Geometric, Feature-based and Graph-based Approaches for the Structural Analysis of Protein Binding Sites : Novel Methods and Computational Analysis

In this thesis, protein binding sites are considered. To enable the extraction of information from the space of protein binding sites, these binding sites must be mapped onto a mathematical space. This can be done by mapping binding sites onto vectors, graphs or point clouds. To finally enable a structure on the mathematical space, a distance measure is required, which is introduced in this thesis. This distance measure eventually can be used to extract information by means of data mining techniques

Computational Techniques for the Structural and Dynamic Analysis of Biological Networks

The analysis of biological systems involves the study of networks from different omics such as genomics, transcriptomics, metabolomics and proteomics. In general, the computational techniques used in the analysis of biological networks can be divided into those that perform (i) structural analysis, (ii) dynamic analysis of structural prop- erties and (iii) dynamic simulation. Structural analysis is related to the study of the topology or stoichiometry of the biological network such as important nodes of the net- work, network motifs and the analysis of the flux distribution within the network. Dy- namic analysis of structural properties, generally, takes advantage from the availability of interaction and expression datasets in order to analyze the structural properties of a biological network in different conditions or time points. Dynamic simulation is useful to study those changes of the biological system in time that cannot be derived from a structural analysis because it is required to have additional information on the dynamics of the system. This thesis addresses each of these topics proposing three computational techniques useful to study different types of biological networks in which the structural and dynamic analysis is crucial to answer to specific biological questions. In particu- lar, the thesis proposes computational techniques for the analysis of the network motifs of a biological network through the design of heuristics useful to efficiently solve the subgraph isomorphism problem, the construction of a new analysis workflow able to integrate interaction and expression datasets to extract information about the chromo- somal connectivity of miRNA-mRNA interaction networks and, finally, the design of a methodology that applies techniques coming from the Electronic Design Automation (EDA) field that allows the dynamic simulation of biochemical interaction networks and the parameter estimation

Migration from Legacy to Reactive Applications in OutSystems

A legacy system is an information system that significantly resists evolution. Through a migration, these systems can be moved to a more modernized environment without having to be redeveloped. OutSystems is a software company with a platform to develop and maintain applications using abstraction to increase productivity. In October 2019, OutSystems launched a new paradigm to allow developers to build reactive web applications. Because of this, the applications implemented in the old web paradigm turned into legacy systems. The OutSystems’ approach to this problem was a manual migration. However, it discards a considerable part of the effort previously made on the legacy system. A well-founded case study took place and allowed us to classify the UI as the most prioritized feature, but coincidently, the major bottleneck in migrations. So, this project had the following objectives: (1) The design and implementation of an automatic migration approach capable of converting UI elements to accelerate the manual migration; (2) The integration of the developed tool in the OutSystems platform. To transform the OutSystems paradigm’s elements, model-driven transformation rules must be set to receive the source UI elements and produce the target equivalent implementation in the new paradigm (each according to their model). However, the trans formations may not be straightforward, and a set of elements may need to be migrated to a different implementation due to Reactive Web’s best practices. Via the creation and search of UI patterns, it is possible to make special transformations for such scenarios. As a result, a migration approach was developed, allowing for the migration of UI (and other) elements. To complement this objective, the developed tool was integrated into the OutSystems platform with an easy to use interaction. Performance and usability tests proved the necessity and impact the final result had on the migration problem. This dissertation’s objectives were fully met and even exceeded, accelerating the man ual migration by providing an automatic UI conversion. This provided a quality increase in the existing process and results, giving OutSystems and its users the possibility of evolving their applications with considerable less effort and investment.Um sistema legado é um sistema de informação que resiste à evolução. Através de uma migração, estes sistemas podem ser movidos para um ambiente modernizado sem necessitar de re-implementação. A OutSystems é uma empresa de software com uma plataforma para desenvolver e manter aplicações usando abstracção para aumentar a produtividade. Em Outubro de 2019, a OutSystems lançou um novo paradigma para desenvolver aplicações reactive web. Assim, as aplicações implementadas no antigo paradigma web tornaram-se sistemas legados. A abordagem da OutSystems ao problema foi uma migração manual, no entanto, esta abordagem desconsidera uma parte significativa do investimento feito no sistema legado. Uma análise permitiu classificar a UI como a característica mais priorizada, mas também como o maior obstáculo em migrações. Assim, este projecto tem como objectivos: (1) O desenho e implementação de uma migração automática capaz de converter os elementos de UI para acelerar a migração manual; (2) A integração da ferramenta desenvolvida na plataforma da OutSystems. Para transformar os elementos dos paradigmas OutSystems, transformações de modelos têm de ser definidas para receber os elementos UI e produzir a implementação equivalente no novo paradigma (de acordo com o seu modelo). No entanto, as transformações podem não ser lineares, e um conjunto de elementos pode necessitar de uma migração para uma implementação diferente devido ao Reactive Web. Com a definição e procura de padrões de UI, é possível fazer transformações especiais para esses cenários. Como resultado, a migração foi desenvolvida, permitindo a conversão de elementos de UI (e não só). Para complementar, a ferramenta desenvolvida foi integrada na plataforma da OutSystems com uma interacção de fácil uso. Testes de desempenho e usabilidade provaram a necessidade e impacto da ferramenta no contexto da migração manual. Os objectivos desta dissertação foram completados na totalidade, acelerando a migração manual com a automação da migração de UI. Isto traz um aumento da qualidade no processo existente e nos seus resultados, dando à OutSystems e aos seus utilizadores a possibilidade de evoluírem as suas aplicações com um esforço e investimento menores

