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Therapeutic agents acting on RNA, including RNA modification, RNA editing and RNA interference (RNAi), play a vital role in gene function study, signaling pathway, drug discovery, disease treatment, vaccine development and so on. Therein, RNAi as an emerging gene therapy has been widely applied in many cancer studies by silencing oncogenes or specific mRNA in malignant tumor cells. Mechanism and efficiency of RNAi are the key issues of RNAi technology. RNA silencing involves dynamic modeling, analyses and optimal control of RNAi gene system. Physiological delay and Hill response describing off-target are considerable elements involving RNAi efficiency. In this paper, we first formulate a four-dimensional RNAi model with time delays and Hill functions, and then investigate the complex dynamic behaviors including the number, existence and stability of internal equilibria and Hopf bifurcations of single delay and two delays. Furthermore, based on the specific mRNA degradation adopted in impulsive patterns, we build an optimal problem by adding exogenous dsRNA at alterable time points in variable dosages in a treatment session. By the method of gradient formula, we can find the optimal impulsive time and proportion of dsRNA. Finally, simulation indicates that (1) physiological lags not only raise the oscillations of mRNA but also cut down the levels of cost; (2) smaller delays and larger rates of siRNA–mRNA complex formation and dsRNA synthesis imply the rapid composition of RISC and fast synthesis of dsRNA leading to more desirable therapeutic schedule, which affords evidence for gene regulation and RNAi; (3) a larger half-saturation coefficient characterizes a unique and stable higher targeted mRNA, whereas a smaller half-saturation coefficient generates bistability in which the higher and lower targeted mRNAs simultaneously emerge; and (4) the bistability will provide a good guidance to control, suppress and degrade targeted mRNA. [ABSTRACT FROM AUTHOR]

Identifying proteins is crucial for disease diagnosis and treatment. With the increase of known proteins, large-scale batch predictions are essential. However, traditional biological experiments being time-consuming and expensive are difficult to accomplish this task efficiently. Nevertheless, deep learning algorithms based on big data analysis have manifested potential in this aspect. In recent years, language representation models, especially BERT, have made significant advancements in natural language processing. In this paper, using three protein segmentation methods and three encoder numbers, nine BERT models with different sizes are constructed to predict whether known proteins are DNA-binding proteins or not. Furthermore, based on the concept of protein motifs, multi-scale convolutional networks are fused into the models to extract the local features of DNA-binding proteins. Finally, we find that the larger the number of encoders, the better the model predictions under the condition of considering each amino acid in the protein as a word. Our proposed algorithm achieves 81.88% sensitivity and 0.39 MCC value on the test set. Furthermore, it achieves 62.41% accuracy on the independent test set PDB2272. It is evident that our proposed method can be a tool to assist in the identification of DNA-binding proteins. [ABSTRACT FROM AUTHOR]