIs across species. Investigation of co-localization of interacting genes in a genome shows that PPIs have an important role in the maintenance of a physical link between neighboring genes. The purpose of this chapter is to introduce methodologies for analyzing PPI data and to describe molecular evolution and comparative genomics insights gained from such studies.

Discovering Network Motifs in Protein Interaction Networks

This chapter examines some of the available techniques for analyzing a protein interaction network (PIN) when depicted as an undirected graph. Within this graph, algorithms have been developed which identify “notable” smaller building blocks called network motifs. The authors examine these algorithms by dividing them into two broad categories based on two de?nitions of “notable”: (a) statistically-based methods and (b) frequency-based methods. They describe how these two classes of algorithms differ not only in terms of ef?ciency, but also in terms of the type of results that they report. Some publicly-available programs are demonstrated as part of their comparison. While most of the techniques are generic and were originally proposed for other types of networks, the focus of this chapter is on the application of these methods and software tools to PINs.

Discovering Interaction Motifs from Protein Interaction Networks

Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction data. However, most of the experiments could only answer the question of whether two proteins interact but not the question on the mechanisms by which proteins interact. Such understanding is crucial for understanding the protein interaction of an organism as a whole (the interactome) and even predicting novel protein interactions. Protein interaction usually occurs at some specific sites on the proteins and, given their importance, they are usually well conserved throughout the evolution of the proteins of the same family. Based on this observation, a number of works on finding protein patterns/motifs conserved in interacting proteins have emerged in the last few years. Such motifs are collectively termed as the interaction motifs. This chapter provides a review on the different approaches on finding interaction motifs with a discussion on their implications, potentials and possible areas of improvements in the future.

Network Cleansing

This chapter provides an overview of the current computational methods for PPI network cleansing. The authors first present the issue of identifying reliable PPIs from noisy and incomplete experimental data. Next, they address the questions of which are the expected results of the different experimental studies, of what can be defined as true interactions, of which kind of data are to be integrated in assigning reliability levels to PPIs and which gold standard should the authors use in training and testing PPI filtering methods. Finally, Marcatili and Tramontano describe the state of the art in the field, presenting the different classes of algorithms and comparing their results. The aim of the chapter is to guide the reader in the choice of the most convenient methods, experiments and integrative data and to underline the most common biases and errors to obtain a portrait of PINs which is not only reliable but as well able to correctly retrieve the biological information contained in such data.

Molecular Biology of Protein-Protein Interactions for Computer Scientists

Advances in protein-protein interaction (PPI) detection technology and computational analysis methods have produced numerous PPI networks, whose completeness appears to depend on the extent of data derived from different PPI assay methods and the complexity of the studied organism. Despite the partial nature of human PPI networks, computational data integration and analyses helped to elucidate new interactions and disease pathways. The success of computational analyses considerably depends on PPI data understanding. Exploration of the data and verification of their quality requires basic knowledge of the molecular biology of PPIs and familiarity with the assay methods used to detect PPIs. Both topics are reviewed in this chapter. After introducing various types of PPIs the principles of selected PPI assays are explained and their limitations discussed. Case studies of the Wnt signaling pathway and splice regulation demonstrate some of the challenges and opportunities that arise from assaying and analyzing PPIs. The chapter is concluded with an extrapolation to human systems biology that offers a glimpse into the future of PPI networks.

Domain-Based Prediction and Analysis of Protein-Protein Interactions

Many methods have been proposed for inference of protein-protein interactions from protein sequence data. This chapter focuses on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This chapter overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, and linear programmingbased method. This chapter also reviews a simple evolutionary model of protein domains, which yields a scalefree distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

Module Finding Approaches for Protein Interaction Networks

This chapter provides an overview of the computational approaches developed for exploring the modular organization of protein interaction networks. A special emphasis is placed on the module finding tools implemented in three freely available software packages, VisANT, Cytoscape and MATISSE, as well as on their biomedical applications. The selected methods are presented in the broader context of module discovery options, ranging from approaches that rely merely on topological properties of the underlying network to those that take into account also other complementary data sources, such as the mRNA levels of the proteins. The author will also highlight some current limitations in the measured network data that should be understood when developing and applying module finding methodology, and discuss some key future trends and promising research directions with potential implications for clinical research.

Network Querying Techniques for PPI Network Comparison

The aim of this chapter is that of analyzing and comparing network querying techniques as applied to protein interaction networks. In the last few years, several automatic tools supporting knowledge discovery from available biological interaction data have been developed. In particular, network querying tools search a whole biological network to identify conserved occurrences of a query network module. The goal of such techniques is that of transferring biological knowledge. Indeed, the query subnetwork generally encodes a well-characterized functional module, and its occurrences in the queried network probably denote that this function is featured by the associated organism. The proposed analysis is intended to be useful to understand problems and research issues, state of the art and opportunities for researchers working in this research area.

Discovering Lethal Proteins in Protein Interaction Networks

This chapter introduces state-of-the-art computational methods which discover lethal proteins from Protein Interaction Networks (PINs). Lethal proteins are an interesting subject in understanding the minimal condition for cellular development and survival. A dysfunctional research subject or absence of a lethal protein would result in fatality of the cell. Biological experiments have been conducted to systematically detect such proteins. However, such processes are time consuming and requires huge amount of effort to conduct. The researchers have developed a series of computational methods which take advantage of the network properties of individual proteins to detect lethal proteins in PINs. In this chapter, each computational method is studied in depth with an analysis on its pros and cons. Finally, a discussion on the possible further research directions will conclude the chapter.