Defining the Plasticity of Transcription Factor Binding Sites by Deconstructing DNA Consensus Sequences: The PhoP-Binding Sites among Gamma/Enterobacteria

Transcriptional regulators recognize specific DNA sequences. Because these sequences are embedded in the background of genomic DNA, it is hard to identify the key cis-regulatory elements that determine disparate patterns of gene expression. The detection of the intra- and inter-species differences among these sequences is crucial for understanding the molecular basis of both differential gene expression and evolution. Here, we address this problem by investigating the target promoters controlled by the DNA-binding PhoP protein, which governs virulence and Mg2+ homeostasis in several bacterial species. PhoP is particularly interesting; it is highly conserved in different gamma/enterobacteria, regulating not only ancestral genes but also governing the expression of dozens of horizontally acquired genes that differ from species to species. Our approach consists of decomposing the DNA binding site sequences for a given regulator into families of motifs (i.e., termed submotifs) using a machine learning method inspired by the “Divide & Conquer” strategy. By partitioning a motif into sub-patterns, computational advantages for classification were produced, resulting in the discovery of new members of a regulon, and alleviating the problem of distinguishing functional sites in chromatin immunoprecipitation and DNA microarray genome-wide analysis. Moreover, we found that certain partitions were useful in revealing biological properties of binding site sequences, including modular gains and losses of PhoP binding sites through evolutionary turnover events, as well as conservation in distant species. The high conservation of PhoP submotifs within gamma/enterobacteria, as well as the regulatory protein that recognizes them, suggests that the major cause of divergence between related species is not due to the binding sites, as was previously suggested for other regulators. Instead, the divergence may be attributed to the fast evolution of orthologous target genes and/or the promoter architectures resulting from the interaction of those binding sites with the RNA polymerase

A survey on test suite reduction frameworks and tools

Software testing is a widely accepted practice that ensures the quality of a System under Test (SUT). However, the gradual increase of the test suite size demands high portion of testing budget and time. Test Suite Reduction (TSR) is considered a potential approach to deal with the test suite size problem. Moreover, a complete automation support is highly recommended for software testing to adequately meet the challenges of a resource constrained testing environment. Several TSR frameworks and tools have been proposed to efficiently address the test-suite size problem. The main objective of the paper is to comprehensively review the state-of-the-art TSR frameworks to highlights their strengths and weaknesses. Furthermore, the paper focuses on devising a detailed thematic taxonomy to classify existing literature that helps in understanding the underlying issues and proof of concept. Moreover, the paper investigates critical aspects and related features of TSR frameworks and tools based on a set of defined parameters. We also rigorously elaborated various testing domains and approaches followed by the extant TSR frameworks. The results reveal that majority of TSR frameworks focused on randomized unit testing, and a considerable number of frameworks lacks in supporting multi-objective optimization problems. Moreover, there is no generalized framework, effective for testing applications developed in any programming domain. Conversely, Integer Linear Programming (ILP) based TSR frameworks provide an optimal solution for multi-objective optimization problems and improve execution time by running multiple ILP in parallel. The study concludes with new insights and provides an unbiased view of the state-of-the-art TSR frameworks. Finally, we present potential research issues for further investigation to anticipate efficient TSR frameworks

Combinatorial Algorithms for Computing Column Space Bases That Have Sparse Inverses

Abstract. This paper presents a new combinatorial approach towards constructing a sparse, implicit basis for the null space of a sparse, under-determined matrix. Our approach is to compute a column space basis of that has a sparse inverse, which could be used to represent a null space basis in implicit form. We investigate three different algorithms for computing column space bases: two greedy algorithms implemented using graph matchings, and a third, which employs a divide and conquer strategy implemented with hypergraph partitioning followed by a matching. Our results show that for many matrices from linear programming, structural analysis, and circuit simulation, it is possible to compute column space bases having sparse inverses, contrary to conventional wisdom. The hypergraph partitioning method yields sparser basis inverses and has low computational time requirements, relative to the greedy approaches. We also discuss the complexity of selecting a column space basis when it is known that such a basis exists in block diagonal form with a given small block size. Key words. sparse column space basis, sparse null space basis, block angular matrix, block diagonal matrix, matching, hypergraph partitioning, inverse of a basis AMS subject classifications. 65F50, 68R10, 90C20 1. Introduction. Man

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), there is an unavoidable need to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To fulfill this need, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small- and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC in function of varying target coverage percentage as well as number and placement direction of dominant obstacles. (C) 2018 Elsevier B.V. All rights reserved

Reducing adverse impacts of Amazon hydropower expansion

Proposed hydropower dams at more than 350 sites throughout the Amazon require strategic evaluation of trade-offs between the numerous ecosystem services provided by Earth\u27s largest and most biodiverse river basin. These services are spatially variable, hence collective impacts of newly built dams depend strongly on their configuration. We use multiobjective optimization to identify portfolios of sites that simultaneously minimize impacts on river flow, river connectivity, sediment transport, fish diversity, and greenhouse gas emissions while achieving energy production goals. We find that uncoordinated, dam-by-dam hydropower expansion has resulted in forgone ecosystem service benefits. Minimizing further damage from hydropower development requires considering diverse environmental impacts across the entire basin, as well as cooperation among Amazonian nations. Our findings offer a transferable model for the evaluation of hydropower expansion in transboundary basins

