Combinatorial Domains Research Articles

Cellular genetic algorithms without additional parameters

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to close ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore, in a better performance of the algorithm. However, it supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. We propose in this work two innovative cGAs with new adaptive techniques that allow removing the neighborhood and population shape from the algorithm's configuration. As a result, the new adaptive cGAs are highly competitive (statistically) with all the compared cGAs in terms of the average solutions found in the continuous and combinatorial domains, while finding, in general, the best solutions for the considered problems, and with less computational effort.

Fell-Muir Lecture: chondroitin sulphate glycosaminoglycans: fun for some and confusion for others.

This review emphasizes the importance of glycobiology in nature and aims to highlight, simplify and summarize the multiple functions and structural complexities of the different oligosaccharide combinatorial domains that are found in chondroitin sulphate/dermatan sulphate (CS/DS) glycosaminoglycan (GAG) chains. For example, there are 1008 different pentasaccharide sequences possible within CS, DS or CS/DS hybrid GAG chains. These combinatorial possibilities provide numerous potential ligand-binding domains that are important for cell and extracellular matrix interactions as well as specific associations with cytokines, chemokines, morphogens and growth factors that regulate cellular differentiation and proliferation during tissue development, for example, morphogen gradient establishment. The review provides some details of the large and diverse number of different enzymes that are involved in CS/DS biosynthesis and attempts to explain how differences in their expression patterns in different cell types can lead to subtle but important differences in the GAG metabolism that influence cellular proliferation and differentiation in development as well as regeneration and repair in disease. Our laboratory was the first to generate and characterize monoclonal antibodies (mAb) that very specifically recognize different ‘native’ sulphation motif/epitopes in CS/DS GAG chains. These monoclonal antibodies have been used to identify very specific spatio-temporal expression patterns of CS/DS sulphation motifs that occur during tissue and organ development (in particular their association with stem/progenitor cell niches) and also their recapitulated expression in adult tissues with the onset of degenerative joint diseases. In summary, diversity in CS/DS sulphation motif expression is a very important necessity for animal life as we know it.

Engineering human MEK-1 for structural studies: A case study of combinatorial domain hunting

Structural biology studies typically require large quantities of pure, soluble protein. Currently the most widely-used method for obtaining such protein involves the use of bioinformatics and experimental methods to design constructs of the target, which are cloned and expressed. Recently an alternative approach has emerged, which involves random fragmentation of the gene of interest and screening for well-expressing fragments. Here we describe the application of one such fragmentation method, combinatorial domain hunting (CDH), to a target which historically was difficult to express, human MEK-1. We show how CDH was used to identify a fragment which covers the kinase domain of MEK-1 and which expresses and crystallizes significantly better than designed expression constructs, and we report the crystal structure of this fragment which explains some of its superior properties. Gene fragmentation methods, such as CDH, thus hold great promise for tackling difficult-to-express target proteins.

A structural and functional dissection of the cardiac stress response factor MS1

AbstractMS1 is a protein predominantly expressed in cardiac and skeletal muscle that is upregulated in response to stress and contributes to development of hypertrophy. In the aortic banding model of left ventricular hypertrophy, its cardiac expression was significantly upregulated within 1 h. Its function is postulated to depend on its F‐actin binding ability, located to the C‐terminal half of the protein, which promotes stabilization of F‐actin in the cell thus releasing myocardin‐related transcription factors to the nucleus where they stimulate transcription in cooperation with serum response factor. Initial attempts to purify the protein only resulted in heavily degraded samples that showed distinct bands on SDS gels, suggesting the presence of stable domains. Using a combination of combinatorial domain hunting and sequence analysis, a set of potential domains was identified. The C‐terminal half of the protein actually contains two independent F‐actin binding domains. The most C‐terminal fragment (294–375), named actin binding domain 2 (ABD2), is independently folded while a proximal fragment called ABD1 (193–296) binds to F‐actin with higher affinity than ABD2 (KD 2.21 ± 0.47 μM vs. 10.61 ± 0.7 μM), but is not structured by itself in solution. NMR interaction experiments show that it binds and folds in a cooperative manner to F‐actin, justifying the label of domain. The architecture of the MS1 C‐terminus suggests that ABD1 alone could completely fulfill the F‐actin binding function opening up the intriguing possibility that ABD2, despite its high level of conservation, could have developed other functions. Proteins 2012. © 2011 Wiley Periodicals, Inc.

