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.

As the focus of over 70 years* of archaeological research, Oaxaca, Mexico, is one of Mesoamerica*s best understood regions. Yet, despite the volume of work in Oaxaca, information about one of its key resource areas, the central Pacific coast, remains limited. Specifically, the ambiguous role of Oaxaca*s Central Coast in interregional relationships during pre-Hispanic times to the sites of Monte Alban and Tututepec has been a chronic problem and major source of debate for decades. The purpose of this thesis is to begin clarifying the role of Oaxaca*s Central Coast in interregional networks and its pre-Hispanic history. Analysis utilized surface observations, surface collections, and information from limited excavations performed by the Proyecto Arqueologico Laguna de Manialtepec (PALM) in the Manialtepec Basin, located on the Central Coast of Oaxaca. The data was then mapped using ArcGIS software to render settlement and artifact patterns. Based on the results of this project I suggest a history of settlement for this area. I also argue that the Basin contained three centers, maintained interregional interactions, and was invaded by the Mixtecs of highland Oaxaca during the Late Postclassic Period (A.D. 1200-1500).

Patients not responding to therapy as expected is one of the most pressing healthcare concerns of today. It causes economical, medical and societal issues along with suffering for patients. This project aimed to address this problem and evaluate how to find the best suited drug treatments for individual patients to treat Covid-19. This project was carried out in collaboration with the company AB Mavatar, that have two networks, one experimental and one predicted, which produce drug treatment rankings differently. Different methods are used to connect drug targets to disease associated genes and thus evaluate what drugs are best suited for specific patients to treat Covid-19. The aim of this project is to examine how network, method and drug category affect the ranking of a drug treatment for four mapped Covid-19 patients. Which drug category a drug belongs to did not seem to significantly affect the drug ranking. Yet, certain drug subcategories were closely correlated. However, these subcategories were not those that are typically associated with Covid-19. The method used to connect drug targets to disease associated genes heavily impacts the ranking of the drug treatment. The methods should be further evaluated to see if some should be excluded or weighted less in drug ranking calculations. The two networks are similar in how they rank different drugs, especially in severely ill patients. Through this project and the evaluation of the impact of method choice, one can start to figure out what should be prioritized among disease related changes. Also, important parameters for personalized treatment can be evaluated. / Patienter som inte svarar på terapi som förväntat är en av de största utmaningarna inom hälso- och sjukvård idag. Det orsakar ekonomiska, medicinska och samhälleliga problem samt lidande för patienter. Det här projektet adresserade detta problem och evaluerade hur man kan hitta det bäst lämpade läkemedlet för specifika patienter för att behandla Covid-19. Projektet gjordes tillsammans med företaget AB Mavatar, som har två interaktom, en experimentell och en datadriven, som rangordnar läkemedelsrekommendationer på olika sätt. Olika metoder används för att koppla samman läkemedelsmål med sjukdomsrelaterade gener och således evaluera vilka läkemedel som är bäst lämpade för specifika patienter för behandling av Covid-19. Syftet med projektet var att undersöka hur nätverk, metod och läkemedelskategori påverkar hur läkemedel rangordnas för fyra kartlagda Covid-19-patienter.  Vilken läkemedelskategori ett läkemedel tillhör tycks inte märkbart påverka läkemedelsrangordning. Trots detta var vissa läkemedelsunderkategorier nära korrelerade. Dock var dessa underkategorier inte typiskt associerade med Covid-19. Metoden för att koppla samman läkemedelsmål med sjukdomsassocierade gener påverkade läkemedelsrangordningen väsentligt. Metoderna borde dock evalueras ytterligare för att eventuellt exkludera eller vikta vissa mindre i uträkningar av läkemedelsrang. De två nätverken är lika i hur de rangordnar olika läkemedel, särskilt för svårt sjuka patienter. Genom detta projekt och genom evaluering av metodvalets påverkan kan man börja begripa hur man borde priorita bland sjukdomsrelaterade förändringar. Dessutom kunde viktiga parametrar inom personlig behandling evalueras.

Indiana University-Purdue University Indianapolis (IUPUI) / Intrinsically disordered proteins are proteins characterized by lack of stable tertiary structures under physiological conditions. Evidence shows that disordered proteins are not only highly involved in protein interactions, but also have the capability to associate with more than one partner. Short disordered protein fragments, called “molecular recognition features” (MoRFs), were hypothesized to facilitate the binding diversity of highly-connected proteins termed “hubs”. MoRFs often couple folding with binding while forming interaction complexes. Two protein disorder mechanisms were proposed to facilitate multiple partner binding and enable hub proteins to bind to multiple partners: 1. One region of disorder could bind to many different partners (one-to-many binding), so the hub protein itself uses disorder for multiple partner binding; and 2. Many different regions of disorder could bind to a single partner (many-to-one binding), so the hub protein is structured but binds to many disordered partners via interaction with disorder. Thousands of MoRF-partner protein complexes were collected from Protein Data Bank in this study, including 321 one-to-many binding examples and 514 many-to-one binding examples. The conformational flexibility of MoRFs was observed at atomic resolution to help the MoRFs to adapt themselves to various binding surfaces of partners or to enable different MoRFs with non-identical sequences to associate with one specific binding pocket. Strikingly, in one-to-many binding, post-translational modification, alternative splicing and partner topology were revealed to play key roles for partner selection of these fuzzy complexes. On the other hand, three distinct binding profiles were identified in the collected many-to-one dataset: similar, intersecting and independent. For the similar binding profile, the distinct MoRFs interact with almost identical binding sites on the same partner. The MoRFs can also interact with a partially the same but partially different binding site, giving the intersecting binding profile. Finally, the MoRFs can interact with completely different binding sites, thus giving the independent binding profile. In conclusion, we suggest that protein disorder with post-translational modifications and alternative splicing are all working together to rewire the protein interaction networks.