Browsing by Author "Fachada, Nuno"
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- 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.