Feedback circuits are crucial dynamic motifs which occur in many biomolecular regulatory networks. They play a pivotal role in the regulation and control of many important cellular processes such as gene transcription, signal transduction, and metabolism. In this study, we develop a novel computationally efficient method to identify feedback loops embedded in intracellular networks, which uses only time-series experimental data and requires no knowledge of the network structure. In the proposed approach, a non-parametric system identification technique, as well as a spectral factor analysis, is applied to derive a graphical criterion based on non-causal components of the system's impulse response. The appearance of non-causal components in the impulse response sequences arising from stochastic output perturbations is shown to imply the presence of underlying feedback connections within a linear network. In order to extend the approach to nonlinear networks, we linearize the intracellular networks about an equilibrium point, and then choose the magnitude of the output perturbations sufficiently small so that the resulting time-series responses remain close to the chosen equilibrium point. In this way, the impulse response sequences of the linearized system can be used to determine the presence or absence of feedback loops in the corresponding nonlinear network. The proposed method utilizes the time profile data from intracellular perturbation experiments and only requires the perturbability of output nodes. Most importantly, the method does not require any a priori knowledge of the system structure. For these reasons, the proposed approach is very well suited to identifying feedback loops in large-scale biomolecular networks. The effectiveness of the proposed method is illustrated via two examples: a synthetic network model with a negative feedback loop and a nonlinear caspase function model of apoptosis with a positive feedback loop.
According to the unique advantages in image processing combining wavelet and fractal and the different ways of combination, a super-resolution image processing methods are proposed. The methods are characterized by combining the wavelet transform, Wavelet Image Interpolation and FBM Fractal Image interpolation in a certain way to achieve super-resolution image reconstruction. Through processing MAG welding pool images polluted by noises seriously, the results show that: the method proposed in this paper, compared with the method based on wavelet bilinear interpolation, not only effectively raises MAG welding image resolution, but also PSNR of reconstruction images are enhanced 21.1049 dB.
The burning point of coal sample is a crucial physical property of coal, and it is also a key technical parameter for the exploitation, transportation, and application of coal. The national standard for coal sample burning point detection prescribes a strict rising rate of temperature when coal samples being heated. To meet with this engineering requirement, we introduce a scheme of a coal sample burning point detecting instrument, which is based on an AT89C55 MCU. The characteristics, key techniques, and solutions of the temperature control system are introduced with respect to the instrument. We employ a self-tunning Dahlin controller with a Recursive Least Squares with Exponential Forgetting (RLSEF) algorithm to solve the control problems of this time-delay, big inertia, and time-variable plant (coal sample heating furnace). Simulations show that the self-tunning Dahlin controller is able to not only compensate the time-delay and big inertia of the plant and but also adapt to the variations of plant parameters, so that the requirement for the rising rate of temperature can be fulfilled effectively.
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