Química combinatória de materiais com análise por microdifração de raios X. Primeira parte: fundamentos

Combinatorial chemistry refers to techniques to rapidly fabricate tens, hundreds or even thousands of different micro samples. The analysis of the large number of samples generated by combinatorial methods requires highly efficient analytical methods. In this case, the challenges are due not only to the large number of samples to be analyzed, but also to the small amount of sample available for analysis. This paper describes the fundamentals of combinatorial chemical methods applied to discover of materials and the development in x-ray diffraction to analyze micro samples.

Testausmenetelmien ja testauksen lähestymistapojen valintaperusteet kirjallisuudessa

Testaustapausten valitseminen on testauksessa tärkeää, koska kaikkia testaustapauksia ei voida testata aika- ja raharajoitteiden takia. Testaustapausten valintaan on paljon eri menetelmiä joista eniten esillä olevat ovat malleihin perustuva valinta, kombinaatiovalinta ja riskeihin perustuva valinta. Kaikkiin edellä mainittuihin menetelmiin testaustapaukset luodaan ohjelman spesifikaation perusteella. Malleihin perustuvassa menetelmässä käytetään hyväksi ohjelman toiminnasta olevia malleja, joista valitaan tärkeimmät testattavaksi. Kombinaatiotestauksessa testitapaukset on muodostettu ominaisuuspareina jolloin yhden parin testaamisesta päätellään kahden ominaisuuden toiminta. Kombinaatiotestaus on tehokas löytämään virheitä, jotka johtuvat yhdestä tai kahdesta tekijästä. Riskeihin perustuva testaus pyrkii arvioimaan ohjelman riskejä ja valitsemaan testitapaukset niiden perusteella. Kaikissa menetelmissä priorisointi on tärkeässä roolissa, jotta testauksesta saadaan riittävä luotettavuus ilman kustannusten nousua.

Explorando produtos naturais microbianos nas fronteiras da Química e da Biologia

The chemistry of natural products has been remarkably growing in the past few decades in Brazil. Aspects related to the isolation and identification of new natural products, as well as their biological activities, have been achieved in different laboratories working on this subject in the country. More recently, the introduction of new molecular biology tools has strongly influenced the research on natural products, mainly those produced by microorganisms, creating new possibilities to assess the chemical diversity of secondary metabolites. This paper describes some ideas on how the research on natural products can have a considerable input from molecular biology in the generation of chemical diversity. We also explore the role of microbial natural products in mediating interspecific interactions and their relevance to ecological studies. Examples of the generation of chemical diversity are highlighted by using genome mining, mutasynthesis, combinatorial biosynthesis, metagenomics, and synthetic biology, while some aspects of microbial ecology are also discussed. The idea to bring up this topic is linked to the remarkable development of molecular biology techniques to generate useful chemicals from different organisms. Here, we focus mainly on microorganisms, even though similar approaches have also been applied to the study of plants and other organisms. Investigations in the frontier of chemistry and biology require interactions between different areas, characterizing the interdisciplinarity of this research field. The necessity of a real integration of chemistry and biology is pivotal to finding correct answers to a number of biological phenomena. The use of molecular biology tools to generate chemical diversity and control biosynthetic pathways is largely explored in the production of important biologically active compounds. Finally, we briefly comment on the Brazilian organization of research in this area, the necessity of new strategies for the graduation programs, and the establishment of networks as a way of organization to overcome some of the problems faced in the area of natural products.

A cell spot microarray method for high-throughput biology

High-throughput screening of cellular effects of RNA interference (RNAi) libraries is now being increasingly applied to explore the role of genes in specific cell biological processes and disease states. However, the technology is still limited to specialty laboratories, due to the requirements for robotic infrastructure, access to expensive reagent libraries, expertise in high-throughput screening assay development, standardization, data analysis and applications. In the future, alternative screening platforms will be required to expand functional large-scale experiments to include more RNAi constructs, allow combinatorial loss-of-function analyses (e.g. genegene or gene-drug interaction), gain-of-function screens, multi-parametric phenotypic readouts or comparative analysis of many different cell types. Such comprehensive perturbation of gene networks in cells will require a major increase in the flexibility of the screening platforms, throughput and reduction of costs. As an alternative for the conventional multi-well based high-throughput screening -platforms, here the development of a novel cell spot microarray method for production of high density siRNA reverse transfection arrays is described. The cell spot microarray platform is distinguished from the majority of other transfection cell microarray techniques by the spatially confined array layout that allow highly parallel screening of large-scale RNAi reagent libraries with assays otherwise difficult or not applicable to high-throughput screening. This study depicts the development of the cell spot microarray method along with biological application examples of high-content immunofluorescence and phenotype based cancer cell biological analyses focusing on the regulation of prostate cancer cell growth, maintenance of genomic integrity in breast cancer cells, and functional analysis of integrin protein-protein interactions in situ.

