Using entropy-driven amplifier circuit response to build nonlinear model under the influence of Lévy jump

Abstract

Background Bioinformatics is a subject produced by the combination of life science and computer science. It mainly uses computer technology to study the laws of biological systems. The design and realization of DNA circuit reaction is one of the important contents of bioinformatics. Results In this paper, nonlinear dynamic system model with Lévy jump based on entropy-driven amplifier (EDA) circuit response is studied. Firstly, nonlinear biochemical reaction system model is established based on EDA circuit response. Considering the influence of disturbance factors on the system, nonlinear biochemical reaction system with Lévy jump is built. Secondly, in order to prove that the constructed system conforms to the actual meaning, the existence and uniqueness of the system solution is analyzed. Next, the sufficient conditions for the end and continuation of EDA circuit reaction are certified. Finally, the correctness of the theoretical results is proved by numerical simulation, and the reactivity of THTSignal in EDA circuit under different noise intensity is verified. Conclusions In EDA circuit reaction, the intensity of external noise has a significant impact on the system. The end of EDA circuit reaction is closely related to the intensity of Lévy noise, and Lévy jump has a significant impact on the nature of biochemical reaction system.

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

“…By manipulating the connections between the input and hidden layers, the algorithm generates novel variables for each neuron, which are subsequently transmitted to the output layer. The output layer then predicts outcomes based on the provided input data ( Fu, Lv & Zhang, 2022 ). The efficacy of the backpropagation algorithm is contingent upon the network architecture and the types of incorporated layers ( Kuninti & Rooban, 2021 ).…”

On the stability of Lévy driven stochastic jump systems. towards an unified approach for modeling and stabilizing impulsive and switched systems

“…, then (by Definition 3.9) η is an IDL of X if η = w-lim t→∞ T0,t ρ 0,c * ρ t Since ρ 0,c and ρ t are both Gaussian (using (12) and applying Lemma 3.15) we can write the characteristic function of T0,t ρ 0,j * ρ t as…”

“…Proof. Proceeding like in Theorem 4.13, taking ρ t = L ( ĨG t ), η is an IDL if η = w-lim t→∞ T0,t ρ 0,j * ρ t Using (12) and applying Lemma 3.15 we can write the characteristic function of T0,t ρ 0,j * ρ t as ϕ T0,tρ 0,j • ϕ ρt = exp ψ ρ 0,j ( T * 0,t ξ) exp t 0 (ψ j ( G * s ξ))ds .…”

“…Nevertheless, such systems can effectively be described based on their statistical properties. Examples of such applications include: biological phenomenon [42,12], queuing systems [36], financial/economic market dynamics [13,14]. Interestingly, also for the modeling and analysis of reinforcement learning algorithms [44], where the online learning of the value function can experience switching and impulsive dynamics.…”