Gray code representation of the universal genetic code: Generation of never born protein sequences using Toeplitz matrix approach

Genetic code, the problem of coding biological cycles, and cyclic Gray codes

This article is devoted to the problem of genetically coding of inherited cyclic structures in biological bodies, whose life activity is based on a great inherited set of mutually coordinated cyclic processes. The author puts forward and arguments the idea that the genetic coding system is capable of encoding inherited cyclic processes because it itself is a system of cyclic codes connected with Boolean algebra of logic. In other words, the physiological processes in question are cyclical because they are genetically encoded by cyclic codes. In support of this idea, the author presents a set of his results on the connection of the genetic coding system with cyclic Gray codes, which are one of many known types of cyclic codes. This opens up the possibility of using for modeling inherited cyclic biostructures those algebraic and logical theories and constructions that are associated with Gray codes and have long been used in engineering technologies: Karnaugh maps, Hilbert curve, Hadamard matrices, Walsh functions, dyadic analysis, etc. The author believes that when studying the origin, evolution and function of the genetic code, it is necessary to take into account the ability of the genetic system to encode many mutually related cyclic processes.

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning

BACKGROUND

The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets.

RESULTS

This study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model's predicted results.

CONCLUSIONS

Our method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub ( https://github.com/pangweixiong/DrugGene ).

Relational model of the standard genetic code

The genetic code is a set of rules that establishes mapping between triplets in messenger RNA and amino acids in proteins. The most common way to display these rules is the Standard Genetic Code (SGC) table. This paper takes an alternative approach, based on the relational data model by Edgar F. Codd (Commun. ACM, 13:377-387, 1970). The relational model (RM) proposes a distributed storage of data into a collection of tables (called relations), that can be connected by shared communality. Basic elements of the table are rows (called records or tuples), and columns (called fields or attributes). The SGC table, according to the relational data model, represents the so called unnormalized form of a table. Using normalization rules it is possible to subdivide the SGC table into four tables. The rows and columns of single tables are defined by the first and second base and individual tables by the third codon base. The result of this model is an approach to managing genetic code data, represented in terms of tuples and grouped into relations, with table structure and language consistent with first-order (predicate) logic. The RM explains that the final step in the development of the SGC was the adoption of coding function by the third base, which makes an informational/functional unit with the first base, despite the different physical location in a triplet. This enabled the synthesis of specific proteins without ambiguity, in accordance with the concept of ambiguity reduction and five phases of the general model on the origin of biological codes by Marcello Barbieri (BioSystems 181:11-19, 2019).