Browsing by Author "Rosa, Agostinho"
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- Comparison of GA and PSO performance in parameter estimation of microbial growth models: a case-study using experimental dataPublication . Calçada, Dulce; Rosa, Agostinho; Duarte, Luís C.; Lopes, Vitor V.In this work we examined the performance of two evolutionary algorithms, a genetic algorithm (GA) and particle swarm optimization (PSO), in the estimation of the parameters of a model for the growth kinetics of the yeast Debaryomyces hansenii. Fitting the model’s predictions simultaneously to three replicates of the same experiment, we used the variability among replicates as a criterion to evaluate the optimization result. The performance of the two algorithms was tested using 12 distinct settings for their operating parameters and running each of them 20 times. For the GA, the crossover fraction, crossover function and magnitude of mutation throughout the run of the algorithm were tested; for the PSO, we tested swarms with 3 different types of convergence behavior - convergent with and without oscillations and divergent - and also varied the relative weights of the local and global acceleration constants. The best objective function values were obtained when the PSO fell in the zone of convergence with oscillations or zigzagging, and had a local acceleration larger than the global acceleration. immunization.
- Simulating antigenic drift and shift in influenza APublication . Fachada, Nuno; Lopes, Vitor V.; Rosa, AgostinhoComputational models of the immune system and pathogenic agents have several applications, such as theory testing and validation, or as a complement to first stages of drug trials. One possible application is the prediction of the lethality of new Influenza A strains, which are constantly created due to antigenic drift and shift. Here, we present an agent-based model of immune-influenza A dynamics, with focus on low level molecular antigen-antibody interactions, in order to study antigenic drift and shift events, and analyze the virulence of emergent strains. At this stage of the investigation, results are presented and discussed from a qualitative point of view against recent and generally recognized immunology and influenza literature.
- Simulation of immune system response to bacterial challengePublication . Fachada, Nuno; Lopes, Vitor V.; Rosa, AgostinhoImmune system (IS) simulations have several applications, such as biological theory testing or as a complement in the development of improved drugs. This paper presents an agent based approach to simulate the IS response to bacterial infection challenge. The agent simulator is implemented in a discrete time and twodimensional space, and composed by two layers: a) a specialized cellular automata responsible for substance di usion and reactions; and b) the layer where agents move, act and interact. The IS model focuses upon low level cellular receptor interactions, receptor diversity and genetic-ruled agents, aiming to observe and study the resultant emergent behavior. The model reproduces the following IS behavioral characteristics: speci city and specialization, immune memory and vaccine immunization.
- Spectrometric differentiation of yeast strains using minimum volume increase and minimum direction change clustering criteriaPublication . Fachada, Nuno; Figueiredo, Mário A.T.; Lopes, Vitor V.; Martins, Rui C.; Rosa, AgostinhoThis paper proposes new clustering criteria for distinguishing Saccharomyces cerevisiae (yeast) strains using their spectrometric signature. These criteria are introduced in an agglomerative hierarchical clustering context, and consist of: (a) minimizing the total volume of clusters, as given by their respective convex hulls; and, (b) minimizing the global variance in cluster directionality. The method is deterministic and produces dendrograms, which are important features for microbiologists. A set of experiments, performed on yeast spectrometric data and on synthetic data, show the new approach outperforms several well-known clustering algorithms, including techniques commonly used for microorganism differentiation.