Contributions to security and privacy protection in recommendation systems

A recommender system is an automatic system that, given a customer model and a set of available documents, is able to select and offer those documents that are more interesting to the customer. From the point of view of security, there are two main issues that recommender systems must face: protection of the users' privacy and protection of other participants of the recommendation process. Recommenders issue personalized recommendations taking into account not only the profile of the documents, but also the private information that customers send to the recommender. Hence, the users' profiles include personal and highly sensitive information, such as their likes and dislikes. In order to have a really useful recommender system and improve its efficiency, we believe that users shouldn't be afraid of stating their preferences. The second challenge from the point of view of security involves the protection against a new kind of attack. Copyright holders have shifted their targets to attack the document providers and any other participant that aids in the process of distributing documents, even unknowingly. In addition, new legislation trends such as ACTA or the ¿Sinde-Wert law¿ in Spain show the interest of states all over the world to control and prosecute these intermediate nodes. we proposed the next contributions: 1.A social model that captures user's interests into the users' profiles, and a metric function that calculates the similarity between users, queries and documents. This model represents profiles as vectors of a social space. Document profiles are created by means of the inspection of the contents of the document. Then, user profiles are calculated as an aggregation of the profiles of the documents that the user owns. Finally, queries are a constrained view of a user profile. This way, all profiles are contained in the same social space, and the similarity metric can be used on any pair of them. 2.Two mechanisms to protect the personal information that the user profiles contain. The first mechanism takes advantage of the Johnson-Lindestrauss and Undecomposability of random matrices theorems to project profiles into social spaces of less dimensions. Even if the information about the user is reduced in the projected social space, under certain circumstances the distances between the original profiles are maintained. The second approach uses a zero-knowledge protocol to answer the question of whether or not two profiles are affine without leaking any information in case of that they are not. 3.A distributed system on a cloud that protects merchants, customers and indexers against legal attacks, by means of providing plausible deniability and oblivious routing to all the participants of the system. We use the term DocCloud to refer to this system. DocCloud organizes databases in a tree-shape structure over a cloud system and provide a Private Information Retrieval protocol to avoid that any participant or observer of the process can identify the recommender. This way, customers, intermediate nodes and even databases are not aware of the specific database that answered the query. 4.A social, P2P network where users link together according to their similarity, and provide recommendations to other users in their neighborhood. We defined an epidemic protocol were links are established based on the neighbors similarity, clustering and randomness. Additionally, we proposed some mechanisms such as the use SoftDHT to aid in the identification of affine users, and speed up the process of creation of clusters of similar users. 