Optimal and Adaptive Operation of a Hydropower System with Unit Commitment and Water Quality Constraints

Management of water resources has become more complex in recent years as a result of changing attitudes towards sustainability and the attribution of greater attention to environmental issues, especially under a scenario of water scarcity risk introduced by climate changes and anthropogenic pressures. This study addresses the optimal short-term operation of a multi-purpose hydropower system under an environment where objectives are conflicting. New optimization models using mixed integer nonlinear programming (MINLP) with binary variables adopted for incorporating unit commitment constraints and adaptive real-time operations are developed and applied to a real life hydropower reservoir in Brazil, utilizing evolutionary algorithms. These formulations address water quality concerns downstream of the reservoir and optimal operations for power generation in an integrated manner and deal with uncertain future flows due to climate change. Results obtained using genetic algorithm (GA) solvers were superior to gradient based methods, converging to superior optimal solutions especially due to computational intractability problems associated with combinatorial domain of integer variables in the unit commitment formulation. The adaptive operation formulation in conjunction with the solution of turbine unit commitment problem yielded more reliable solutions, reducing forecasting uncertainty and providing more flexible operational rules.

Insights into Hox protein function from a large scale combinatorial analysis of protein domains.

Protein function is encoded within protein sequence and protein domains. However, how protein domains cooperate within a protein to modulate overall activity and how this impacts functional diversification at the molecular and organism levels remains largely unaddressed. Focusing on three domains of the central class Drosophila Hox transcription factor AbdominalA (AbdA), we used combinatorial domain mutations and most known AbdA developmental functions as biological readouts to investigate how protein domains collectively shape protein activity. The results uncover redundancy, interactivity, and multifunctionality of protein domains as salient features underlying overall AbdA protein activity, providing means to apprehend functional diversity and accounting for the robustness of Hox-controlled developmental programs. Importantly, the results highlight context-dependency in protein domain usage and interaction, allowing major modifications in domains to be tolerated without general functional loss. The non-pleoitropic effect of domain mutation suggests that protein modification may contribute more broadly to molecular changes underlying morphological diversification during evolution, so far thought to rely largely on modification in gene cis-regulatory sequences.

Four-layer framework for combinatorial optimization problems domain

Four-layer framework for combinatorial optimization problems/models domain is suggested for applied problems structuring and solving: (1) basic combinatorial models and multicriteria decision making problems (e.g., clustering, knapsack problem, multiple choice problem, multicriteria ranking, assignment/allocation); (2) composite models/procedures (e.g., multicriteria combinatorial problems, morphological clique problem); (3) basic (standard) solving frameworks, e.g.: (i) Hierarchical Morphological Multicriteria Design (HMMD) (ranking, combinatorial synthesis based on morphological clique problem), (ii) multi-stage design (two-level HMMD), (iii) special multi-stage composite framework (clustering, assignment/location, multiple choice problem); and (4) domain-oriented solving frameworks, e.g.: (a) design of modular software, (b) design of test inputs for multi-function system testing, (c) combinatorial planning of medical treatment, (d) design and improvement of communication network topology, (e) multi-stage framework for information retrieval, (f) combinatorial evolution and forecasting of software, devices. The multi-layer approach covers 'decision cycle', i.e., problem statement, models, algorithms/procedures, solving schemes, decisions, decision analysis and improvement.

