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We derive a new model of circadian oscillations in Neurospora crassa, which is suitable to analyze both temporal and spatial dynamics of proteins responsible for mechanism of rythms. The model is based on the non-linear interplay between proteins FRQ and WCC which are products of transcription of frequency and white collar genes forming a feedback loop comprised both positive and negative elements. The main component of oscillations mechanism is supposed to be time-delay in biochemical reactions of transcription. We show that the model accounts for various features observed in Neurospora’s experiments such as entrainment by light cycles, phase shift under light pulse, robustness to action of fluctuations and so on. Wave patterns excited during spatial development of the system are studied. It is shown that the wave of synchronization of biorythms arises under basal transcription factors.

The paper proposes a new mathematical model to study the interaction dynamics of the longitudinal wall of a narrow channel with its end seal. The end seal was considered as the edge wall on a spring, i.e. spring-mass system. These walls interaction occurs via a viscous liquid filling the narrow channel; thus required the formulation and solution of the hydroelasticity problem. However, this problem has not been previously studied. The problem consists of the Navier–Stokes equations, the continuity equation, the edge wall dynamics equation, and the corresponding boundary conditions. Two cases of fluid motion in a narrow channel with parallel walls were studied. In the first case, we assumed the liquid motion as the creeping one, and in the second case as the laminar, taking into account the motion inertia. The hydroelasticty problem solution made it possible to determine the distribution laws of velocities and pressure in the liquid layer, as well as the motion law of the edge wall. It is shown that during creeping flow, the liquid physical properties and the channel geometric dimensions completely determine the damping in the considered oscillatory system. Both the end wall velocity and the longitudinal wall velocity affect the damping properties of the liquid layer. If the fluid motion inertia forces were taken into account, their influence on the edge wall vibrations was revealed, which manifested itself in the form of two added masses in the equation of its motion. The added masses and damping coefficients of the liquid layer due to the joint consideration of the liquid layer inertia and its viscosity were determined. The frequency and phase responses of the edge wall were constructed for the regime of steady-state harmonic oscillations. The simulation showed that taking into account the fluid layer inertia and its damping properties leads to a shift in the resonant frequencies to the low-frequency region and an increase in the oscillation amplitudes of the edge wall.

Predicting novel drug properties is fundamental to polypharmacology, repositioning, and the study of biologically active substances during the preclinical phase. The use of machine learning, including deep learning methods, for the identification of drug – target interactions has gained increasing popularity in recent years.

The objective of this study was to develop a method for recognizing psychotropic effects and drug mechanisms of action (drug – target interactions) based on an analysis of the bioelectrical activity of the brain using artificial intelligence technologies.

Intracranial electroencephalographic (EEG) signals from rats were recorded (4 channels at a sampling frequency of 500 Hz) after the administration of psychotropic drugs (gabapentin, diazepam, carbamazepine, pregabalin, eslicarbazepine, phenazepam, arecoline, pentylenetetrazole, picrotoxin, pilocarpine, chloral hydrate). The signals were divided into 2-second epochs, then converted into $2000\times 4$ images and input into an autoencoder. The output of the bottleneck layer was subjected to classification and clustering using t-SNE, and then the distances between resulting clusters were calculated. As an alternative, an approach based on feature extraction with dimensionality reduction using principal component analysis and kernel support vector machine (kSVM) classification was used. Models were validated using 5-fold cross-validation.

The classification accuracy obtained for 11 drugs during cross-validation was $0.580 \pm 0.021$, which is significantly higher than the accuracy of the random classifier $(0.091 \pm 0.045, p < 0.0001)$ and the kSVM $(0.441 \pm 0.035, p < 0.05)$. t-SNE maps were generated from the bottleneck parameters of intracranial EEG signals. The relative proximity of the signal clusters in the parametric space was assessed.

The present study introduces an original method for biopotential-mediated prediction of effects and mechanism of action (drug – target interaction). This method employs convolutional neural networks in conjunction with a modified selective parameter reduction algorithm. Post-treatment EEGs were compressed into a unified parameter space. Using a neural network classifier and clustering, we were able to recognize the patterns of neuronal response to the administration of various psychotropic drugs.