Lethal weapons : novel approaches for receptor-targeted cancer cell elimination

The currently used forms of cancer therapy are associated with drug resistance and toxicity to healthy tissues. Thus, more efficient methods are needed for cancer-specific induction of growth arrest and programmed cell death, also known as apoptosis. Therapeutic forms of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) are investigated in clinical trials due to the capability of TRAIL to trigger apoptosis specifically in cancer cells by activation of cell surface death receptors. Many tumors, however, have acquired resistance to TRAIL-induced apoptosis and sensitizing drugs for combinatorial treatments are, therefore, in high demand. This study demonstrates that lignans, natural polyphenols enriched in seeds and cereal, have a remarkable sensitizing effect on TRAIL-induced cell death at non-toxic lignan concentrations. In TRAIL-resistant and androgen-dependent prostate cancer cells we observe that lignans repress receptor tyrosine kinase (RTK) activity and downregulate cell survival signaling via the Akt pathway, which leads to increased TRAIL sensitivity. A structure-activity relationship analysis reveals that the γ-butyrolactone ring of the dibenzylbutyrolactone lignans is essential for the rapidly reversible TRAIL-sensitizing activity of these compounds. Furthermore, the lignan nortrachelogenin (NTG) is identified as the most efficient of the 27 tested lignans and norlignans in sensitization of androgen-deprived prostate cancer cells to TRAIL-induced apoptosis. While this combinatorial anticancer approach may leave normal cells unharmed, several efficient cancer drugs are too toxic, insoluble or unstable to be used in systemic therapy. To enable use of such drugs and to protect normal cells from cytotoxic effects, cancer-targeted drug delivery vehicles of nanometer scale have recently been generated. The newly developed nanoparticle system that we tested in vitro for cancer cell targeting combines the efficient drug-loading capacity of mesoporous silica to the versatile particle surface functionalization of hyperbranched poly(ethylene imine), PEI. The mesoporous hybrid silica nanoparticles (MSNs) were functionalized with folic acid to promote targeted internalization by folate receptor overexpressing cancer cells. The presented results demonstrate that the developed carrier system can be employed in vitro for cancer selective delivery of adsorbed or covalently conjugated molecules and furthermore, for selective induction of apoptotic cell death in folate receptor expressing cancer cells. The tested carrier system displays potential for simultaneous delivery of several anticancer agents specifically to cancer cells also in vivo.

Solving Challenging Real-World Scheduling Problems

This work contains a series of studies on the optimization of three real-world scheduling problems, school timetabling, sports scheduling and staff scheduling. These challenging problems are solved to customer satisfaction using the proposed PEAST algorithm. The customer satisfaction refers to the fact that implementations of the algorithm are in industry use. The PEAST algorithm is a product of long-term research and development. The first version of it was introduced in 1998. This thesis is a result of a five-year development of the algorithm. One of the most valuable characteristics of the algorithm has proven to be the ability to solve a wide range of scheduling problems. It is likely that it can be tuned to tackle also a range of other combinatorial problems. The algorithm uses features from numerous different metaheuristics which is the main reason for its success. In addition, the implementation of the algorithm is fast enough for real-world use.

Qualitative Characteristics and Quantitative Measures of Solution's Reliability in Discrete Optimization: Traditional Analytical Approaches, Innovative Computational Methods and Applicability

The purpose of this thesis is twofold. The first and major part is devoted to sensitivity analysis of various discrete optimization problems while the second part addresses methods applied for calculating measures of solution stability and solving multicriteria discrete optimization problems. Despite numerous approaches to stability analysis of discrete optimization problems two major directions can be single out: quantitative and qualitative. Qualitative sensitivity analysis is conducted for multicriteria discrete optimization problems with minisum, minimax and minimin partial criteria. The main results obtained here are necessary and sufficient conditions for different stability types of optimal solutions (or a set of optimal solutions) of the considered problems. Within the framework of quantitative direction various measures of solution stability are investigated. A formula for a quantitative characteristic called stability radius is obtained for the generalized equilibrium situation invariant to changes of game parameters in the case of the H¨older metric. Quality of the problem solution can also be described in terms of robustness analysis. In this work the concepts of accuracy and robustness tolerances are presented for a strategic game with a finite number of players where initial coefficients (costs) of linear payoff functions are subject to perturbations. Investigation of stability radius also aims to devise methods for its calculation. A new metaheuristic approach is derived for calculation of stability radius of an optimal solution to the shortest path problem. The main advantage of the developed method is that it can be potentially applicable for calculating stability radii of NP-hard problems. The last chapter of the thesis focuses on deriving innovative methods based on interactive optimization approach for solving multicriteria combinatorial optimization problems. The key idea of the proposed approach is to utilize a parameterized achievement scalarizing function for solution calculation and to direct interactive procedure by changing weighting coefficients of this function. In order to illustrate the introduced ideas a decision making process is simulated for three objective median location problem. The concepts, models, and ideas collected and analyzed in this thesis create a good and relevant grounds for developing more complicated and integrated models of postoptimal analysis and solving the most computationally challenging problems related to it.

