SUMMARY: Since few years the problem of finding optimal solutions for drug or vaccine protocols have been tackled using system biology modeling. These approaches are usually computationally expensive. Our previous experiences in optimizing vaccine or drug protocols using genetic algorithms required the use of a high performance computing infrastructure for a couple of days. In the present article we show that by an appropriate use of a different optimization algorithm, the simulated annealing, we have been able to downsize the computational effort by a factor 10(2). The new algorithm requires computational effort that can be achieved by current generation personal computers. AVAILABILITY: Software and additional data can be found at http://www.immunomics.eu/SA/

We study routing for massively dense wireless networks, i.e., wireless networks that contain so many nodes that, in addition to their usual microscopic description, a novel macroscopic description becomes possible. The macroscopic description is not detailed, but nevertheless contains enough information to permit a meaningful study and performance optimization of the network. Within this context, we continue and significantly expand previous work on the analogy between optimal routing and the propagation of light according to the laws of Geometrical Optics. Firstly, we pose the analogy in a more general framework than previously, notably showing how the eikonal equation, which is the central equation of Geometrical Optics, also appears in the networking context. Secondly, we develop a methodology for calculating the cost function, which is the function describing the network at the macroscopic level. We apply this methodology for two important types of networks: bandwidth limited and energy limited.

Recently trajectory based forwarding (TBF) has been proposed for dense wireless multihop networks. However, no strategies have been proposed so far for the minimization of the communication energy cost. In this paper a framework is proposed for the evaluation of the optimal route in wireless multihop networks which exploits analogies with geometrical optics. In the proposed framework a communication cost function is defined which represents the communication cost in each point of the deployment area. Such function has been calculated using the node density function. Accordingly, the proposed framework requires the density of nodes in the network to be known a priori. The proposed framework has been used in a relevant scenario. Finally, several ideas for future research are presented.