Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4+ T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

Pattern discovery in protein structures is a fundamental task in computational biology, with important applications in protein structure prediction, profiling and alignment. We propose a novel approach for pattern discovery in protein structures using Particle Swarm-based flying windows over potentially promising regions of the search space. Using a heuristic search, based on Particle Swarm Optimization (PSO) is, however, easily trapped in local optima due to the sparse nature of the problem search space. Thus, we introduce a novel fitness-based stagnation detection technique that effectively and efficiently restarts the search process to escape potential local optima. The proposed fitness-based method significantly outperforms the commonly-used distance-based method when tested on eight classical and advanced (shifted/rotated) benchmark functions, as well as on two other applications for proteomic pattern matching and discovery. The main idea is to make use of the already-calculated fitness values of swarm particles, instead of their pairwise distance values, to predict an imminent stagnation situation. That is, the proposed fitness-based method does not require any computational overhead of repeatedly calculating pairwise distances between all particles at each iteration. Moreover, the fitness-based method is less dependent on the problem search space, compared with the distance-based method. The proposed pattern discovery algorithms are first applied to protein contact maps, which are the 2D compact representation of protein structures. Then, they are extended to work on actual protein 3D structures and interaction networks, offering a novel and low-cost approach to protein structure classification and interaction prediction. Concerning protein structure classification, the proposed PSO-based approach correctly distinguishes between the positive and negative examples in two protein datasets over 50 trials. As for protein interaction prediction, the proposed approach works effectively on complex, mostly sparse protein interaction networks, and predicts high-confidence protein-protein interactions — validated by more than one computational and experimental source — through knowledge transfer between topologically-similar interaction patterns of close proximity. Such encouraging results demonstrate that pattern discovery in protein structures and interaction networks are promising new applications of the fast-growing and far-reaching PSO algorithms, which is the main argument of this thesis. / Thesis (Ph.D, Computing) -- Queen's University, 2014-04-21 12:54:03.37

Interactions between proteins give rise to many functions in cells. In the lastdecade, highthroughput experiments have identified thousands of protein interactions, which are often represented together as large protein interaction networks. However, the classical way of representing interaction networks, as nodes and edges, is too limited to take dynamic properties such as compatible and mutually exclusive interactions into account. In this work, we study protein interaction networks using structural information. More specifically, the analysis of protein interfaces in threedimensional protein structures enables us to identify which interfaces are compatible and which are not. Based on this principle, we have implemented a method, which aims at the analysis of protein interaction networks from a structural point of view by (1) predicting possible binary interactions for proteins that have been found in complex experimentally and (2) identifying possible mutually exclusive and compatible complexes. We validated our method by using positive and negative reference sets from literature and set up an assay to benchmark the identification of compatible and mutually exclusive structural interactions. In addition, we reconstructed the protein interaction network associated with the G proteincoupled receptor Rhodopsin and defined related functional submodules by combining interaction data with structural analysis of the network. Besides its established role in vision, our results suggest that Rhodopsin triggers two additional signaling pathways towards (1) cytoskeleton dynamics and (2) vesicular trafficking. / Las funciones de las proteínas resultan de la manera con la que interaccionan entre ellas. Los experimentos de alto rendimiento han permitido identificar miles de interacciones de proteínas que forman parte de redes grandes y complejas. En esta tesis, utilizamos la información de estructuras de proteínas para estudiar las redes de interacciones de proteínas. Con esta información, se puede entender como las proteínas interaccionan al nivel molecular y con este conocimiento se puede identificar las interacciones que pueden ocurrir al mismo tiempo de las que están incompatibles. En base a este principio, hemos desarrollado un método que permite estudiar las redes de interacciones de proteínas con un punto de vista mas dinámico de lo que ofrecen clásicamente. Además, al combinar este método con minería de la literatura y Los datos de la proteomica hemos construido la red de interacciones de proteínas asociada con la Rodopsina, un receptor acoplado a proteínas G y hemos identificado sus sub--‐módulos funcionales. Estos análisis surgieron una novel vıa de señalización hacia la regulación del citoesqueleto y el trafico vesicular por Rodopsina, además de su papel establecido en la visión.