5.A document distribution system that provides the recommended documents at the end of the process. In our view of a recommender system, the recommendation is a complete process that ends when the customer receives the recommended document. We proposed SCFS, a distributed and secure filesystem where merchants, documents and users are protectedEste documento explora c omo localizar documentos interesantes para el usuario en grandes redes distribuidas mediante el uso de sistemas de recomendaci on. Se de fine un sistema de recomendaci on como un sistema autom atico que, dado un modelo de cliente y un conjunto de documentos disponibles, es capaz de seleccionar y ofrecer los documentos que son m as interesantes para el cliente. Las caracter sticas deseables de un sistema de recomendaci on son: (i) ser r apido, (ii) distribuido y (iii) seguro. Un sistema de recomendaci on r apido mejora la experiencia de compra del cliente, ya que una recomendaci on no es util si es que llega demasiado tarde. Un sistema de recomendaci on distribuido evita la creaci on de bases de datos centralizadas con informaci on sensible y mejora la disponibilidad de los documentos. Por ultimo, un sistema de recomendaci on seguro protege a todos los participantes del sistema: usuarios, proveedores de contenido, recomendadores y nodos intermedios. Desde el punto de vista de la seguridad, existen dos problemas principales a los que se deben enfrentar los sistemas de recomendaci on: (i) la protecci on de la intimidad de los usuarios y (ii) la protecci on de los dem as participantes del proceso de recomendaci on. Los recomendadores son capaces de emitir recomendaciones personalizadas teniendo en cuenta no s olo el per l de los documentos, sino tambi en a la informaci on privada que los clientes env an al recomendador. Por tanto, los per les de usuario incluyen informaci on personal y altamente sensible, como sus gustos y fobias. Con el n de desarrollar un sistema de recomendaci on util y mejorar su e cacia, creemos que los usuarios no deben tener miedo a la hora de expresar sus preferencias. Para ello, la informaci on personal que est a incluida en los per les de usuario debe ser protegida y la privacidad del usuario garantizada. El segundo desafi o desde el punto de vista de la seguridad implica un nuevo tipo de ataque. Dado que la prevenci on de la distribuci on ilegal de documentos con derechos de autor por medio de soluciones t ecnicas no ha sido efi caz, los titulares de derechos de autor cambiaron sus objetivos para atacar a los proveedores de documentos y cualquier otro participante que ayude en el proceso de distribuci on de documentos. Adem as, tratados y leyes como ACTA, la ley SOPA de EEUU o la ley "Sinde-Wert" en España ponen de manfi esto el inter es de los estados de todo el mundo para controlar y procesar a estos nodos intermedios. Los juicios recientes como MegaUpload, PirateBay o el caso contra el Sr. Pablo Soto en España muestran que estas amenazas son una realidad

Quality of Service-aware matchmaking for adaptive microservice-based applications

Applications that make use of Internet of Things (IoT) can capture an enormous amount of raw data from sensors and actuators, which is frequently transmitted to cloud data centers for processing and analysis. However, due to varying and unpredictable data generation rates and network latency, this can lead to a performance bottleneck for data processing. With the emergence of fog and edge computing hosted microservices, data processing could be moved towards the network edge. We propose a new method for continuous deployment and adaptation of multi-tier applications along edge, fog, and cloud tiers by considering resource properties and non-functional requirements (e.g., operational cost, response time and latency etc.). The proposed approach supports matchmaking of application and Cloud-To-Things infrastructure based on a subgraph pattern matching (P-Match) technique. Results show that the proposed approach improves resource utilization and overall application Quality of Service. The approach can also be integrated into software engineering workbenches for the creation and deployment of cloud-native applications, enabling partitioning of an application across the multiple infrastructure tiers outlined above

Network modularity and local environment similarity as descriptors of protein structure

As the number of solved protein structures increases, the opportunities for meta-analysis of this dataset increase too. Here we explore two approaches for analysing protein structure, both starting from the three-dimensional co-ordinates of each atom within the structure, which are then abstracted into a more useful form. The first method transforms the protein into a network in which its amino acids are the nodes, and where the edges are generated using a simple proximity test. By applying the Infomap community detection algorithm, we can fragment the protein into highly intra-connected subregions - these subregions are compact and globular, and can be compared with known structural and functional subunits of the protein (also known as domains). By performing this fragmentation process systematically across a large set of proteins, and checking for structurally conserved fragments, we can search for novel candidate domains. This method for automatically decomposing a protein into compact substructures may also be useful in coarse-graining molecular dynamics, analysing the protein’s topology, in de novo protein design, or in fitting electron density maps derived from single particle electron microscopy. The second method calculates a descriptor for each atom of the protein based on its local environment, known as a Smooth Overlap of Atomic Positions (SOAP) descriptor. Using these descriptors we can perform overall comparisons of the subregions identified above. In addition, by comparing the descriptors of a set of proteins known to share common structural or functional features (such as binding of a particular ligand), we can automatically identify the most highly conserved atoms of the set. These atoms may line ligand binding pockets or correspond to allosteric sites, which could inform drug design

Topological Deep Learning: Going Beyond Graph Data

Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes, cell complexes, and hypergraphs, which generalize many domains encountered in scientific computations. In this paper, we present a unifying deep learning framework built upon a richer data structure that includes widely adopted topological domains. Specifically, we first introduce combinatorial complexes, a novel type of topological domain. Combinatorial complexes can be seen as generalizations of graphs that maintain certain desirable properties. Similar to hypergraphs, combinatorial complexes impose no constraints on the set of relations. In addition, combinatorial complexes permit the construction of hierarchical higher-order relations, analogous to those found in simplicial and cell complexes. Thus, combinatorial complexes generalize and combine useful traits of both hypergraphs and cell complexes, which have emerged as two promising abstractions that facilitate the generalization of graph neural networks to topological spaces. Second, building upon combinatorial complexes and their rich combinatorial and algebraic structure, we develop a general class of message-passing combinatorial complex neural networks (CCNNs), focusing primarily on attention-based CCNNs. We characterize permutation and orientation equivariances of CCNNs, and discuss pooling and unpooling operations within CCNNs in detail. Third, we evaluate the performance of CCNNs on tasks related to mesh shape analysis and graph learning. Our experiments demonstrate that CCNNs have competitive performance as compared to state-of-the-art deep learning models specifically tailored to the same tasks. Our findings demonstrate the advantages of incorporating higher-order relations into deep learning models in different applications

Improving Model-Based Software Synthesis: A Focus on Mathematical Structures

Computer hardware keeps increasing in complexity. Software design needs to keep up with this. The right models and abstractions empower developers to leverage the novelties of modern hardware. This thesis deals primarily with Models of Computation, as a basis for software design, in a family of methods called software synthesis. We focus on Kahn Process Networks and dataflow applications as abstractions, both for programming and for deriving an efficient execution on heterogeneous multicores. The latter we accomplish by exploring the design space of possible mappings of computation and data to hardware resources. Mapping algorithms are not at the center of this thesis, however. Instead, we examine the mathematical structure of the mapping space, leveraging its inherent symmetries or geometric properties to improve mapping methods in general. This thesis thoroughly explores the process of model-based design, aiming to go beyond the more established software synthesis on dataflow applications. We starting with the problem of assessing these methods through benchmarking, and go on to formally examine the general goals of benchmarks. In this context, we also consider the role modern machine learning methods play in benchmarking. We explore different established semantics, stretching the limits of Kahn Process Networks. We also discuss novel models, like Reactors, which are designed to be a deterministic, adaptive model with time as a first-class citizen. By investigating abstractions and transformations in the Ohua language for implicit dataflow programming, we also focus on programmability. The focus of the thesis is in the models and methods, but we evaluate them in diverse use-cases, generally centered around Cyber-Physical Systems. These include the 5G telecommunication standard, automotive and signal processing domains. We even go beyond embedded systems and discuss use-cases in GPU programming and microservice-based architectures