Search

Metabolic pathway building is an active field of research, necessary to understand and manipulate the metabolism of organisms. There are different approaches, mainly based on classical search methods, to find linear sequences of reactions linking two compounds. However, an important limitation of these methods is the exponential increase of search trees when a large number of compounds and reactions is considered. Besides, such models do not take into account all substrates for each reaction during the search, leading to solutions that lack biological feasibility in many cases. This work proposes a new evolutionary algorithm that allows searching not only linear, but also branched metabolic pathways, formed by feasible reactions that relate multiple compounds simultaneously. Tests performed using several sets of reactions show that this algorithm is able to find feasible linear and branched metabolic pathways. Fil: Gerard, Matias Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina

Searching metabolic pathways that relate two compounds is a common task in bioinformatics. This is of particular interest when trying, for example, to discover metabolic relations among compounds clustered with a data mining technique. Search strategies find sequences to relate two or more states (compounds) using an appropriate set of transitions (reactions). Evolutionary algorithms carry out the search guided by a fitness function and explore multiple candidate solutions using stochastic operators. In this work we propose an evolutionary algorithm for searching metabolic pathways between two compounds. The operators and fitness function employed are described and the effect of mutation rate is studied. Performance of this algorithm is compared with two classical search strategies. Fil: Gerard, Matias Fernando. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina Fil: Stegmayer, Georgina. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Centro de Investigacion y Desarrollo de Ingenieria en Sistemas de Informacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Santa Fe. Instituto de Investigacion en Señales, Sistemas e Inteligencia Computacional; Argentina; Argentina

Magnetic and EPR data have been collected for complex [Cu(L-Arg)2](NO3)2-3H2O (Arg ¼ arginine). Magnetic susceptibility w in the temperature range 2–160 K, and a magnetization isotherm at T ¼ 2.29(1)K with magnetic fields between 0 and 9 T were measured. The observed variation of wT with T indicates predominant antiferromagnetic interactions between Cu(II) ions coupled in 1D chains along the b axis. Fitting a molecular field model to the susceptibility data allows to evaluate g ¼ 2.10(1) for the average g-factor and J ¼ -0.42(6) cm-1 for the nearest neighbor exchange coupling (defined as Hex ¼ -PJijSi- Sj). This coupling is assigned to syn–anti equatorial–apical carboxylate bridges connecting Cu(II) ion neighbors at 5.682A ° , with a total bond length of 6.989A ° and is consistent with the magnetization isotherm results. It is discussed and compared with couplings observed in other compounds with similar exchange bridges. EPR spectra at 9.77 were obtained in powder samples and at 9.77 and at 34.1GHz in the three orthogonal planes of single crystals. At both microwave frequencies, and for all magnetic field orientations a single signal arising from the collapse due to exchange interaction of resonances corresponding to two rotated Cu(II) sites is observed. From the EPR results the molecular g-tensors corresponding to the two copper sites in the unit cell were evaluated, allowing an estimated lower limit |J |40.1 cm-1 for the exchange interaction between Cu(II) neighbors, consistent with the magnetic measurements. Fil: Gerard, Matias Fernando. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Aiassa, C.. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina Fil: Casado, Nieves Maria Claudia. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina Fil: Santana, R. C.. Universidade Federal de Goiás; Brasil Fil: Perec, Mireille. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Inorgánica, Analítica y Química Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Rapp, R. E.. Universidade Federal do Rio de Janeiro; Brasil Fil: Calvo, Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina