Journal of Systems Science & Complexity最新文献

Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.

This paper studies the problem of principal-agent with moral hazard in continuous time. The firm's cash flow is described by geometric Brownian motion (hereafter GBM). The agent affects the drift of the firm's cash flow by her hidden effort. Meanwhile, the firm rewards the agent with corresponding compensation and equity which depend on the output. The model extends dynamic optimal contract theory to an inflation environment. Firstly, the authors obtain the dynamic equation of the firm's real cash flow under inflation by using the Itô formula. Then, the authors use the martingale representation theorem to obtain agent's continuation value process. Moreover, the authors derive the Hamilton-Jacobi-Bellman (HJB) equation of investor's value process, from which the authors derive the investors' scaled value function by solving the second-order ordinary differential equation. Comparing with He^[1], the authors find that inflation risk affects the agent's optimal compensation depending on the firm's position in the market.

This paper proposes various stages of the hepatitis B virus (HBV) besides its transmissibility and nonlinear incidence rate to develop an epidemic model. The authors plan the model, and then prove some basic results for the well-posedness in term of boundedness and positivity. Moreover, the authors find the threshold parameter R ₀, called the basic/effective reproductive number and carry out local sensitive analysis. Furthermore, the authors examine stability and hence condition for stability in terms of R ₀. By using sensitivity analysis, the authors formulate a control problem in order to eradicate HBV from the population and proved that the control problem actually exists. The complete characterization of the optimum system was achieved by using the 4^th-order Runge-Kutta procedure.

Over the last two decades, pigs have become animal biomodels widely used for the investigation and practice of surgical techniques because of their great physiological and anatomical similarities to humans. Even though many of these studies must be carried out later in humans, the description of basic information is limited, making exact repetitions of the reported experimental methods impossible. In this review, 108 studies from 2013 to 2018 were considered to determine the quality of adherence to the ARRIVE guidelines in the reports of the methodologies. The majority of the studies lacked the details recommended in the ARRIVE guidelines regarding data directly related to the welfare of animals undergoing surgery and those about anesthetic protocols and analgesics. Information related to sample size calculation and housing and husbandry conditions was also very limited. We believe that the ARRIVE guidelines are an excellent tool for good-quality reporting. We encourage scientists to consistently use them as a tool to improve the quality of their scientific reports and, consequently, ensure animal welfare.

In this paper, epidemic spread with the staged progression model on homogeneous and heterogeneous networks is studied. First, the epidemic threshold of the simple staged progression model is given. Then the staged progression model with birth and death is also considered. The case where infectivity is a nonlinear function of the nodes' degree is discussed, too. Finally, the analytical results are verified by numerical simulations.

Langevin equation is widely used to study the stochastic effects in molecular networks, as it often approximates well the underlying chemical master equation. However, frequently it is not clear when such an approximation is applicable and when it breaks down. This paper studies the simple Schnakenberg model consisting of three reversible reactions and two molecular species whose concentrations vary. To reduce the residual errors from the conventional formulation of the Langevin equation, the authors propose to explicitly model the effective coupling between macroscopic concentrations of different molecular species. The results show that this formulation is effective in correcting residual errors from the original uncoupled Langevin equation and can approximate the underlying chemical master equation very accurately.