RNA and DNA aptamers as potential tools to prevent cell adhesion in disease

Recent research has shown that receptor-ligand interactions between surfaces of communicating cells are necessary prerequisites for cell proliferation, cell differentiation and immune defense. Cell-adhesion events have also been proposed for pathological conditions such as cancer growth, metastasis, and host-cell invasion by parasites such as Trypanosoma cruzi. RNA and DNA aptamers (aptus = Latin, fit) that have been selected from combinatorial nucleic acid libraries are capable of binding to cell-adhesion receptors leading to a halt in cellular processes induced by outside signals as a consequence of blockage of receptor-ligand interactions. We outline here a novel approach using RNA aptamers that bind to T. cruzi receptors and interrupt host-cell invasion in analogy to existing procedures of blocking selectin adhesion and function in vitro and in vivo.

Design and applications of modified oligonucleotides

Oligonucleotides have a wide range of applications in fields such as biotechnology, molecular biology, diagnosis and therapy. However, the spectrum of uses can be broadened by introducing chemical modifications into their structures. The most prolific field in the search for new oligonucleotide analogs is the antisense strategy, where chemical modifications confer appropriate characteristics such as hybridization, resistance to nucleases, cellular uptake, selectivity and, basically, good pharmacokinetic and pharmacodynamic properties. Combinatorial technology is another research area where oligonucleotides and their analogs are extensively employed. Aptamers, new catalytic ribozymes and deoxyribozymes are RNA or DNA molecules individualized from a randomly synthesized library on the basis of a particular property. They are identified by repeated cycles of selection and amplification, using PCR technologies. Modified nucleotides can be introduced either during the amplification procedure or after selection.

Optimization and Measuring Techniques for Collect-and-Place Machines in Printed Circuit Board Industry

This thesis considers optimization problems arising in printed circuit board assembly. Especially, the case in which the electronic components of a single circuit board are placed using a single placement machine is studied. Although there is a large number of different placement machines, the use of collect-and-place -type gantry machines is discussed because of their flexibility and increasing popularity in the industry. Instead of solving the entire control optimization problem of a collect-andplace machine with a single application, the problem is divided into multiple subproblems because of its hard combinatorial nature. This dividing technique is called hierarchical decomposition. All the subproblems of the one PCB - one machine -context are described, classified and reviewed. The derived subproblems are then either solved with exact methods or new heuristic algorithms are developed and applied. The exact methods include, for example, a greedy algorithm and a solution based on dynamic programming. Some of the proposed heuristics contain constructive parts while others utilize local search or are based on frequency calculations. For the heuristics, it is made sure with comprehensive experimental tests that they are applicable and feasible. A number of quality functions will be proposed for evaluation and applied to the subproblems. In the experimental tests, artificially generated data from Markov-models and data from real-world PCB production are used. The thesis consists of an introduction and of five publications where the developed and used solution methods are described in their full detail. For all the problems stated in this thesis, the methods proposed are efficient enough to be used in the PCB assembly production in practice and are readily applicable in the PCB manufacturing industry.

Efficacy of the dietary histone deacetylase inhibitor butyrate alone or in combination with vitamin A against proliferation of MCF-7 human breast cancer cells

The combined treatment with histone deacetylase inhibitors (HDACi) and retinoids has been suggested as a potential epigenetic strategy for the control of cancer. In the present study, we investigated the effects of treatment with butyrate, a dietary HDACi, combined with vitamin A on MCF-7 human breast cancer cells. Cell proliferation was evaluated by the crystal violet staining method. MCF-7 cells were plated at 5 x 10(4) cells/mL and treated with butyrate (1 mM) alone or combined with vitamin A (10 µM) for 24 to 120 h. Cell proliferation inhibition was 34, 10 and 46% following treatment with butyrate, vitamin A and their combination, respectively, suggesting that vitamin A potentiated the inhibitory activities of butyrate. Furthermore, exposure to this short-chain fatty acid increased the level of histone H3K9 acetylation by 9.5-fold (Western blot), but not of H4K16, and increased the expression levels of p21WAF1 by 2.7-fold (Western blot) and of RARβ by 2.0-fold (quantitative real-time PCR). Our data show that RARβ may represent a molecular target for butyrate in breast cancer cells. Due to its effectiveness as a dietary HDACi, butyrate should be considered for use in combinatorial strategies with more active retinoids, especially in breast cancers in which RARβ is epigenetically altered.

