Desempenho de algoritmos de ordenação em hardware e software implementados em SOC

Mestrado em Engenharia de Computadores e TelemáticaField Programmable Gate Arrays (FPGAs) were invented by Xilinx in 1985. Their reconfigurable nature allows to use them in multiple areas of Information Technologies. This project aims to study this technology to be an alternative to traditional data processing methods, namely sorting. The proposed solution is based on the principle of reusing resources to counter this technology’s known resources limitations.As Field Programmable Gate Arrays (FPGAs) foram inventadas em 1985 pela Xilinx. A sua natureza reconfiguratória permite que sejam utilizadas em várias áreas das tecnologias de informação. Este trabalho tem como objectivo estudar o uso desta tecnologia como alternativa aos métodos tradicionais de processamento de dados, nomeadamente a ordenação. A solução proposta baseia-se na reutilização de recursos para combater as conhecidas limitações deste tipo de tecnologia

To Index or Not to Index: Optimizing Exact Maximum Inner Product Search

Exact Maximum Inner Product Search (MIPS) is an important task that is widely pertinent to recommender systems and high-dimensional similarity search. The brute-force approach to solving exact MIPS is computationally expensive, thus spurring recent development of novel indexes and pruning techniques for this task. In this paper, we show that a hardware-efficient brute-force approach, blocked matrix multiply (BMM), can outperform the state-of-the-art MIPS solvers by over an order of magnitude, for some -- but not all -- inputs. In this paper, we also present a novel MIPS solution, MAXIMUS, that takes advantage of hardware efficiency and pruning of the search space. Like BMM, MAXIMUS is faster than other solvers by up to an order of magnitude, but again only for some inputs. Since no single solution offers the best runtime performance for all inputs, we introduce a new data-dependent optimizer, OPTIMUS, that selects online with minimal overhead the best MIPS solver for a given input. Together, OPTIMUS and MAXIMUS outperform state-of-the-art MIPS solvers by 3.2$\times$ on average, and up to 10.9$\times$, on widely studied MIPS datasets.Comment: 12 pages, 8 figures, 2 table

Combining evolutionary algorithms with reaction rules towards focused molecular design

Designing novel small molecules with desirable properties and feasible synthesis continues to pose a significant challenge in drug discovery, particularly in the realm of natural products. Reaction-based gradient-free methods are promising approaches for designing new molecules as they ensure synthetic feasibility and provide potential synthesis paths. However, it is important to note that the novelty and diversity of the generated molecules highly depend on the availability of comprehensive reaction templates. To address this challenge, we introduce ReactEA, a new open-source evolutionary framework for computer-aided drug discovery that solely utilizes biochemical reaction rules. ReactEA optimizes molecular properties using a comprehensive set of 22,949 reaction rules, ensuring chemical validity and synthetic feasibility. ReactEA is versatile, as it can virtually optimize any objective function and track potential synthetic routes during the optimization process. To demonstrate its effectiveness, we apply ReactEA to various case studies, including the design of novel drug-like molecules and the optimization of pre-existing ligands. The results show that ReactEA consistently generates novel molecules with improved properties and reasonable synthetic routes, even for complex tasks such as improving binding affinity against the PARP1 enzyme when compared to existing inhibitors.Centre of Biological Engineering (CEB, University of Minho) for financial and equipment support. Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit and through a Ph.D. scholarship awarded to João Correia (SFRH/BD/144314/2019). European Commission through the project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408).info:eu-repo/semantics/publishedVersio

Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design

In recent years, deep learning has demonstrated promising results in de novo drug design. However, the proposed techniques still lack an efficient exploration of the large chemical space. Most of these methods explore a small fragment of the chemical space of known drugs, if the desired molecules were not found, the process ends. In this work, we introduce a curiosity-driven method to force the model to navigate many parts of the chemical space, therefore, achieving higher desirability and diversity as well. At first, we train a recurrent neural network-based general molecular generator (G), then we fine-tune G to maximize curiosity and desirability. We define curiosity as the Tanimoto similarity between two generated molecules, a first molecule generated by G, and a second one generated by a copy of G (Gcopy). We only backpropagate the loss through G while keeping Gcopy unchanged. We benchmarked our approach against two desirable chemical properties related to drug-likeness and showed that the discovered chemical space can be significantly expanded, thus, discovering a higher number of desirable molecules with more diversity and potentially easier to synthesize. All Code and data used in this paper are available at https://github.com/amine179/Curiosity-RL-for-Drug-Design

Mapping interactions between metabolites and transcriptional regulators at a genome-scale

