Oomycete plant pathogens deliver effector proteins inside host cells to modulate plant defense circuitry and to enable parasitic colonization. These effectors are defined by a conserved motif, termed RXLR (for Arg, any amino acid, Leu, Arg), that is located downstream of the signal peptide and that has been implicated in host translocation. Because the phenotypes of RXLR effectors extend to plant cells, their genes are expected to be the direct target of the evolutionary forces that drive the antagonistic interplay between pathogen and host. We used the draft genome sequences of three oomycete plant pathogens, Phytophthora sojae, Phytophthora ramorum, and Hyaloperonospora parasitica, to generate genome-wide catalogs of RXLR effector genes and determine the extent to which these genes are under positive selection. These analyses revealed that the RXLR sequence is overrepresented and positionally constrained in the secretome of Phytophthora relative to other eukaryotes. The three examined plant pathogenic oomycetes carry complex and diverse sets of RXLR effector genes that have undergone relatively rapid birth and death evolution. We obtained robust evidence of positive selection in more than two-thirds of the examined paralog families of RXLR effectors. Positive selection has acted for the most part on the C-terminal region, consistent with the view that RXLR effectors are modular, with the N terminus involved in secretion and host translocation and the C-terminal domain dedicated to modulating host defenses inside plant cells.

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Nowadays, complex systems are dominating our contemporary as well as professional lives. These modern technical products are considered as mechatronic systems which incorporate mechanics with electronics, software, and control in various domains mainly transport, medicine, and robotics. The development of these modern technical products is thus so tough. Hence, mechatronics’ critical challenges are to be not only well understood but also supported by practical models and tools in order to overcome this difficulty. Moreover, using the traditional design method which is point based is inefficient as it leads to a huge decrease in the innovation potential through limiting the design space to few solutions, an important increase in the cost of the product as well as production delay due to the great number of iterations. Some product development practices have shifted from using the “fixed-point design” approach to the “set-based design” one. Indeed, the set-based concurrent engineering widely considers a set of possible solutions and then shrinks the number of possibilities in order to converge toward a final solution. Yet, since this approach is too difficult to be put into action, a small number of industries in the field of mechatronics use the set-based concurrent engineering concept. Accordingly, this work aims to develop a novel method for the purpose of facilitating the development of a complex product based on the set-based concurrent engineering method and its implementation in an industrial setting by developing an algorithm to find the set of possible solutions to the design problem and narrow this set to merge toward the final solution. Finally, this algorithm is implemented using “Python” language.