This paper presents a heuristic approach combining constraint satisfaction, local search and a constructive optimization algorithm for a large-scale energy management and maintenance scheduling problem. The methodology shows how to successfully combine and orchestrate different types of algorithms and produce competitive results. The local search for production assignment is a simple yet optimal solution for the relaxed initial problem. We also propose an efficient way to scale the method for huge instances. A large part of the presented work is done to compete in the ROADEF/EURO Challenge 2010, organized jointly by the ROADEF, EURO and the Électricité de France. The numerical results obtained for the official competition instances testify about the quality of the approach. The method achieves 3 out of 15 possible best results.

The double cut-and-join (DCJ) operation, or 2-break rearrangement was introduced by Yancopoulos et al. ([1]). Finding DCJ distance between double genomes is NP-hard ([2]). To a pair of regular and all-duplicates genome, having only circular chromosomes, we assign generalized breakpoint graph (introduced by Alekseyev and Pevzner ([3]). We give an algorithm for finding the approximation and exact double DCJ distance. We do this by decomposing GBG into maximum number of alternating, closed trails, using branch-and-bound method.

Motivation: Ancestral genomes provide a better way to understand the structural evolution of genomes than the simple comparison of extant genomes. Most ancestral genome reconstruction methods rely on universal markers, that is, homologous families of DNA segments present in exactly one exemplar in every considered species. Complex histories of genes or other markers, undergoing duplications and losses, are rarely taken into account. It follows that some ancestors are inaccessible by these methods, such as the proto–monocotyledon whose evolution involved massive gene loss following a whole genome duplication. Results: We propose a mapping approach based on the combinatorial notion of ‘sandwich consecutive ones matrix’, which explicitly takes gene losses into account. We introduce combinatorial optimization problems related to this concept, and propose a heuristic solver and a lower bound on the optimal solution. We use these results to propose a configuration for the proto-chromosomes of the monocot ancestor, and study the accuracy of this configuration. We also use our method to reconstruct the ancestral boreoeutherian genomes, which illustrates that the framework we propose is not specific to plant paleogenomics but is adapted to reconstruct any ancestral genome from extant genomes with heterogeneous marker content. Availability: Upon request to the authors. Contact: haris.gavranovic@gmail.com; eric.tannier@inria.fr

Since the availability of assembled eukaryotic genomes, the first one being a budding yeast, many computational methods for the reconstruction of ancestral karyotypes and gene orders have been developed. The difficulty has always been to assess their reliability, since we often miss a good knowledge of the true ancestral genomes to compare their results to, as well as a good knowledge of the evolutionary mechanisms to test them on realistic simulated data. In this study, we propose some measures of reliability of several kinds of methods, and apply them to infer and analyse the architectures of two ancestral yeast genomes, based on the sequence of seven assembled extant ones. The pre-duplication common ancestor of S. cerevisiae and C. glabrata has been inferred manually by Gordon et al. (Plos Genet. 2009). We show why, in this case, a good convergence of the methods is explained by some properties of the data, and why results are reliable. In another study, Jean et al. (J. Comput Biol. 2009) proposed an ancestral architecture of the last common ancestor of S. kluyveri, K. thermotolerans, K. lactis, A. gossypii, and Z. rouxii inferred by a computational method. In this case, we show that the dataset does not seem to contain enough information to infer a reliable architecture, and we construct a higher resolution dataset which gives a good reliability on a new ancestral configuration.

We present theoretical and practical advances on the Guided Genome Halving problem, a combinatorial optimisation problem which aims at proposing ancestral configurations of extant genomes when one of them has undergone a whole genome duplication. We provide a lower bound on the optimal solution, devise a heuristic algorithm based on subgraph identification, and apply it to yeast gene order data. On some instances, the computation of the bound yields a proof that the obtained solutions are optimal. We analyse a set of optimal solutions, compare them with a manually curated standard ancestor, showing that on yeast data, results coming from different methodologies are largely convergent: the optimal solutions are distant of at most one rearrangement from the reference.

Since the availability of assembled eukaryotic genomes, the first one being a budding yeast, many computational methods for the reconstruction of ancestral karyotypes and gene orders have been developed. The difficulty has always been to assess their reliability, since we often miss a good knowledge of the true ancestral genomes to compare their results to, as well as a good knowledge of the evolutionary mechanisms to test them on realistic simulated data. In this study, we propose some measures of reliability of several kinds of methods, and apply them to infer and analyse the architectures of two ancestral yeast genomes, based on the sequence of seven assembled extant ones. The pre-duplication common ancestor of S. cerevisiae and C. glabrata has been inferred manually by Gordon et al. (Plos genet. 2009). We show why, in this case, a good convergence of the methods is explained by some properties of the data, and why results are reliable. In another study, Jean et al. (J. Comp. Biol. 2009) proposed an ancestral architecture of the last common ancestor of S. kluyveri, K. thermotolerans, K. lactis, A. gossypii and Z. rouxii inferred by a computational method. In this case, we show that the dataset does not seem to contain enough information to infer a reliable architecture, and we construct a higher resolution dataset which gives a good reliability on a new ancestral configuration.