Comparative genomics in bacteria belonging to the Rhizobiaceae family, such as Sinorhizobium meliloti, Agrobacterium tumefaciens, Mesorhizobium loti, Brucella melitensis and Rhizobium etli, has led to the understanding that syntenic genes participate in 80–90% of the essential functions for growth and cellular division. In this way, synteny has an important biological significance in these organisms (Guerrero et al., 2005). Proteins encoded by syntenic genes had a high degree of identity, although they displayed some differences in their amino acid composition. These changes may result from an evolutionary adaptation to their specific environments.

The selection of manufacturing resources affects the cost, time, and quality of products. Manufacturing resources must be selected to fulfill product design specifications and production requirements. One particular case of determining resources is the configuration of machine tools, as it includes the selection of the required machine tools and the corresponding cutters, fixtures and operation conditions.This paper describes a machine tools configuration process and a software application based on information models to assist it. The process has been devised to concurrently select resources and determine operation conditions, emphasizing the close relationship that exists between them and to guarantee their compatibility. The information models represent product and manufacturing data, and capture suppliers catalogues information. The software applications employ neural networks to select manufacturing process and resources, and genetic algorithms to determine operation conditions to obtain a compromise between cost, time and quality. The utility of the approach is explored through a case study.