The control and regulation of cellular metabolism is required to maintain the biosynthesis of building blocks and energy, but also to prevent the loss of energy and to be able to quickly adjust to changing conditions. Hence, the metabolic network and the flow of genetic information has multiple layers of regulation and information is transmitted between gene expression and metabolism. For this purpose, metabolites serve as key signals of the regulatory network to balance metabolism via the adjustment of protein levels and the activity of enzymes. Understanding these regulations and interplays of bacterial metabolism will enable us to improve the modelling and engineering of metabolic networks and ultimately to develop new antibiotics and production strains. The aim of this thesis is to investigate which regulatory mechanisms are used by the cell to respond to genetic perturbations. Moreover, we develop new methods to map protein-metabolite interactions and to prove their functionality in the cell. After introducing the fundamentals of metabolic network regulation, we investigate in chapter 1 how Escherichia coli (E. coli) reacts to genetic perturbations. We use a library of 7177 CRISPRi strains to perform a pooled fitness growth assay, demonstrating the buffering effects of metabolism. Additionally, measuring the metabolome and proteome of 30 arrayed CRISPRi strains enables us to elucidate three gene-specific buffering mechanisms. In chapter 2, we use our new insights about genetic perturbations of chapter 1 to develop a method for systematically mapping interactions between metabolites and transcriptional regulators. CRISPRi leads to a knockdown of a gene and therefore induces specific changes in the metabolome and proteome of the cell. We therefore combine the pooled CRISPRi library with a fluorescent reporter for transcription factor activity and extract cells, which show a response of the reporter to the changing conditions, via FACS from the pooled library. By analyzing proteome and metabolome data, we confirm previously reported and discover new interactions. With chapter 3, we provide a detailed protocol of how to work with CRISPRi libraries. We explain the design and construction of sgRNAs of arrayed as well as pooled CRISPRi strains and how to perform growth assays. Furthermore, we explain the execution and analysis of Illumina Next-generation sequencing of pooled libraries. We also explain the sorting of cells from pooled libraries via FACS. In chapter 4, we show how to find new interactions between metabolites and transcription factors by external perturbations. By switching a growing E. coli culture between growth and glucose limitation, we provoke strong changes of metabolite levels and transcript levels. Calculating the transcription factor activity from gene expression levels and correlating them with metabolite levels, enables us to recover known interactions but also to discover new interactions, of which we prove five in in vitro binding assays. In chapter 5, we investigate the function of allosteric regulation of metabolic enzymes in amino acid pathways of E. coli. We constructed 7 mutants of allosteric enzymes to remove the allosteric feedback regulation. By metabolomics, proteomics and flux profiling analysis we show how allostery helps to adjust enzyme levels of the cell. Furthermore, using a metabolic model and the application of CRISPRi we show how well-adjusted enzyme levels make the cell more stable towards genetic perturbations

Combining Metabolic Engineering and Synthetic Biology Approaches for the Production of Abscisic Acid in Yeast

Nature presents us with a myriad of complex and diverse molecules. Many of these molecules prove to be useful to humans and find applications as pharmaceuticals, biofuels, agrochemicals, cosmetic ingredients or food additives. One highly promising natural product with a broad range of potential applications is the terpenoid abscisic acid (ABA). ABA fulfils a pivotal role in higher plants by regulating various developmental processes as well as abiotic stress responses. However, ABA is also produced in many other organisms, including humans. It appears to be a ubiquitous and evolutionary conserved signalling molecule throughout nature. Genetically engineered microorganisms, referred to as microbial cell factories, can be a sustainable source of natural products. In this thesis, a cell factory for the heterologous production of ABA was established and optimized employing the yeast Saccharomyces cerevisiae. Cell factory development is an inherently time-consuming process. As an enabling technology for subsequent work on the ABA cell factory, we expanded the modular cloning toolkit for yeast and made it more applicable for common genetic engineering tasks (Paper I). The ABA biosynthetic pathway of Botrytis cinerea was used to construct an ABA-producing S. cerevisiae strain (Paper II). The activity of two B. cinerea proteins, BcABA1 and BcABA2, was found to limit ABA titers. Two optimization approaches were devised for the following studies. Firstly, various rational engineering targets were explored, of which the native yeast gene PAH1 was identified as the most promising candidate (Paper III). Knockdown of PAH1 benefited ABA production without affecting growth. Secondly, platform strains for screening BcABA1 and BcABA2 enzyme libraries were developed, which utilize an ABA biosensor and enable a high throughput screening approach (Paper IV). In this work, we combined metabolic engineering and synthetic biology approaches for the heterologous production of ABA, and furthermore provided tools and insights that will be useful beyond the scope of this project

Neutral Networks of Real-World Programs and their Application to Automated Software Evolution

The existing software development ecosystem is the product of evolutionary forces, and consequently real-world software is amenable to improvement through automated evolutionary techniques. This dissertation presents empirical evidence that software is inherently robust to small randomized program transformations, or \u27mutations. Simple and general mutation operations are demonstrated that can be applied to software source code, compiled assembler code, or directly to binary executables. These mutations often generate variants of working programs that differ significantly from the original, yet remain fully functional. Applying successive mutations to the same software program uncovers large \u27neutral networks\u27 of fully functional variants of real-world software projects. These properties of \u27mutational robustness\u27 and the corresponding \u27neutral networks\u27 have been studied extensively in biology and are believed to be related to the capacity for unsupervised evolution and adaptation. As in biological systems, mutational robustness and neutral networks in software systems enable automated evolution. The dissertation presents several applications that leverage software neutral networks to automate common software development and maintenance tasks. Neutral networks are explored to generate diverse implementations of software for improving runtime security and for proactively repairing latent bugs. Next, a technique is introduced for automatically repairing bugs in the assembler and executables compiled from off-the-shelf software. As demonstration, a proprietary executable is manipulated to patch security vulnerabilities without access to source code or any aid from the software vendor. Finally, software neutral networks are leveraged to optimize complex nonfunctional runtime properties. This optimization technique is used to reduce the energy consumption of the popular PARSEC benchmark applications by 20% as compared to the best available public domain compiler optimizations. The applications presented herein apply evolutionary computation techniques to existing software using common software engineering tools. By enabling evolutionary techniques within the existing software development toolchain, this work is more likely to be of practical benefit to the developers and maintainers of real-world software systems

An algorithmic framework for synthetic cost-aware decision making in molecular design

Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing, and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. We propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction, and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate through three case studies that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules. SPARROW is open source and can be found at http://github.com/coleygroup/sparrow