A Combinatorial Method to Enable Detailed Investigation of Protein–Protein Interactions

Successful structural investigations of protein-protein interactions can be facilitated by studying only the core interacting regions of the constituent proteins. However, attempting the discovery of stable core complexes using informed trial-and-error approaches can prove time and resource intensive. We describe a valuable extension of combinatorial domain hunting (CDH), a technology for the timely elucidation of soluble protein truncations. The new method, CDH(2), enables empirical discovery of stable protein-protein core complexes. CDH(2) is demonstrated ab initio using a previously well-characterized Hsp90/Cdc37 complex. Without using a priori information, we demonstrate the isolation of stable protein-protein complexes, suitable for further analyses. This resource-efficient process can provide protein complexes for screening of compounds designed to modulate protein-protein interactions, thus facilitating novel drug discovery.

Decision making with multiple objectives using GAI networks

This paper deals with preference representation on combinatorial domains and preference-based recommendation in the context of multicriteria or multiagent decision making. The alternatives of the decision problem are seen as elements of a product set of attributes and preferences over solutions are represented by generalized additive decomposable (GAI) utility functions modeling individual preferences or criteria. Thanks to decomposability, utility vectors attached to solutions can be compiled into a graphical structure closely related to junction trees, the so-called GAI network. Using this structure, we present preference-based search algorithms for multicriteria or multiagent decision making. Although such models are often non-decomposable over attributes, we actually show that GAI networks are still useful to determine the most preferred alternatives provided preferences are compatible with Pareto dominance. We first present two algorithms for the determination of Pareto-optimal elements. Then the second of these algorithms is adapted so as to directly focus on the preferred solutions. We also provide results of numerical tests showing the practical efficiency of our procedures in various contexts such as compromise search and fair optimization in multicriteria or multiagent problems.

Guest editorial: special issue on computational social choice

Computational social choice is a new interdisciplinary research area at the intersection of social choice theory and computer science that studies computational aspects of social choice mechanisms as well as applications of social choice ideas in the design and analysis of computing systems. Research on computational social choice now encompasses a variety of topics, including the algorithmic aspects of voting rules, computational barriers to strategic behaviour, collective decision-making in multi-agent systems, preference elicitation and communication issues in voting, fair division, computational aspects of weighted voting games, collective decision-making in combinatorial domains, logic-based formalisms for social choice problems, belief and judgement aggregation, and social software. The aim of this special issue is to build on the spectacular success of two workshops on computational social choice (COMSOC’06, Amsterdam and COMSOC’08, Liverpool), and provide a forum for reseachers in the area to present their ideas on a variety of topics. This special issue is composed of nine research articles and a short note that span a fairly wide range of issues, including voting, mechanism design, judgment aggregation, and fairness, and a similarly wide range of techniques, such as logic, experiments, and computational complexity. The paper by Agotnes, van der Hoek and Wooldridge defines a new modal logic for representing and reasoning about preference and judgment aggregation. The authors show that their logic can be used to express a variety of concepts and results from the social choice domain, including majority voting, the discursive paradox, Arrow’s theorem and Condorcet’s paradox. Baharad, Goldberger, Koppel and Nitzan propose a maximum likelihood estimation-based approach for aggregating voters’ opinions over several issues. They argue that their method is vastly superior to simple majority voting, and use both analytical and empirical evaluation to support their argument. Balan, Richards and Luke deal with the problem of selecting a sequence of actions when the actions have multiple beneficiaries. Their goal is to select the actions according to a lexicographic fairness criterion. The explore the complexity of this problem for both finite and infinite sequences of actions and present both algorithms and intractability results for this problem. The paper by Caminada and Pigozzi applies argumentation theory to judgment aggregation, and proposes aggregation operators satisfying not only the usual property of collective rationality, but another highly desirable property,

Compactly representing utility functions using weighted goals and the max aggregator

Weighted propositional formulas can be used to model preferences over combinatorial domains: each formula represents a goal we would like to see satisfied, the weight of a formula represents the importance of the goal in question, and to assess the desirability of a given alternative we aggregate the weights of the goals satisfied by that alternative. One of several options is to aggregate by using the maximum of the weights of the satisfied goals. This approach gives rise to a family of preference representation languages, one for each of a range of possible restrictions we can impose on either formulas or weights. We analyze the properties of these languages and establish results regarding their expressivity, and absolute and relative succinctness. We also study the computational complexity of the problem of finding the best and the worst alternative for a given set of weighted goals, and of finding an alternative that is optimal for a group of agents, for a range of different notions of collective optimality proposed in social choice theory and welfare economics.

