On the Universality of Axon P Systems Xingyi Zhang; Linqiang Pan; Paun, Andrei
IEEE transaction on neural networks and learning systems,
11/2015, Letnik:
26, Številka:
11
Journal Article
Axon P systems are computing models with a linear structure in the sense that all nodes (i.e., computing units) are arranged one by one along the axon. Such models have a good biological motivation: ...an axon in a nervous system is a complex information processor of impulse signals. Because the structure of axon P systems is linear, the computational power of such systems has been proved to be greatly restricted; in particular, axon P systems are not universal as language generators. It remains open whether axon P systems are universal as number generators. In this paper, we prove that axon P systems are universal as both number generators and function computing devices, and investigate the number of nodes needed to construct a universal axon P system. It is proved that four nodes (respectively, nine nodes) are enough for axon P systems to achieve universality as number generators (respectively, function computing devices). These results illustrate that the simple linear structure is enough for axon P systems to achieve a desired computational power.
Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most ...existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.
Abstract
In nature, allostery is the principal approach for regulating cellular processes and pathways. Inspired by nature, structure-switching aptamer-based nanodevices are widely used in artificial ...biotechnologies. However, the canonical aptamer structures in the nanodevices usually adopt a duplex form, which limits the flexibility and controllability. Here, a new regulating strategy based on a clamp-like triplex aptamer structure (CLTAS) was proposed for switching DNA polymerase activity via conformational changes. It was demonstrated that the polymerase activity could be regulated by either adjusting structure parameters or dynamic reactions including strand displacement or enzymatic digestion. Compared with the duplex aptamer structure, the CLTAS possesses programmability, excellent affinity and high discrimination efficiency. The CLTAS was successfully applied to distinguish single-base mismatches. The strategy expands the application scope of triplex structures and shows potential in biosensing and programmable nanomachines.
In this study, an aptamer-substrate strategy is introduced to control programmable DNA origami pattern. Combined with DNA aptamer-substrate binding and DNAzyme-cutting, small DNA tiles were ...specifically controlled to fill into the predesigned DNA origami frame. Here, a set of DNA logic gates (OR, YES, and AND) are performed in response to the stimuli of adenosine triphosphate (ATP) and cocaine. The experimental results are confirmed by AFM imaging and time-dependent fluorescence changes, demonstrating that the geometric patterns are regulated in a controllable and programmable manner. Our approach provides a new platform for engineering programmable origami nanopatterns and constructing complex DNA nanodevices.
Spiking neural P systems are a class of distributed and parallel computing models inspired by spiking neurons.In this work,the features of neuron division and neuron budding are introduced into the ...framework of spiking neural P systems, which are processes inspired by neural stem cell division. With neuron division and neuron budding, a spiking neural P system can generate exponential work space in polynomial time as the case for P systems with active membranes.In this way,spiking neural P systems can efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with an efficient solution to SAT problem.
Cell signaling governs the basic cellular activities and coordinates the actions in cell. Abnormal regulations in cell signaling processing are responsible for many human diseases, such as diabetes ...and cancers. With the accumulation of massive data related to human cell signaling, it is feasible to obtain a human signaling network. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis to biological networks. In this work, we apply structural controllability to a human signaling network and detect driver nodes, providing a systematic analysis of the role of different proteins in controlling the human signaling network. We find that the proteins in the upstream of the signaling information flow and the low in-degree proteins play a crucial role in controlling the human signaling network. Interestingly, inputting different control signals on the regulators of the cancer-associated genes could cost less than controlling the cancer-associated genes directly in order to control the whole human signaling network in the sense that less drive nodes are needed. This research provides a fresh perspective for controlling the human cell signaling system.
DNA strand displacement technology performs well in sensing and programming DNA segments. In this work, we construct DNA molecular systems based on DNA strand displacement performing computation of ...logic gates. Specifically, a class of so-called "DNA neurons" are achieved, in which a "smart" way inspired by biological neurons encoding information is developed to encode and deliver information using DNA molecules. The "DNA neuron" is bistable, that is, it can sense DNA molecules as input signals, and release "negative" or "positive" signals DNA molecules. We design intelligent DNA molecular systems that are constructed by cascading some particularly organized "DNA neurons", which could perform logic computation, including AND, OR, XOR logic gates, automatically. Both simulation results using visual DSD (DNA strand displacement) software and experimental results are obtained, which shows that the proposed systems can detect DNA signals with high sensitivity and accretion; moreover, the systems can process input signals automatically with complex nonlinear logic. The method proposed in this work may provide a new way to construct a sensitive molecular signal detection system with neurons spiking behavior in vitro, and can be used to develop intelligent molecular processing systems in vivo.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Time-Free Spiking Neural P Systems Pan, Linqiang; Zeng, Xiangxiang; Zhang, Xingyi
Neural computation,
05/2011, Letnik:
23, Številka:
5
Journal Article
Recenzirano
Different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, a realistic definition of nonsynchronized spiking neural ...P systems (SN P systems, for short) is considered: during the work of an SN P system, the execution times of spiking rules cannot be known exactly (i.e., they are arbitrary). In order to establish robust systems against the environmental factors, a special class of SN P systems, called time-free SN P systems, is introduced, which always produce the same computation result independent of the execution times of the rules. The universality of time-free SN P systems is investigated. It is proved that these P systems with extended rules (several spikes can be produced by a rule) are equivalent to register machines. However, if the number of spikes present in the system is bounded, then the power of time-free SN P systems falls, and in this case, a characterization of semilinear sets of natural numbers is obtained.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In this paper we continue previous studies on the computational efficiency of spiking neural P systems, under the assumption that some pre-computed resources of exponential size are given in advance. ...Specifically, we give a deterministic solution for each of two well known
PSPACE-complete problems:
QSAT and
Q3SAT. In the case of
QSAT, the answer to any instance of the problem is computed in a time which is linear with respect to both the number
n
of Boolean variables and the number
m
of clauses that compose the instance. As for
Q3SAT, the answer is computed in a time which is at most cubic in the number
n
of Boolean variables.
Real-world systems interact with one another via dependency connectivities. Dependency connectivities make systems less robust because failures may spread iteratively among systems via dependency ...links. Most previous studies have assumed that two nodes connected by a dependency link are strongly dependent on each other; that is, if one node fails, its dependent partner would also immediately fail. However, in many real scenarios, nodes from different networks may be weakly dependent, and links may fail instead of nodes. How interdependent networks with weak dependency react to link failures remains unknown. In this paper, we build a model of fully interdependent networks with weak dependency and define a parameter α in order to describe the node-coupling strength. If a node fails, its dependent partner has a probability of failing of 1−α. Then, we develop an analytical tool for analyzing the robustness of interdependent networks with weak dependency under link failures, with which we can accurately predict the system robustness when 1−p fractions of links are randomly removed. We find that as the node coupling strength increases, interdependent networks show a discontinuous phase transition when α<αc and a continuous phase transition when α>αc. Compared to site percolation with nodes being attacked, the crossover points αc are larger in the bond percolation with links being attacked. This finding can give us some suggestions for designing and protecting systems in which link failures can happen.
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