Mycobacterium tuberculosis (M.tb), the causative agent of tuberculosis (TB), has remained the largest killer among infectious diseases for over a century. The increasing emergence of drug resistant varieties such as the multidrug resistant (MDR) and extremely drug resistant (XDR) strains are only increasing the global burden of the disease. Available statistics indicate that nearly one-third of the world’s population is infected, where the bacteria remains in the latent state but can reactivate into an actively growing stage to cause disease when the individual is immunocompromised. It is thus immensely important to rethink newer strategies for containing and combating the spread of this disease. Extraction of iron from the host cell is one of the many factors that enable the bacterium to survive in the harsh environments of the host macrophages and promote tuberculosis. Host–pathogen interactions can be interpreted as the battle of two systems, each aiming to overcome the other. From the host’s perspective, iron is essential for diverse processes such as oxygen transport, repression, detoxification and DNA synthesis. Infact, during infection, both the host and the pathogen are known to fight for the available iron, thereby influencing the outcome of the infection. It is of no surprise therefore, that many studies have investigated several components of the iron regulatory machinery of M.tb and the host. However, very few attempts have been made to study the interactions between these components and how such interactions lead to a better adapted phenotype. Such studies require exploration at multiple levels of structural and functional complexity, thereby necessitating the use of a multiscale approach. Systems biology adopts an integrated approach to study and understand the function of biological systems. It involves building large scale models based on individual biochemical interactions, followed by model validation and predictions of the system’s response to perturbations, such as a gene knock-out or exposure to drug. In multiscale modeling, an approach employed in this thesis, a particular biological phenomenon is studied at different spatiotemporal levels. Studying responses at multiple scales provides a broader picture of the communications that occur between a host and pathogen. Moreover, such an analysis also provides valuable insights into how perturbation at a particular level can elicit responses at another level and help in the identification of crucial inter-level communications that can possibly be hindered or activated for a desired physiological outcome. The broad objectives of this thesis was to obtain a comprehensive in silico understanding of mycobacterial iron homeostasis and metabolism, the influence of iron on host-pathogen interactions, identification of key players that mediate such interactions, determination of the molecular consequences of inhibiting the key players and finally the global response of M.tb to altered iron concentration. Perturbation of iron homeostasis holds a strong therapeutic potential, given its essentiality in both the host and the pathogen. Understanding the workings of iron metabolism and regulation in M.tb has been a main objective, so as to ultimately obtain insights about specific therapeutic strategies that capitalize on the criticality of iron concentration. An in-depth study of iron metabolism and regulation is performed at different levels of temporal and spatial scales using diverse methods, each appropriate to investigate biological events associated with the different scales. The specific investigations carried out in the thesis are as follows, a) Reconstruction of a host-pathogen interaction (HPI) model, with focus on iron homeostasis. This study represented the inter-cellular level analysis and was crucial for the identification of key players that mediate communication between the host and pathogen. Additionally, the model also provided a mathematical framework to study the effect of perturbations and gene knock-outs. b) Understanding the influence of iron on IdeR, an iron-responsive transcription factor, also identified as a key player in the HPI model. The study was carried out at the molecular level to identify atomistic details of how IdeR senses iron and the resulting structural modifications, which finally enables IdeR-DNA interaction. The study enabled identification of residues for the functioning of IdeR. c) Genome scale identification of genes that are regulated by IdeR to obtain an overview of the various biological processes affected by changing iron concentrations and IdeR mutation in M.tb. d) To understand the direct and indirect influences of iron and IdeR on the M.tb proteome using large scale protein-protein interaction network. The study enabled identification