Combinatorial Domain Hunting: solving problems in protein expression

Modern drug discovery demands large amounts of high-quality protein and, therefore, begins by expressing target genes in heterologous systems such as bacteria or insect cells. However, some of the most attractive drug targets have proven challenging to clone and express. A technology called Combinatorial Domain Hunting has been developed to express regions or domains of proteins in a non-aggregated form. This technology has been applied to more than 50 targets and, in all cases, high-expressing protein domains have been produced. In many cases, the protein is also non-aggregated, making it ideal for developing assays for screening or determining its X-ray crystal structure.

Nonparametric Combinatorial Regression for Shape Constrained Modeling

We introduce a unified approach for calculating nonparametric shape constrained regression. Enforcement of the shape constraint often accounts for the impact of a physical phenomenon or a specific property. It also improves the model's predicability and facilitates subsequent optimizations. The regression models are built by transforming the problem into the combinatorial domain where the shape constraints are imposed by bounding the combinatorial search space. We start by addressing isotonicity shape constraint using a dynamic programming algorithm and demonstrate how the problem can be mapped to the graph combinatorics domain. Next we show how a number of other important shape constraints including unimodality, convexity, limited level set, and limited slope can be addressed using the same framework. The flexibility of proposed framework enables solving the shape constrained regression problem with an arbitrary user-defined error metric. This flexibility is exploited to add robustness against outliers to the model. The algorithms are described in detail and their computational complexity is established. The performance and effectiveness of the shape constrained regression is evaluated on traces of temperature and humidity measurements from a deployed sensor network where a high degree of accuracy and robustness is demonstrated.

Origami fold as algebraic graph rewriting

We formalize paper fold (origami) by graph rewriting. Origami construction is abstractly described by a rewriting system ( O , ↬ ) , where O is the set of abstract origamis and ↬ is a binary relation on O , that models fold. An abstract origami is a structure ( Π , ∽ , ≻ ) , where Π is a set of faces constituting an origami, and ∽ and ≻ are binary relations on Π , each representing adjacency and superposition relations between the faces. We then address representation and transformation of abstract origamis and further reasoning about the construction for computational purposes. We present a labeled hypergraph of origami and define fold as algebraic graph transformation. The algebraic graph-theoretic formalism enables us to reason about origami in two separate domains of discourse, i.e. pure combinatorial domain where symbolic computation plays the main role and geometrical domain R × R . We detail the program language for the algebraic graph rewriting and graph rewriting algorithms for the fold, and show how fold is expressed by a set of graph rewrite rules.

Directed Engineering of a High-expression Chimeric Transgene as a Strategy for Gene Therapy of Hemophilia A

Human coagulation factor VIII (fVIII) is inefficiently biosynthesized in vitro and has proven difficult to express at therapeutic levels using available clinical gene-transfer technologies. Recently, we showed that a porcine and certain hybrid human/porcine fVIII transgenes demonstrate up to 100-fold greater expression than human fVIII. In this study, we extend these results to describe the use of a humanized, high-expression, hybrid human/porcine fVIII transgene that is 89% identical to human fVIII and was delivered by lentiviral vectors (LVs) to hematopoietic stem cells for gene therapy of hemophilia A. Recombinant human immunodeficiency virus-based vectors encoding the fVIII chimera efficiently transduced human embryonic kidney (HEK)-293T cells. Cells transduced with hybrid human/porcine fVIII encoding vectors expressed fVIII at levels 6- to 100-fold greater than cells transduced with vectors encoding human fVIII. Transplantation of transduced hematopoietic stem and progenitor cells into hemophilia A mice resulted in long-term fVIII expression at therapeutic levels despite <5% genetically modified blood mononuclear cells. Furthermore, the simian immunodeficiency virus (SIV) -derived vector effectively transduced the human hematopoietic cell lines K562, EU1, U.937, and Jurkat as well as the nonhematopoietic cell lines, HEK-293T and HeLa. All cell lines expressed hybrid human/porcine fVIII, albeit at varying levels with the K562 cells expressing the highest level of the hematopoietic cell lines. From these studies, we conclude that humanized high-expression hybrid fVIII transgenes can be utilized in gene therapy applications for hemophilia A to significantly increase fVIII expression levels compared to what has been previously achieved.

