Membrane algorithms are a class of distributed and parallel algorithms inspired by the structure and behavior of living cells. Many attractive features of living cells have already been abstracted as operators to improve the performance of algorithms. In this work, inspired by the function of biological neuron cells storing information, we consider a memory mechanism by introducing memory modules into a membrane algorithm. The framework of the algorithm consists of two kinds of modules (computation modules and memory modules), both of which are arranged in a ring neighborhood topology. They can store and process information, and exchange information with each other. We test our method on a knapsack problem to demonstrate its feasibility and effectiveness. During the process of approaching the optimum solution, feasible solutions are evolved by rewriting rules in each module, and the information transfers according to directions defined by communication rules. Simulation results showed that the performance of membrane algorithms with memory cells is superior to that of algorithms without memory cells for solving a knapsack problem. Furthermore, the memory mechanism can prevent premature convergence and increase the possibility of finding a global solution.
The flowering time of Arabidopsis is sensitive to climate variability, with lighting conditions being a major determinant of the flowering time. Long-days induce early flowering, while short-days induce late flowering or even no flowers. This study investigates the intrinsic mechanisms for Arabidopsis flowering in different lighting conditions using mutual information networks and logic networks. The structure parameters of the mutual information networks show that the average degree and the average core clearly distinguish these networks. A method is then given to find the key structural genes in the mutual information networks and the logic networks respectively. Ten genes are found to possibly promote flowering with three genes that may restrain flowering. The sensitivity of this method to find the genes that promote flowering is 80%, while the sensitivity of the method to find the genes that restrain flowering is 100%.
Spiking neural （SN） P systems are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes. In this work, we investigate reversibility in SN P systems, as well as the computing power of reversible SN P systems. Reversible SN P systems are proved to have Turing creativity, that is, they can compute any recursively enumerable set of non-negative integers by simulating universal reversible register machine.