Computational models for and from biology : simple gene assembly and reaction systems

The advancement of science and technology makes it clear that no single perspective is any longer sufficient to describe the true nature of any phenomenon. That is why the interdisciplinary research is gaining more attention overtime. An excellent example of this type of research is natural computing which stands on the borderline between biology and computer science. The contribution of research done in natural computing is twofold: on one hand, it sheds light into how nature works and how it processes information and, on the other hand, it provides some guidelines on how to design bio-inspired technologies. The first direction in this thesis focuses on a nature-inspired process called gene assembly in ciliates. The second one studies reaction systems, as a modeling framework with its rationale built upon the biochemical interactions happening within a cell. The process of gene assembly in ciliates has attracted a lot of attention as a research topic in the past 15 years. Two main modelling frameworks have been initially proposed in the end of 1990s to capture ciliates’ gene assembly process, namely the intermolecular model and the intramolecular model. They were followed by other model proposals such as templatebased assembly and DNA rearrangement pathways recombination models. In this thesis we are interested in a variation of the intramolecular model called simple gene assembly model, which focuses on the simplest possible folds in the assembly process. We propose a new framework called directed overlap-inclusion (DOI) graphs to overcome the limitations that previously introduced models faced in capturing all the combinatorial details of the simple gene assembly process. We investigate a number of combinatorial properties of these graphs, including a necessary property in terms of forbidden induced subgraphs. We also introduce DOI graph-based rewriting rules that capture all the operations of the simple gene assembly model and prove that they are equivalent to the string-based formalization of the model. Reaction systems (RS) is another nature-inspired modeling framework that is studied in this thesis. Reaction systems’ rationale is based upon two main regulation mechanisms, facilitation and inhibition, which control the interactions between biochemical reactions. Reaction systems is a complementary modeling framework to traditional quantitative frameworks, focusing on explicit cause-effect relationships between reactions. The explicit formulation of facilitation and inhibition mechanisms behind reactions, as well as the focus on interactions between reactions (rather than dynamics of concentrations) makes their applicability potentially wide and useful beyond biological case studies. In this thesis, we construct a reaction system model corresponding to the heat shock response mechanism based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We also introduce for RS various concepts inspired by biology, e.g., mass conservation, steady state, periodicity, etc., to do model checking of the reaction systems based models. We prove that the complexity of the decision problems related to these properties varies from P to NP- and coNP-complete to PSPACE-complete. We further focus on the mass conservation relation in an RS and introduce the conservation dependency graph to capture the relation between the species and also propose an algorithm to list the conserved sets of a given reaction system.

Particle swarm optimization for two-connected networks with bounded rings

The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic population-based optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.

Zero-sum problems in finite cyclic groups

The purpose of this thesis is to investigate some open problems in the area of combinatorial number theory referred to as zero-sum theory. A zero-sequence in a finite cyclic group G is said to have the basic property if it is equivalent under group automorphism to one which has sum precisely IGI when this sum is viewed as an integer. This thesis investigates two major problems, the first of which is referred to as the basic pair problem. This problem seeks to determine conditions for which every zero-sequence of a given length in a finite abelian group has the basic property. We resolve an open problem regarding basic pairs in cyclic groups by demonstrating that every sequence of length four in Zp has the basic property, and we conjecture on the complete solution of this problem. The second problem is a 1988 conjecture of Kleitman and Lemke, part of which claims that every sequence of length n in Zn has a subsequence with the basic property. If one considers the special case where n is an odd integer we believe this conjecture to hold true. We verify this is the case for all prime integers less than 40, and all odd integers less than 26. In addition, we resolve the Kleitman-Lemke conjecture for general n in the negative. That is, we demonstrate a sequence in any finite abelian group isomorphic to Z2p (for p ~ 11 a prime) containing no subsequence with the basic property. These results, as well as the results found along the way, contribute to many other problems in zero-sum theory.

Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.