Morphically primitive words

In the present paper, we introduce an alternative notion of the primitivity of words, that–unlike the standard understanding of this term–is not based on the power (and, hence, the concatenation) of words, but on morphisms. For any alphabet Σ , we call a word w ∈ Σ ∗ morphically imprimitive provided that there are a shorter word v and morphisms h , h ′ : Σ ∗ → Σ ∗ satisfying h ( v ) = w and h ′ ( w ) = v , and we say that w is morphically primitive otherwise. We explain why this is a well-chosen terminology, we demonstrate that morphic (im-) primitivity of words is a vital attribute in many combinatorial domains based on finite words and morphisms, and we study a number of fundamental properties of the concepts under consideration.

Preference Handling in Combinatorial Domains: From AI to Social Choice

In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multi-attribute) structure. We give an introduction to preference handling in combinatorial domains in the context of collective decision making, and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains, and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, known as computational social choice, that brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

A tool to convert continuous multiobjective optimisation test problems into combinatorial ones

We present a tool to convert continuous multiobjective test problems into combinatorial ones. In this paper, we present some methods to transform a multiobjective continuous test problem into a multiobjective combinatorial test problem. These transformations are applied to two experiments: a speed measurement comparison between three classical optimisation methods and trajectory visualisation of some classical optimisation method on a combinatorial test problem. With this transformation, we will be able to compare the behaviour of an optimisation method on a continuous test problem and on a combinatorial test problem. We will see that an explanation in the continuous domain of a change of behaviour among two aggregation methods can be transposed into the combinatorial domain.

A Model Reference Adaptive Search Method for Global Optimization

Model reference adaptive search (MRAS) for solving global optimization problems works with a parameterized probabilistic model on the solution space and generates at each iteration a group of candidate solutions. These candidate solutions are then used to update the parameters associated with the probabilistic model in such a way that the future search will be biased toward the region containing high-quality solutions. The parameter updating procedure in MRAS is guided by a sequence of implicit probabilistic models we call reference models. We provide a particular algorithm instantiation of the MRAS method, where the sequence of reference models can be viewed as the generalized probability distribution models for estimation of distribution algorithms (EDAs) with proportional selection scheme. In addition, we show that the model reference framework can also be used to describe the recently proposed cross-entropy (CE) method for optimization and to study its properties. Hence, this paper can also be seen as a study on the effectiveness of combining CE and EDAs. We prove global convergence of the proposed algorithm in both continuous and combinatorial domains, and we carry out numerical studies to illustrate the performance of the algorithm.

Combined Action of PHD and Chromo Domains Directs the Rpd3S HDAC to Transcribed Chromatin

Nucleosomes must be deacetylated behind elongating RNA polymerase II to prevent cryptic initiation of transcription within the coding region. RNA polymerase II signals for deacetylation through the methylation of histone H3 lysine 36 (H3K36), which provides the recruitment signal for the Rpd3S histone deacetylase complex (HDAC). The recognition of methyl H3K36 by Rpd3S requires the chromodomain of its Eaf3 subunit. Paradoxically, Eaf3 is also a subunit of the NuA4 acetyltransferase complex, yet NuA4 does not recognize methyl H3K36 nucleosomes. In Saccharomyces cerevisiae, we found that methyl H3K36 nucleosome recognition by Rpd3S also requires the plant homeobox domain (PHD) of its Rco1 subunit. Thus, the coupled chromo and PHD domains of Rpd3S specify recognition of the methyl H3K36 mark, demonstrating the first combinatorial domain requirement within a protein complex to read a specific histone code.