of highest differentially regulated genes and altered activity of the different biological processes under differing iron concentrations and regulation. e) Systems level analysis of the M.tb metabolome to investigate the metabolic re-adjustments undertaken by M.tb to adapt to altered iron concentration and regulation. The conceptual details and the background of each of the methods used to study the specific aims are provided in the Methodology chapter (Chapter 2). Construction of the host-pathogen interaction (HPI) model and the insights obtained from this study are presented in Chapter 3. A rule based HPI model was built with a focus on the iron regulatory mechanisms in both the host and pathogen. The model consisted of 194 rules, of which 4 rules represented interactions between the host and pathogen. The model not only represented an overview of iron metabolism but also allowed prediction of critical interaction that had the potential to form bottleneck in the system so as to control bacterial proliferation. Infact, model simulation led to the identification of 5 bottlenecks or chokepoints in the system, which if perturbed, could successfully interfere with the host-pathogen dynamics in favour of the host. The model also provided a framework to test perturbation strategies based on the bottlenecks. The study also established the importance of an iron responsive transcription factor, IdeR for regulating iron concentration in the pathogen and mediating host-pathogen interactions. Additionally, the importance of mycobactin and transferrin as key molecular players, involved in host-pathogen dynamics was also determined. The model provided a mathematical framework to test TB pathogenesis and provided significant insights about key molecular players and perturbation strategies that can be used to enhance therapeutic strategies. Given the importance of IdeR in HPI, its molecular mechanism of activation and dimerization was explored in Chapter 4. The main objective of the study was to explore the structural details of IdeR and its iron sensing capacity at the molecular level. A combination of molecular dynamics and protein structure network (PSN) were used to analyse IdeR monomers and dimers in the presence and absence of iron. PSNs used in this thesis are based on non-covalent interactions between sidechain atoms and are quite efficient in identifying iron induced subtle conformational variations. The study distinctly indicated the role of iron in IdeR stability. Further, it was observed that IdeR monomers can take up two major conformations, the ‘open’ and ‘close’ conformation with the iron bound structure preferring the ‘close’ conformation. Major structural changes, such as the N-terminal folding and increased propensity for dimerization were observed upon iron binding. Interestingly, careful analysis of structure suggests a role of these structural modifications towards DNA binding and has been tested in the next chapter. Overall, the results clearly highlight the influence of iron on IdeR activation and dimerization. The predisposition of IdeR to bind to DNA in the presence of metal is clearly visible even when the simulations are performed solely on protein molecules. However, to confirm the conjectures proposed in this chapter and to obtain the atomistic details of IdeR-DNA interactions, the IdeR-DNA complex was investigated. Chapter 5 focuses on the mechanistic details of IdeR-DNA interactions and the influence of iron on the same. IdeR is known to bind to a specific stretch of DNA, known as the ‘iron-box’ motif to form a dimer-of-dimer complex. Molecular dynamics followed by protein-DNA bipartite network analysis was performed on a set of four IdeR-DNA complexes to obtain a molecular level understanding of IdeR-DNA interactions. A striking observation was the dissociation of IdeR-DNA complex in the absence of iron, undoubtedly establishing the importance of iron for IdeR-DNA binding. At the residue level, hydrogen bond and non-covalent interactions clearly established the importance of N-terminal residues for DNA binding, thereby confirming the conjecture put forth in the previous chapter. An important aspect studied in this chapter is the allosteric nature of IdeR-DNA binding. Recent years have witnessed a paradigm shift in the understanding of allostery. Unlike the classical definition of allostery that was based on static structures, the newer definition is based on the conformational ensemble as represented by the shift in the energy landscape of the protein. The allosteric nature of IdeR-DNA complex was probed using simulated trajectories and indeed they suggest iron to be an allosteric regulator of the protein. Finally, based on the known experimental data and observations presented in Chapters 4 and 5, a multi-step model of IdeR activation and DNA binding has been proposed. In chapter 6, a global perspective of IdeR regulation in M.tb was obtained. This was important to gain insights about the influences of iron and its regulation at the M.tb cellular level. A genome scale identification of all possible IdeR targets based on the presence of ‘iron-box’ motif in the promoter region of the genes was carried out. An interesting aspect of this study was the use of energetic information from previous molecular dynamics study as an input for generation of the motif. A total of 255 such IdeR targets were identified and converted into an IdeR target network (IdeRnet). Along with IdeRnet, an unbiased systems level protein-protein interaction network was also generated. To study the response of the pathogen to external perturbations, iron-specific gene expression data was integrated into the network as node weights and edge weights. Analysis of IdeRnet provides interesting associations between fatty acid metabolism and IdeR regulations. Specific genes such as fadD32, DesA3 or lppW have been found to be affected by IdeR mutation. While IdeRnet discusses the direct associations, the global level responses are monitored by analysing pathways for the flow of information in the protein-protein interaction network (PPInet). Comparisons of the PPInets under conditions such as altering iron concentrations and lack of iron homeostasis led to the identification of the ‘top-most’ active paths under the different conditions. The study clearly suggests a halt in the protein synthesis machinery and decreased energy consumption under iron scarcity and an uninhibited consumption of energy when iron homeostasis is perturbed. In the final chapter (Chapter 7), flux balance analyses has been used to investigate the influence of iron on M.tb metabolism. The importance of iron for metabolic enzymes has already been established in the previous chapter. Additionally, M.tb is known to produce siderophores, an important metabolite that requires amino acids as its precursors, for iron extraction. All this, together highlighted the importance of iron and its regulation of M.tb metabolism. Flux balance analysis has been used previously to study the metabolic alterations that occur in an organism under different conditions. For this study, iron specific gene expression data was also incorporated into the model as reaction bounds and the flux values so obtained were compared in different environmental conditions. The study provided valuable insights into the metabolic adjustments taken up by M.tb under iron stress conditions and correlates well with the responses observed from the interactome as well as experimental observations. Most significantly, changes were observed in the energy preferences of the cell. For instance, it was noted that while the wild type strain of M.tb prefers synthesis of ATP via glycolysis, the IdeR mutant strain preferred oxidative phosphorylation. The picture becomes clearer when one accounts for the uncontrolled utilization of energy and rapid activation of protein synthesis machinery in the IdeR mutant strain. Biological systems are inherently multiscale in nature and therefore for a successful drug target regime, analysis of the genome to the phenome, which captures interactions at multiple levels, is essential. In this thesis, a detailed understanding of iron homeostasis and regulation in M.tb at multiple levels has been attempted. More importantly, insights obtained from one level, formed questions in the next level. The study was initiated at the inter-cellular level, where the influence of iron on HPI was modeled and analysed. From this study, IdeR, an iron-responsive transcription factor was identified as a key player that had the potential to alter host-pathogen interactions in the favour of the host. For a complete understanding of how IdeR regulates iron homeostasis, it was imperative to obtain a molecular level insight of its mechanism of action. Finally, the various aspects of IdeR regulation were investigated at the cellular level by analysing direct and indirect influences of IdeR on M.tb proteome and metabolome. The study suggests certain therapeutic interventions, such as 1) reduction in the concentration of free transferrin various, 2) mutations at the N-terminal sites of IdeR, 3) regulation of proteins involved in production of mycolic acids by iron and 4) perturbation of altering energy sources, which capitalize on iron and should be investigated in detail. In summary, the consequences of iron on TB infection were studied by threading different levels. This is based on the belief that most biological functions involve multiple spatio-temporal levels with frequent cross talks between the different levels, thereby making such multiscale approaches very useful.

Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4+ T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

Molecular biology has entered an era of systematic and automated experimentation. High-throughput techniques have moved biology from small-scale experiments focused on specific genes and proteins to genome and proteome-wide screens. One result of this endeavor is the compilation of complex networks of interacting proteins. Molecular biologists hope to understand life's complex molecular machines by studying these networks. This thesis addresses tree open problems centered upon their analysis and quality assessment. First, we introduce power graph analysis as a novel approach to the representation and visualization of biological networks. Power graphs are a graph theoretic approach to lossless and compact representation of complex networks. It groups edges into cliques and bicliques, and nodes into a neighborhood hierarchy. We demonstrate power graph analysis on five examples, and show its advantages over traditional network representations. Moreover, we evaluate the algorithm performance on a benchmark, test the robustness of the algorithm to noise, and measure its empirical time complexity at O (e1.71)- sub-quadratic in the number of edges e. Second, we tackle the difficult and controversial problem of data quality in protein interaction networks. We propose a novel measure for accuracy and completeness of genome-wide protein interaction networks based on network compressibility. We validate this new measure by i) verifying the detrimental effect of false positives and false negatives, ii) showing that gold standard networks are highly compressible, iii) showing that authors' choice of confidence thresholds is consistent with high network compressibility, iv) presenting evidence that compressibility is correlated with co-expression, co-localization and shared function, v) showing that complete and accurate networks of complex systems in other domains exhibit similar levels of compressibility than current high quality interactomes. Third, we apply power graph analysis to networks derived from text-mining as well to gene expression microarray data. In particular, we present i) the network-based analysis of genome-wide expression profiles of the neuroectodermal conversion of mesenchymal stem cells. ii) the analysis of regulatory modules in a rare mitochondrial cytopathy: emph{Mitochondrial Encephalomyopathy, Lactic acidosis, and Stroke-like episodes} (MELAS), and iii) we investigate the biochemical causes behind the enhanced biocompatibility of tantalum compared with titanium.

The astonishing progress of molecular biology, engineering and computer science has resulted in mature technologies capable of examining multiple cellular components at a genome-wide scale. Protein-protein interactions are one example of such growing data. These data are often organised as networks with proteins as nodes and interactions as edges. Albeit still incomplete, there is now a substantial amount of data available and there is a need for biologically meaningful methods to analyse and interpret these interactions. In this thesis we focus on how to compare protein interaction networks (PINs) and on the rela- tionship between network architecture and the biological characteristics of proteins. The underlying theme throughout the dissertation is the use of small subgraphs – small interaction patterns between 2-5 proteins. We start by examining two popular scores that are used to compare PINs and network models. When comparing networks of the same model type we find that the typical scores are highly unstable and depend on the number of nodes and edges in the networks. This is unsatisfactory and we propose a method based on non-parametric statistics to make more meaningful comparisons. We also employ principal component analysis to judge model fit according to subgraph counts. From these analyses we show that no current model fits to the PINs; this may well reflect our lack of knowledge on the evolution of protein interactions. Thus, we use explanatory variables such as protein age and protein structural class to find patterns in the interactions and subgraphs we observe. We discover that the yeast PIN is highly heterogeneous and therefore no single model is likely to fit the network. Instead, we focus on ego-networks containing an initial protein plus its interacting partners and their interaction partners. In the final chapter we propose a new, alignment-free method for network comparison based on such ego-networks. The method compares subgraph counts in neighbourhoods within PINs in an averaging, many-to-many fashion. It clusters networks of the same model type and is able to successfully reconstruct species phylogenies solely based on PIN data providing exciting new directions for future research.

Chorea Huntington (HD) ist eine neurodegenerative Erkrankung, die durch Ablagerungen von N-terminal Polyglutamin-reichen Huntingtin (Htt) -Fragmenten in den betroffenen Neuronen charakterisiert ist. Das mutierte Htt (mHtt) Protein wird ubiquitär exprimiert. Das zellspezifische Absterben von „medium-sized spiny neurons“ (MSN) wird jedoch im Striatum von HD Patienten verursacht (Albin, 1995). Es wird angenommen, dass Striatum-spezifische Proteine, die mit Htt interagieren, eine wichtige Rolle in der Pathogenese von HD spielen (Ross, 1995). Protein-Protein-Interaktionsstudien haben gezeigt, dass einige der Htt-Interaktionspartner mit unlöslichen Htt-Ablagerungen in den Gehirnen von HD-Patienten kolokalisieren und die Bildung von Protein-Aggregaten beeinflussen (Goehler, 2004). Kürzlich wurde durch die Integration von Genexpressions- und Interaktionsdaten ein Striatum-spezifisches Protein-Interaktionsnetzwerk erstellt (Chaurasia, unveröffentlichte Daten). Eines der identifizierten Proteine ist CRMP1 (collapsin response mediator protein 1), das spezifisch in Neuronen exprimiert wird und möglicherweise eine wichtige Rolle bei der Pathogenese von HD spielt. Experimentelle Untersuchungen mithilfe eines Filter-Retardationsassays zeigten, dass CRMP1 die Anordnung von Htt zu fibrillären, SDS-unlöslichen Aggregaten verringert. Durch Rasterkraftmikroskopie wurde der direkte Effekt von CRMP1 auf den Aggregationsprozess von Htt bestätigt. Ko-Immunopräzipitationsstudien zeigten, dass CRMP1 und Htt in Säugerzellen unter physiologischen Bedingungen miteinander interagieren. Es wurde nachgewiesen, dass CRMP1 die Polyglutamin-abhängige Aggregation und Toxizität von Htt in Zell- und Drosophila-Modellen von HD moduliert. Außerdem konnte CRMP1 in neuronalen Ablagerungen in R6/2 Mäusegehirnen und dessen selektive Spaltung durch Calpaine gezeigt werden. Diese Ergebnisse deuten darauf hin, dass die Lokalisation und Funktion von CRMP1 bei der Krankheitsentstehung verändert werden. / Huntington’s disease (HD) is a neurodegenerative disorder characterized by the accumulation of N-terminal polyglutamine (polyQ)-containing huntingtin (Htt) fragments in affected neurons. The mutant Htt (mHtt) protein is ubiquitously expressed but causes specific dysfunction and death of striatal medium-sized spiny neurons (MSNs) (Albin, 1995). It is assumed that striatum specific proteins interacting with Htt might play an important role in HD pathogenesis (Ross, 1995). Previous protein-protein interaction (PPI) studies demonstrated that many Htt-interacting proteins colocalize with insoluble Htt inclusions in HD brains and modulate the mHtt phenotype (Goehler 2004). A striatum-specific, dysregulated PPI network has been created recently by integrating PPI networks with information from gene expression profiling data (Chaurasia, unpublished data). One of the identified dysregulated proteins potentially involved in HD pathogenesis was the neuron-specific collapsin response-mediator protein 1 (CRMP1). Here, I show that CRMP1 reduces the self-assembly of SDS-insoluble mHtt protein aggregates in vitro, indicating a direct role of CRMP1 on the mHtt aggregation process. Coimmunoprecipitation studies showed that CRMP1 and Htt associate in mammalian cells under physiological conditions. In addition, CRMP1 localizes to abnormal neuronal inclusions and efficiently modulates polyQ-mediated Htt aggregation and toxicity in cell and Drosophila models of HD. This suggests that dysfunction of the protein is crucial for disease pathogenesis. Finally, I observed that CRMP1 localizes to neuronal inclusions and is selectively cleaved by calpains in R6/2 mouse brains, indicating that its distribution and function are altered in pathogenesis. In conclusion, this study presents new findings on the function of CRMP1 and its role in the pathogenesis of HD. The protein interacts with Htt and modulates its aggregation and toxicity, in this way influencing the molecular course of the disease.

La importancia de comprender los procesos biológicos ha estimulado el desarrollo de métodos para la detección de interacciones proteína-proteína. Esta tesis presenta PIANA (Protein Interactions And Network Analysis), un programa informático para la integración y el análisis de redes de interacción proteicas. Además, describimos un método que identifica motivos de interacción basándose en que las proteínas con parejas de interacción comunes tienden a interaccionar con esas parejas a través del mismo motivo de interacción. Encontramos que las proteínas altamente conectadas (i.e., hubs) con múltiples motivos tienen mayor probabilidad de ser esenciales para la viabilidad de la célula que los hubs con uno o dos motivos. Finalmente, presentamos un método que predice genes relacionados con cáncer mediante la integración de redes de interacción proteicas, datos de expresión diferenciada y propiedades estructurales, funcionales y evolutivas. El valor de predicción positiva es 71% con sensitividad del 1%, superando a otros métodos usados independientemente. / The importance of understanding cellular processes prompted the development of experimental approaches that detect protein-protein interactions. Here, we describe a software platform called PIANA (Protein Interactions And Network Analysis) that integrates interaction data from multiple sources and automates the analysis of protein interaction networks. Moreover, we describe a method that delineates interacting motifs by relying on the observation that proteins with common interaction partners tend to interact with these partners through the same interacting motif. We find that highly connected proteins (i.e., hubs) with multiple interacting motifs are more likely to be essential for cellular viability than hubs with one or two interacting motifs. Furthermore, we present a method that predicts cancer genes by integrating protein interaction networks, differential expression studies and structural, functional and evolutionary properties. For a sensitivity of 1%, the positive predictive value is 71%, which outperforms the use of any of the methods independently.

Proteins are indispensable players in virtually all biological events. The functions of proteins are determined by their three dimensional (3D) structure and coordinated through intricate networks of protein-protein interactions (PPIs). Hence, a deep comprehension of such networks turns out to be crucial for understanding the cellular biology. Computational approaches have become critical tools for analysing PPI networks. In silico methods take advantage of the existing PPI knowledge to both predict new interactions and predict the function of proteins. Regarding the task of predicting PPIs, several methods have been already developed. However, recent findings demonstrate that such methods could take advantage of the knowledge on non-interacting protein pairs (NIPs). On the task of predicting the function of proteins,the Guilt-by-Association (GBA) principle can be exploited to extend the functional annotation of proteins over PPI networks. In this thesis, a new algorithm for PPI prediction and a protocol to complete cell signalling networks are presented. iLoops is a method that uses NIP data and structural information of proteins to predict the binding fate of protein pairs. A novel protocol for completing signalling networks –a task related to predicting the function of a protein, has also been developed. The protocol is based on the application of GBA principle in PPI networks. / Les proteïnes tenen un paper indispensable en virtualment qualsevol procés biològic. Les funcions de les proteïnes estan determinades per la seva estructura tridimensional (3D) i són coordinades per mitjà d’una complexa xarxa d’interaccions protiques (en anglès, protein-protein interactions, PPIs). Axí doncs, una comprensió en profunditat d’aquestes xarxes és fonamental per entendre la biologia cel•lular. Per a l’anàlisi de les xarxes d’interacció de proteïnes, l’ús de tècniques computacionals ha esdevingut fonamental als darrers temps. Els mètodes in silico aprofiten el coneixement actual sobre les interaccions proteiques per fer prediccions de noves interaccions o de les funcions de les proteïnes. Actualment existeixen diferents mètodes per a la predicció de noves interaccions de proteines. De tota manera, resultats recents demostren que aquests mètodes poden beneficiar-se del coneixement sobre parelles de proteïnes no interaccionants (en anglès, non-interacting pairs, NIPs). Per a la tasca de predir la funció de les proteïnes, el principi de “culpable per associació” (en anglès, guilt by association, GBA) és usat per extendre l’anotació de proteïnes de funció coneguda a través de xarxes d’interacció de proteïnes. En aquesta tesi es presenta un nou mètode pre a la predicció d’interaccions proteiques i un nou protocol basat per a completar xarxes de senyalització cel•lular. iLoops és un mètode que utilitza dades de parells no interaccionants i coneixement de l’estructura 3D de les proteïnes per a predir interaccions de proteïnes. També s’ha desenvolupat un nou protocol per a completar xarxes de senyalització cel•lular, una tasca relacionada amb la predicció de les funcions de les proteïnes. Aquest protocol es basa en aplicar el principi GBA a xarxes d’interaccions proteiques.