Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes.

We describe preliminary results from a multiobjective graph matching algorithm, in the coarsening step of an aggregation-based Algebraic MultiGrid (AMG) preconditioner, for solving large and sparse linear systems of equations on highend parallel computers. We have two objectives. First, we wish to improve the convergence behavior of the AMG method when applied to highly anisotropic problems. Second, we wish to extend the parallel package PSCToolkit to exploit multi-threaded parallelism at the node level on multi-core processors.

Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes and experiments. This "biclustering "paradigm aims at discovering clusters that consist of a subset of the genes showing a coherent expression pattern over a subset of conditions. The pClustering approach is a technique that belongs to this paradigm. Despite many theoretical advantages, this technique has been rarely applied to actual gene expression data analysis.

Mathematical models based on non-linear integral and integro-differential equations are gaining increasing attention in mathematical epidemiology due to their ability to incorporate the past infection dynamic into its current development. This property is particularly suitable to represent the evolution of diseases where the dependence of infectivity on the time since becoming infected plays a crucial role.