Programmable matter is a material that can change its physical properties at will, whether it is its shape, density or conductivity. It can be implemented as an ensemble of micro-robots arranged in space to form a specific shape and having their own computing power. This technology behaves as a distributed system. Each micro-robot is called a module and the whole forms a modular robot. This paper tackles the self-reconfiguration problem by presenting a deterministic planning algorithm that can decide which positions can be filled over multiple iterations using virtual forces. The proposed algorithm implements the Hungarian method to optimize the planning by minimizing the total number of movements of the robots and preventing positions from being blocked. Each module embeds the same algorithm and coordinates with the others using neighbor-to-neighbor communications. Simulation results are conducted to show the effectiveness of the proposed approach.

MOTIVATION: Proteomics has particularly evolved to become of high interest for the field of biomarker discovery and drug development. Especially the combination of liquid chromatography and mass spectrometry (LC/MS) has proven to be a powerful technique for analyzing protein mixtures. Clinically orientated proteomic studies will have to compare hundreds of LC/MS runs at a time. In order to compare different runs, sophisticated preprocessing steps have to be performed. An important step is the retention time (rt) alignment of LC/MS runs. Especially non-linear shifts in the rt between pairs of LC/MS runs make this a crucial and non-trivial problem. RESULTS: For the purpose of demonstrating the particular importance of correcting non-linear rt shifts, we evaluate and compare different alignment algorithms. We present and analyze two versions of a new algorithm that is based on regression techniques, once assuming and estimating only linear shifts and once also allowing for the estimation of non-linear shifts. As an example for another type of alignment method we use an established alignment algorithm based on shifting vectors that we adapted to allow for correcting non-linear shifts also. In a simulation study, we show that rt alignment procedures that can estimate non-linear shifts yield clearly better alignments. This is even true under mild non-linear deviations. AVAILABILITY: R code for the regression-based alignment methods and simulated datasets are available at http://www.statistik.tu-dortmund.de/genetik-publikationen-alignment.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.