Most combinatorial optimization problems cannot be solved exactly. A class of methods, called metaheuristics, has proved its efficiency to give good approximated solutions in a reasonable time. Cooperative metaheuristics are a subset of metaheuristics, which implies a parallel exploration of the search space by several entities with information exchange between them. The importance of information exchange in the optimization process is related to the building block hypothesis of evolutionary algorithms, which is based on these two questions: what is the pertinent information of a given potential solution and how this information can be shared? A classification of cooperative metaheuristics methods depending on the nature of cooperation involved is presented and the specific properties of each class, as well as a way to combine them, is discussed. Several improvements in the field of metaheuristics are also given. In particular, a method to regulate the use of classical genetic operators and to define new more pertinent ones is proposed, taking advantage of a building block structured representation of the explored space. A hierarchical approach resting on multiple levels of cooperative metaheuristics is finally presented, leading to the definition of a complete concerted cooperation strategy. Some applications of these concepts to difficult proteomics problems, including automatic protein identification, biological motif inference and multiple sequence alignment are presented. For each application, an innovative method based on the cooperation concept is given and compared with classical approaches. In the protein identification problem, a first level of cooperation using swarm intelligence is applied to the comparison of mass spectrometric data with biological sequence database, followed by a genetic programming method to discover an optimal scoring function. The multiple sequence alignment problem is decomposed in three steps involving several evolutionary processes to infer different kind of biological motifs and a concerted cooperation strategy to build the sequence alignment according to their motif content.

An individual-based model, EcoSim, was employed to investigate if specialized resource use could promote sympatric speciation. Prey individuals in the original version of EcoSim were supplied with a single primary food resource. A dual resource version with different food resources (Food 1 and Food 2) was also developed to create favorable conditions for the emergence of specialized food consumption among prey individuals. The single resource version was used as the control to determine the impact of the presence of multiple food resources on the occurrence of sympatric speciation. Each unit of Food 2 contained a higher amount of energy than Food 1, and Food 1 was more accessible than Food 2. Initially, prey individuals mostly fed on Food 1. However, after the emergence of food specialization, the consumption rate of Food 2 significantly exceeded the consumption rate of Food 1; although prey individuals more frequently encountered Food 1. While sympatric speciation was observed in the dual resource version runs, we could not identify any sympatric species in the single resource version runs. Machine learning techniques were also employed to identify the most influential initial conditions leading to sympatric speciation. According to the obtained results, in most lineages sympatric speciation occurred at the beginning of the food specialization process. When the lineage had a high special diversity, the lineage needed two different criteria to diverge sympatrically: possessing high genetic diversity and a large population size. In support of previous findings, this study demonstrated that the most accurate determination of initial conditions leading to sympatric speciation can be obtained from lineages that are at the beginning of the divergence process. In conclusion, this study indicated that divergent foraging behavior could potentially lead to the sympatric emergence of new How to cite this paper: Pour, M.K., Bandehbahman, S., Gras, R. and Cristescu, M.E.

INTRODUCTIONThe molecular scanner offers a flexible and powerful visualization tool that can create a fully annotated 2D gel electrophoresis map. Proteins separated by 2D gel electrophoresis are simultaneously digested while undergoing electrotransfer from the gel to a membrane. The peptides are subjected to peptide mass fingerprint (PMF) analysis to identify proteins directly from the PVDF membranes by MALDI-TOF-MS scanning. An ensemble of dedicated tools is then used to create, analyze, and visualize a proteome as a multidimensional image. The molecular scanner method reduces to a minimum the sample handling prior to mass analysis and decreases the sample size to a few tens of micrometers, that is, the size of the MALDI-TOF-MS laser beam impact. The process can be divided into four parts: separation and digestion of proteins, acquisition of PMF data, processing of the MS data and protein identification, and creation of multidimensional proteome maps.

No abstract

No abstract