Genetic algorithm is one of the possible ways to break the limit of brute-force method in DNA computing. Using the idea of Darwinian evolution, we introduce a genetic DNA computing algorithm to solve the maximal cliqu...Genetic algorithm is one of the possible ways to break the limit of brute-force method in DNA computing. Using the idea of Darwinian evolution, we introduce a genetic DNA computing algorithm to solve the maximal clique prob-lem. All the operations in the algorithm are accessible with todays molecular biotechnology. Our computer simulations show that with this new computing algorithm, it is possible to get a solution from a very small initial data pool, avoiding enumerating all candidate solutions. For randomly generated problems, genetic algorithm can give correct solution within a few cycles at high probability. Although the current speed of a DNA computer is slow compared with silicon computers, our simulation indicates that the number of cycles needed in this genetic algorithm is approximately a linear function of the number of vertices in the network. This may make DNA computers more powerfully attacking some hard computa-tional problems.展开更多
In cells, the interactions of distinct signaling transduction pathways originating from cross-talkings between signaling molecules give rise to the formation of signaling transduction networks, which contributes to th...In cells, the interactions of distinct signaling transduction pathways originating from cross-talkings between signaling molecules give rise to the formation of signaling transduction networks, which contributes to the changes (emergency) of kinetic behaviors of signaling system compared with single molecule or pathway. Depending on the known experimental data, we have constructed a model for complex cellular signaling transduction system, which is derived from signaling transduction of epidermal growth factor receptor in neuron. By the computational simulating methods, the self-adaptive controls of this system have been investigated. We find that this model exhibits a relatively stable selfadaptive system, especially to over-stimulation of agonist, and the amplitude and duration of signaling intermediates in it could be controlled by multiple self-adaptive effects, such as 'signal scattering', 'positive feedback', 'negative feedback' and 'B-Raf shunt'. Our results provide an approach to understanding the dynamic behaviors of complex biological systems.展开更多
文摘Genetic algorithm is one of the possible ways to break the limit of brute-force method in DNA computing. Using the idea of Darwinian evolution, we introduce a genetic DNA computing algorithm to solve the maximal clique prob-lem. All the operations in the algorithm are accessible with todays molecular biotechnology. Our computer simulations show that with this new computing algorithm, it is possible to get a solution from a very small initial data pool, avoiding enumerating all candidate solutions. For randomly generated problems, genetic algorithm can give correct solution within a few cycles at high probability. Although the current speed of a DNA computer is slow compared with silicon computers, our simulation indicates that the number of cycles needed in this genetic algorithm is approximately a linear function of the number of vertices in the network. This may make DNA computers more powerfully attacking some hard computa-tional problems.
基金This research is supported by the National Natural Science Foundation of China (No. 70071040).
文摘In cells, the interactions of distinct signaling transduction pathways originating from cross-talkings between signaling molecules give rise to the formation of signaling transduction networks, which contributes to the changes (emergency) of kinetic behaviors of signaling system compared with single molecule or pathway. Depending on the known experimental data, we have constructed a model for complex cellular signaling transduction system, which is derived from signaling transduction of epidermal growth factor receptor in neuron. By the computational simulating methods, the self-adaptive controls of this system have been investigated. We find that this model exhibits a relatively stable selfadaptive system, especially to over-stimulation of agonist, and the amplitude and duration of signaling intermediates in it could be controlled by multiple self-adaptive effects, such as 'signal scattering', 'positive feedback', 'negative feedback' and 'B-Raf shunt'. Our results provide an approach to understanding the dynamic behaviors of complex biological systems.
