International Journal of Modern Physics C

Several computational methods have been developed to solve the problem of protein thermostabilization. One common drawback of them is that they must have the information of a backbone structure of a protein for the generation of a proper amino acid sequence. In this paper, we propose a new method called TargetStar by incorporating computational biology and statistical physics, in which an approximate partition function and a specific heat are used to calculate the folding transition temperature of a protein and then to predict the relative thermal stabilities for given proteins based only on their amino acid sequences. To evaluate the prediction accuracy of TargetStar, we calculated folding transition temperatures of 289 orthologous protein pairs using the proposed method, where each protein pair contains one hyperthermophilic protein and one mesophilic protein. According to our evaluation, hyperthermophilic and mesophilic proteins are distinguished from each other in terms of relative thermal stabilities with 77% prediction accuracy. Thus, TargetStar may serve as an efficient method to design an amino acid sequence of a target protein with the desired thermal stability prior to the expensive and time-consuming mutagenesis experiment.

We study a large social network consisting of 3 × 10⁴ individuals, who interact in a large virtual world of Massive Multiplayer Online Role Playing Games (MMORPGs). On the basis of the players' list of friends and the playing time received from the on-line game server, we investigate the structure of the network and human dynamics. We try to show that the structure of this friendship network is very similar to the structure of different social networks. In order to investigate the relation between networks of acquaintances in virtual and real worlds, we carried out a survey among the players. We found very interesting scaling laws concerning human dynamics. Our research has shown how long people are interested in a single task, and how much time they devote to it. It is surprising that exponent values in both cases are close to minus one.

As networks and their structure have become a major field of research, a strong demand for network visualization has emerged. We address this challenge by formalizing the well established spring layout in terms of dynamic equations. We thus open up the design space for new algorithms. Drawing from the knowledge of systems design, we derive a layout algorithm that remedies several drawbacks of the original spring layout. This new algorithm relies on the balancing of two antagonistic forces. We thus call it arf for "attractive and repulsive forces". It is, as we claim, particularly suited for a dynamic layout of smaller networks (n < 10³). We back this claim with several application examples from ongoing complex systems research.

In this paper the existence of quantum wave-packets in neural structures, such as automata and associative memories, is studied. The synaptic weights are considered to be stochastic variables, with probability density functions given by the solution of Schrödinger's equation. It is shown that the weights' update can be performed with the use of unitary operators, as indicated by the postulates of quantum mechanics. Moreover, it is proved that the number of attractors of quantum associative memories increases exponentially with respect to conventional associative memories. Finally, simulation tests are used to demonstrate the improved pattern storage capabilities of quantum associative memories.

Utilizing an appropriate ansatz to the wave function, we reproduce the exact bound-state solutions of the radial Schrödinger equation to various exactly solvable sextic anharmonic oscillator and confining perturbed Coulomb models in D-dimensions. We show that the perturbed Coulomb problem with eigenvalue E can be transformed to a sextic anharmonic oscillator problem with eigenvalue . We also check the explicit relevance of these two related problems in higher-space dimensions. It is shown that exact solutions of these potentials exist when their coupling parameters with k = D +2ℓ appearing in the wave equation satisfy certain constraints.

Around us, the society structure and external field, such as government policy, the newspaper, the internet and other mass media, play a special role in shaping the attitudes, beliefs and public opinion. For studying the role of the society structure and the external field, we propose a new opinion model based on the former models. With computer simulations of opinion dynamics, we find that the smaller the clustering coefficient and the society size, the easier the consensus phase is reached and other interesting results.

In this paper we provide numerical evidence of the richer behavior of the connectivity degrees in heterogeneous preferential attachment networks in comparison to their homogeneous counterparts. We analyze the degree distribution in the threshold model, a preferential attachment model where the affinity between node states biases the attachment probabilities of links. We show that the degree densities exhibit a power-law multiscaling which points to a signature of heterogeneity in preferential attachment networks. This translates into a power-law scaling in the degree distribution, whose exponent depends on the specific form of heterogeneity in the attachment mechanism.

Here, we apply multi team concept to the prey-predator model. The prey teams help each other. Local stability of the system is studied. Global stability and persistence of the model without help are investigated.

We present a simple model of a population of individuals characterized by their genetic structure in the form of a double string of bits and the phenotype following from it. The population is living in an unchanging habitat preferring a certain type of phenotype (optimum). Individuals are unisex, however a pair is necessary for breeding. An individual rejects a mate if the latter's phenotype contains too many bad, i.e. different from the optimum, genes in the same places as the individual's. We show that such strategy, analogous to disassortative mating based on the major histocompatibility complex, avoiding inbreeding and incest, could be beneficial for the population and could reduce considerably the extinction risk, especially in small populations.

The classical cellular automata model has been modified by introducing "shadow" states or perturbations to represent the application of external signals to a configuration of discrete individual units. Simple update rules are studied to determine which of them are best suited to produce the emergent computation which can simulate signal transmission within a system. The study is made with a view to understanding electrical signalling in plant cells which are known to determine plant responses to the environment.

Time evolution of the cities and languages is considered in terms of multiplicative noise¹ and fragmentation² processes; where power law (Pareto-Zipf law)³ and slightly asymmetric log-normal (Gauss)⁴ distribution result for the size distribution of the cities and for that of the languages, respectively. The cities and the languages are treated differently (and as connected; for example, the languages split in terms of splitting the cities, etc.) and thus two distributions are obtained in the same computation at the same time. Evolutions of lifetimes and families for the cities and the languages are also studied. We suggest that the regularities may be evolving out of randomness, in terms of the relevant processes.

We analyze a model of interacting agents (e.g. prebiotic chemical species) which are represented by nodes of a network, whereas their interactions are mapped onto directed links between these nodes. On a fast time scale, each agent follows an eigendynamics based on catalytic support from other nodes, whereas on a much slower time scale the network evolves through selection and mutation of its nodes-agent. In the first part of the paper, we explain the dynamics of the model by means of characteristic snapshots of the network evolution and confirm earlier findings on crashes and recoveries in the network structure. In the second part, we focus on the aggregate behavior of the network dynamics. We show that the disruptions in the network structure are smoothed out, so that the average evolution can be described by a growth regime followed by a saturation regime, without an initial random regime. For the saturation regime, we obtain a logarithmic scaling between the average connectivity per node 〈l〉_s and a parameter m, describing the average incoming connectivity, which is independent of the system size N.