Frédéric Lardeux

A «Repertoire for Repertoire» Hypothesis: Repertoires of Type Three Effectors are Candidate Determinants of Host Specificity in Xanthomonas

Background The genetic basis of host specificity for animal and plant pathogenic bacteria remains poorly understood. For plant pathogenic bacteria, host range is restricted to one or a few host plant species reflecting a tight adaptation to specific hosts. Methodology/Principal Findings Two hypotheses can be formulated to explain host specificity: either it can be explained by the phylogenetic position of the strains, or by the association of virulence genes enabling a pathological convergence of phylogenically distant strains. In this latter hypothesis, host specificity would result from the interaction between repertoires of bacterial virulence genes and repertoires of genes involved in host defences. To challenge these two hypotheses, we selected 132 Xanthomonas axonopodis strains representative of 18 different pathovars which display different host range. First, the phylogenetic position of each strain was determined by sequencing the housekeeping gene rpoD. This study showed that many pathovars of Xanthomonas axonopodis are polyphyletic. Second, we investigated the distribution of 35 type III effector genes (T3Es) in these strains by both PCR and hybridization methods. Indeed, for pathogenic bacteria T3Es were shown to trigger and to subvert host defences. Our study revealed that T3E repertoires comprise core and variable gene suites that likely have distinct roles in pathogenicity and different evolutionary histories. Our results showed a correspondence between composition of T3E repertoires and pathovars of Xanthomonas axonopodis. For polyphyletic pathovars, this suggests that T3E genes might explain a pathological convergence of phylogenetically distant strains. We also identified several DNA rearrangements within T3E genes, some of which correlate with host specificity of strains. Conclusions/Significance These data provide insight into the potential role played by T3E genes for pathogenic bacteria and support a “repertoire for repertoire” hypothesis that may explain host specificity. Our work provides resources for functional and evolutionary studies aiming at understanding host specificity of pathogenic bacteria, functional redundancy between T3Es and the driving forces shaping T3E repertoires.

Autonomous operator management for evolutionary algorithms

The performance of an evolutionary algorithm strongly depends on the design of its operators and on the management of these operators along the search; that is, on the ability of the algorithm to balance exploration and exploitation of the search space. Recent approaches automate the tuning and control of the parameters that govern this balance. We propose a new technique to dynamically control the behavior of operators in an EA and to manage a large set of potential operators. The best operators are rewarded by applying them more often. Tests of this technique on instances of 3-SAT return results that are competitive with an algorithm tailored to the problem.

GASAT: a genetic local search algorithm for the satisfiability problem

This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.

Evolutionary computing for the satisfiability problem

This paper presents GASAT, a hybrid evolutionary algorithm for the satisfiability problem (SAT). A specific crossover operator generates new solutions, that are improved by a tabu search procedure. The performance of GASAT is assessed using a set of well-known benchmarks. Comparisons with state-of-the-art SAT algorithms show that GASAT gives very competitive results. These experiments also allow us to introduce a new SAT benchmark from a coloring problem.

A dynamic island model for adaptive operator selection

In this paper we propose a generic framework for Dynamic Island Models, which can be used as an original approach for the adaptive selection of operators in evolutionary algorithms. Assigning a variation operator to each island, we show that the dynamic regulation of migrations, which takes into account the pertinence of recent migrations, distributes the individuals on the most promising islands, i.e., the most efficient operators, at each stage of the search. The efficiency of this approach is assessed on the One-Max problem by comparing theoretical expected results to those obtained by our dynamic island model. Experiments show that the model provides the expected behavior.

A dynamic Island-based genetic algorithms framework

This work presents a dynamic island model framework for helping the resolution of combinatorial optimization problems with evolutionary algorithms. In this framework, the possible migrations among islands are represented by a complete graph. The migrations probabilities associated to each edge are dynamically updated with respect to the last migrations impact. This new framework is tested on the well-known 0/1 Knapsack problem and MAX-SAT problem. Good results are obtained and several properties of this framework are studied.

A multiplex-PCR assay for identification of the quarantine plant pathogen Xanthomonas axonopodis pv. phaseoli.

In this study we developed an algorithm to screen for all exact molecular signatures of the quarantine pathogen Xanthomonas axonopodis pv. phaseoli (Xap), based on available data of the presence or absence of virulence-associated genes. The simultaneous presence of genes avrBsT and xopL is specific to Xap. Therefore we developed a multiplex PCR assay targeting avrBsT and xopL for the molecular identification of Xap. The specificity of this multiplex was validated by comparison to that of other molecular identification assays aimed at Xap, on a wide collection of reference strains. This multiplex was further validated on a blind collection of Xanthomonas isolates for which pathogenicity was assayed by stem wounding and by dipping leaves into calibrated inocula. This multiplex was combined to the previously described X4c/X4e molecular identification assay for Xap. Such a combination enables the molecular identification of all strains of Xanthomonas pathogenic on bean. Results also show that assay by stem wounding does not give reliable results in the case of Xap, and that pathogenicity assays by dipping should be preferred.

Adaptive Operator Selection and Management in Evolutionary Algorithms

One of the settings that most affect the performance of Evolutionary Algorithms is the selection of the variation operators that are efficient to solve the problem at hand. The control of these operators can be handled in an autonomous way, while solving the problem, at two different levels: at the structural level, when deciding which operators should be part of the algorithmic framework, referred to as Adaptive Operator Management (AOM); and at the behavioral level, when selecting which of the available operators should be applied at a given time instant, called as Adaptive Operator Selection (AOS). Both controllers guide their choices based on a common knowledge about the recent performance of each operator. In this chapter, we present methods for these two complementary aspects of operator control, the ExCoDyMAB AOS and the Blacksmith AOM, providing case studies to analyze them in order to highlight the major issues that should be considered for the design of more autonomous Evolutionary Algorithms.

Controlling behavioral and structural parameters in evolutionary algorithms

Evolutionary algorithms have been efficiently used for solving combinatorial problems. However a successful application rely on a good definition of the algorithm structure and a correct search guidance. Similarly to the majority of metaheuristic methods, the performance of an evolutionary algorithm is intrinsically linked to its ability to properly manage the balance between the exploitation and the exploration of the search space. Recently, new approaches have emerged to make these algorithms more independent, especially by automating the setting of parameters. We propose a new approach whose objective is twofold: (1) to manage an important set of potential operators, whose performances are a priori unknown, and (2) to dynamically control the behavior of operators in a evolutionary algorithm, thanks to their probabilities of

SAT Encoding and CSP Reduction for Interconnected Alldiff Constraints

Constraint satisfaction problems (CSP) or Boolean satisfiability problem (SAT) are two well known paradigm to model and solve combinatorial problems. Modeling and resolution of CSP is often strengthened by global constraints (e.g., Alldiff constraint). This paper highlights two different ways of handling specific structural information: a uniform propagation framework to handle (interleaved) Alldiff constraints with some CSP reduction rules; and a SAT encoding of these rules that preserves the reduction properties of CSP.