I. Ispolatov, S. Maslov. Detection of the dominant direction of information flow in densely interconnected regulatory networks. This goal is achieved by identifying and removing a (hopefully) small number of links that  close the  feedback loops in the original network and hierarchically arranging the nodes in the remaining network. In the mathematical language this corresponds to a problem of making a graph acyclic by removing as few links as possible and thus altering the original graph in the least possible way.  As a byproduct the algorithm can be used to estimate the optimal number of hierarchical levels and the distribution of nodes over these hierarchical levels in biological (and other complex) networks. This allows to better understand the dynamics of processes taking place on the network. It is also practically important e.g. in network visualization tasks.  You can download our Matlab (hierarchy_levels.m) and Fortran90 (cycle_enumer.f90 and pathway_layout.f90) programs implementing this algorithm. 

S. Maslov and K. Sneppen. Specificity and Stability in Topology of Protein Networks. The set of Matlab algorithms implementing the degree-preserving random rewiring of a network and calculating its correlation profile (correlations between degrees of connected nodes e.g. hubs avoiding other hubs).

Java-applets visualizing the Bak-Tang-Wiesenfeld (BTW) sandpile model of Self-Organized Criticality. One of these applets simulates the BTW model on narrow quasi-1D stripes which was the subject of my paper with Chao Tang and Yi-Cheng Zhang.