Catálogo de publicaciones - libros

Computational models of biochemical systems are usually very large, and moreover, if reaction frequencies of different reaction types differ in orders of magnitude, models possess the mathematical property of stiffness, which renders system analysis difficult and often even impossible with traditional methods. Recently, an accelerated stochastic simulation technique based on a system partitioning, the slow-scale stochastic simulation algorithm, has been applied to the enzyme-catalyzed substrate conversion to circumvent the inefficiency of standard stochastic simulation in the presence of stiffness. We propose a numerical algorithm based on a similar partitioning but without resorting to simulation. The algorithm exploits the connection to continuous-time Markov chains and decomposes the overall problem to significantly smaller subproblems that become tractable. Numerical results show enormous efficiency improvements relative to accelerated stochastic simulation.

The important research objective of identifying genes with similar behavior with respect to different conditions has recently been tackled with biclustering techniques. In this paper we introduce a new approach to the biclustering problem using the Possibilistic Clustering paradigm. The proposed Possibilistic Biclustering algorithm finds one bicluster at a time, assigning a membership to the bicluster for each gene and for each condition. The biclustering problem, in which one would maximize the size of the bicluster and minimizing the residual, is faced as the optimization of a proper functional. We applied the algorithm to the Yeast database, obtaining fast convergence and good quality solutions. We discuss the effects of parameter tuning and the sensitivity of the method to parameter values. Comparisons with other methods from the literature are also presented.