The identifiability of gene regulatory networks: the role of observation data.

Identifying gene regulatory networks (GRN) from observation data is significant to understand biological systems. Conventional studies focus on improving the performance of identification algorithms. However, besides algorithm performance, the GRN identification is strongly depended on the observation data. In this work, for three GRN S-system models, three observation data collection schemes are used to perform the identifiability test procedure. A modified genetic algorithm-particle swarm optimization algorithm is proposed to implement this task, including the multi-level mutation operation and velocity limitation strategy. The results show that, in scheme 1 (starting from a special initial condition), the GRN systems are of identifiability using the sufficient transient observation data. In scheme 2, the observation data are short of sufficient system dynamic. The GRN systems are not of identifiability even though the state trajectories can be reproduced. As a special case of scheme 2, i.e., the steady-state observation data, the equilibrium point analysis is given to explain why it is infeasible for GRN identification. In schemes 1 and 2, the observation data are obtained from zero-input GRN systems, which will evolve to the steady state at last. The sufficient transient observation data in scheme 1 can be obtained by changing the experimental conditions. Additionally, the valid observation data can be also obtained by means of adding impulse excitation signal into GRN systems (scheme 3). Consequently, the GRN systems are identifiable using scheme 3. Owing to its universality and simplicity, these results provide a guide for biologists to collect valid observation data for identifying GRNs and to further understand GRN dynamics.

Research on multi-load AGV path planning of weaving workshop based on time priority.

The multi-load AGV (Automatic Guided Vehicle) is a new kind of materials handling equipment used to load cloth automatically in an intelligent weaving workshop. It can transport multiple rolls of cloth and choose the correct, most effective path to improve the transportation efficiency without people engaged in. This paper creates a feasible path topology according to the layout of the workshop and the logistics environment, and uses the Warshall-Floyd algorithm to search for the optimal route between two arbitrary points. The aim of the path planning is to maximize the machine efficiency, which is constrained by environmental limits, load limits and work limits. This paper establishes the mathematical model of the path planning problem using the mixed genetic particle swarm optimization algorithm (GA-PSO) to solve the problem, and the particle iteration mechanism based on the time priority is proposed to make the evolution more directional and accelerate the convergence speed of the algorithm. The effectiveness and practicability of the model and methods are verified by simulation and benefit analysis.