Rosario Lombardo

Rosario Lombardo,  April 13, 2013
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My Ph.D. Thesis ''Unconventional Computations and Genome Representations'' is the result of a research focused on two main investigation lines. The first research line traces its roots to natural computing while the second goes further and springs into the bioinformatic area of genomic investigations. Both fields have been receiving significantly increasing attention by the research community and in this Thesis they meet under the unconventional methodologies employed in the quest of novel knowledge. The dual nature of the inquiry is cast into the formal structure of the Thesis, organized in two main Parts of original research, coming after the introductory Part I, where two chapters of relevant background literature are briefly introduced. In particular, Chapter 1 starts from the natural computing area to provide an insight through mathematical glasses into the state-of-the-art approaches used in membrane computing and modeling. Specifically, the Metabolic P systems described here are central for the development in Part II of novel unconventional computation models within the general unconventional nature-inspired area of ''membrane computing''. Chapter 2 is meant to provide a general overview of the genomic area and, in particular, of some alignment-free comparison methods and informational indexes that, in this case, constitute just inspiring trends for the original research documented in the Thesis. Part II take up the first two original chapters. The Arithmetical Metabolic P systems are introduced in Chapter 3 where the Author will contribute a fresh method to deterministically compute discretization tables of mathematical functions, using the metabolic extension of the P systems in a proper mathematical setting. Remarkably it is possible to use such metabolic systems to model non-linear functions by using linear regulators. In Chapter 4 a novel membrane structure inspired by nature will be introduced, namely the multi-membranes, which has been finely matched with bio-physically convincing topological manifolds. Multi-membranes smoothly blend with a clear computational model defined upon a deterministic strategy of metabolic processes modeled through the theory of Metabolic P Systems. Multi-membranes result to be expressible with a declarative formalism, namely the pure graphs, allowing to design algorithms by visually drawing channels among membranes. Hence, computable functions can be naturally, and remarkably, described by means of pure geometrical forms, articulated at different distributed levels, without any single textual information. Both chapters are backed by powerful software applications able to keep-up with the developed theory and to support the user both in complex modeling of computed simulations and streamlined interactive visual programming. Part III shifts the attention towards newly-implemented original algorithms and data structures explicitly dealing with the elongation of DNA sequences starting from initial clusters of bases called seeds. In Chapter 5 the distinctive idea of a synthetic elongation process is introduced revealing to be fascinating and at the same time inspiring for many new ideas. Although not all the potential of the approach has been explored, the central result documented in this Thesis is a genome representation algorithm called ''Crescendo'' - and a dual inverse reconstruction algorithm - that exhibits the interesting property of ''noise amplification''. That is, inducing a minimal alteration in the genome, the difference between the Crescendo-representations of the original genome and the altered one is subject to a significant percentage increase. In Chapter 6, novel genome visualization methods are devised based on the Crescendo data structures, suggesting the usefulness in visually analyzing systemic properties of the genomes. In fact when genomes are turned from a plain nucleotide-sequence into complex multidimensional structures, an uncommon global vision can be obtained, providing unanticipated insights over the traditional methods based on sequence alignment and local similarities. Supplementary material and more information are available at


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Research groups

Bioinformatics and Natural Computing
Algorithmic analysis of biological processes.