A Multi-objective Genetic Algorithm for Peptide Optimization

The peptide-based drug design process requires the identification of a wide range of candidate molecules with specific biological, chemical and physical properties. The laboratory analysis in terms of in vitro methods for the discovery of several physiochemical properties of theoretical candidate molecules is time- and cost-intensive. Hence, in silico methods are required for this purpose. Metaheuristics like evolutionary algorithms are considered to be adequate in silico methods providing good approximate solutions to the underlying multiobjective optimization problems. The general issue in this area is the design of a multi-objective evolutionary algorithm to achieve a maximum number of high-quality candidate peptides that differ in their genetic material, in a minimum number of generations. A multi-objective evolutionary algorithm as an in silico method of discovering a large number of high-quality peptides within a low number of generations for a broad class of molecular optimization problems of different dimensions is challenging, and the development of such a promising multi-objective evolutionary algorithm based on theoretical considerations is the major contribution of this thesis. The design of this algorithm is based on a qualitative landscape analysis applied on a three- and four-dimensional biochemical optimization problem. The conclusions drawn from the empirical landscape analysis of the three- and four-dimensional optimization problem result in the formulation of hypotheses regarding the types of evolutionary algorithm components which lead to an optimized search performance for the purpose of peptide optimization. Starting from the established types of variation operators and selection strategies, different variation operators and selection strategies are proposed and empirically verified on the three- and four-dimensional molecular optimization problem with regard to an optimized interaction and the identification of potential interdependences as well as a fine-tuning of the parameters. Moreover, traditional issues in the field of evolutionary algorithms such as selection pressure and the influence of multi-parent recombination are investigated

Evolutionary computation for software testing

A variety of products undergo a transformation from a pure mechanical design to more and more software and electronic components. A polarized example are watches. Several decades ago they have been purely mechanical. Modern smart watches are almost completely electronic devices which heavily rely on software. Further, a smart watch offers a lot more features than just the information about the current time. This change had a crucial impact on how software is being developed. A first attempt to control the rising complexity was to move to agile development practices such as extreme programming or scrum. This rise in complexity is not only affecting the development process but also quality assurance and software testing. If a product contains more and more features then this leads to a higher number of tests necessary to ensure quality standards. Furthermore agile development practices work in an iterative manner which leads to repetitive testing that puts more effort on the testing team. We aimed within the thesis to ease the pain of testing. Thereby we examined a series of subproblems that arise. A key complexity is the number of test cases. We intended to reduce the number of test cases before they are executed manually or implemented as automated tests. Thereby we examined the test specification and based on the requirements coverage of the individual tests, we were able to identify redundant tests. We relied on a novel metaheuristic called GCAIS which we improved upon iteratively. Another task is to control the remaining complexity. Testing is often time crucial and an appropriate subset of the available tests must be chosen in order to get a quick insight into the status of the device under test. We examined this challenge in two different testing scenarios. The first scenario is located in semi-automated testing where engineers execute a set of automated tests locally and closely observe the behaviour of the system under test. We extended GCAIS to compute test suites that satisfy different criteria if provided with sufficient search time. The second use case is located in fully automated testing in a continuous integration (CI) setting. CI focuses on frequent software build cycles which also include testing. These builds contain a testing stage which greatly emphasizes speed. Thus there we also have to compute crucial tests. However, due to the nature of the process we have to continuously recompute a test suite for each build as the software and maybe even the test cases at hand have changed. Hence it is hard to compute the test suite ahead of time and these tests have to be determined as part of the CI execution. Thus we switched to a computational lightweight learning classifier system (LCS) to prioritize and select test cases. We integrated a series of innovations we made into an LCS known as XCSF such as continuous priorities, experience replay and transfer learning. This enabled us to outperform a state of the art artificial neural network which is used by companies such as Netflix. We further investigated how LCS can be made faster using parallelism. We developed generic approaches which may run on any multicore computing device. This is of interest for our CI use case as the build server's architecture is unknown. However, the methods are also independent of the concrete LCS and are not linked to our testing problem. We identified that many of the challenges that need to be faced in the CI use case have been tackled by Organic Computing (OC), for example the need to adapt to an ever changing environment. Hence we relied on OC design principles to create a system architecture which wraps the LCS developed and integrates it into existing CI processes. The final system is robust and highly autonomous. A side-effect of the high degree of autonomy is a high level of automatization which fits CI well. We also gave insight on the usability and delivery of the full system to our industrial partner. Test engineers can easily integrate it with a few lines of code and need no knowledge about LCS and OC in order to use it. Another implication of the developed system is that OC's ideas and design principles can also be employed outside the field of embedded systems. This shows that OC has a greater level of generality. The process of testing and correcting found errors is still only partially automated. We make a first step into automating the entire process and thereby take an analogy to the concept of self-healing of OC. As a first proof of concept of this school of thought we take a look at touch interfaces. There we can automatically manipulate the software to fulfill the specified behaviour. Thus only a minimalistic amount of manual work is required

An Evolutionary Approach to Multistage Portfolio Optimization

Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl