COMPUTER RECOGNITION OF CHEMICAL SUBSTANCES BASED ON THEIR ELECTROPHYSIOLOGICAL CHARACTERISTICS

Bioinformation Systems with Detectors and Signal Coding Capabilities

Introduction. The integration of computer technologies into various fields of science allows the development of new methodologies, hybrid information systems with advanced capabilities, such as EcoIS bioinformation system for monitoring the environment with the use of biological data detectors.Problem Statement. The development of innovation bioinformation systems with biological data detectors is a very important task, as they have numerous advantages: allow rapid diagnostics and testing of chemicals in thefirst moments of their action, may be incorporated easily into electronic registration systems, may serve as elementary analytical units with data coding capabilities, etc.Purpose. The purpose of this research is to make a comprehensive analysis of different types of biological data detectors to develop a physical model of a biosensor capable of encoding signals and a bioinformation system with such detectors.Materials and Methods. The comparative analysis of information systems with functions of ecomonitoring and different types of biosensors have been used; the data are taken from electrophysiological experiments on registration of chemosensitive transmembrane electric currents in voltage clamp and patch clamp modes.Results. The physical model of biosensor has been developed and tested. The integration of the developed biosensors into the electronic bioinformation system by the example of EcoIS authors’ system has been demonstrated. Neuron-like biosensor has been considered an abstraction in the unity of its functions: signal receiver — filter — analyzer — encoder/decoder, where the input information is obtained in the form of chemical structures or electrical signals, after the conversion (recoding) of information it is registered as electrical signals with changed characteristics. The prospects for developing the cutting-edge methods for information protection in systems with biosensors have been shown. Conclusions. This development may be used for creating a bioinformation system for environmental moni toring with integrated biosensor system and data protection based on the principles and achievements of contemporary biophysics.