On the complexity of string folding

A fold of a finite string S over a given alphabet is an embedding of S in some fixed infinite grid, such as the square or cubic mesh. The score of a fold is the number of pairs of matching string symbols which are embedded at adjacent grid vertices. Folds of strings and sets of strings in two- and three-dimensional meshes are considered, and the corresponding problems of optimizing the score or achieving a given target score are shown to be NP-hard

A functional programming approach to a computational biology problem

Protein Folding is an important open problem in the eld of Computational Biology Due to its com binatorial nature exact polynomial algorithms to solve it could not exist and so approximation algorithms and heuristics has to be used In this paper a new heuristic is studied based on the approach that considers that the folding process is coded into the protein One important aspect of this work is that the algorithm was implemented using functional programming resulting in advantages for the understanding of the problem The results obtained are comparable with the ones obtained for classical algorithms .Eje: Conferencia latinoamericana de programación funcionalRed de Universidades con Carreras en Informática (RedUNCI

A Firefly-inspired method for protein structure prediction in lattice models

We introduce a Firefly-inspired algorithmic approach for protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa–Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function valuations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models

A Firefly-inspired method for protein structure prediction in lattice models

We introduce a Firefly-inspired algorithmic approach for protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa–Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function valuations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models

A functional programming approach to a computational biology problem

Protein Folding is an important open problem in the eld of Computational Biology Due to its com binatorial nature exact polynomial algorithms to solve it could not exist and so approximation algorithms and heuristics has to be used In this paper a new heuristic is studied based on the approach that considers that the folding process is coded into the protein One important aspect of this work is that the algorithm was implemented using functional programming resulting in advantages for the understanding of the problem The results obtained are comparable with the ones obtained for classical algorithms .Eje: Conferencia latinoamericana de programación funcionalRed de Universidades con Carreras en Informática (RedUNCI

Un algoritmo genetico per la predizione della configurazione spaziale del nucleo idrofobico di proteine

Il lavoro di questa tesi, si inserisce in un'area di interesse che interseca la bioinformatica e l'intelligenza artificiale. Nello specifico, si è affrontato il problema del protein folding (ripiegamento proteico) su un modello semplificato della proteina. L' approccio risolutivo in questione è basato sull'algoritmo genetico nella sua definizione più classica, a cui viene integrato, nella fase di selezione della popolazione, il metodo Probabilistic Roadmaps ( PRM). La modellazione della proteina è fatta su un modello minimalista, detto modello HP che studia solamente le interazioni idrofobiche che avvengono nel processo di folding tra gli amminoacidi che compongono la proteina. Questo modello permette, quindi, di individuare il nucleo idrofobico della proteina