MODERN APPROACHES TO AUTOMATED DESIGN OF BIOTECHNOLOGICAL SYSTEMS

Abstract

This article presents a comprehensive review of current approaches to the automated design of biotechnological systems, with a particular focus on the role of mathematical modeling as a core tool of systems bioengineering. In the context of increasing complexity of biological entities and processes, the growth of multi-omics data, and the need to accelerate the development of novel biotechnological products, the integration of computational technologies into every stage of the biosystem life cycle is becoming critically important. The paper highlights key strategies that shape the emerging digital paradigm in biotechnology, including whole-cell modeling, the iterative Design-Build-Test-Learn (DBTL) cycle, deterministic and stochastic modeling, and Flux Balance Analysis (FBA). Special attention is paid to modern software platforms that enable practical implementation of these strategies—COPASI, CellDesigner, MATLAB SimBiology, Virtual Cell, Tellurium, among others. The article reviews recent trends such as the integration of machine learning methods into design optimization and predictive modeling, as well as the adoption of international biological modeling standards (SBML, CellML) that ensure model interoperability and reproducibility. Challenges such as the availability and quality of input data, the complexity of integrating diverse model types, and the need for interdisciplinary expertise and infrastructure support are also addressed. This review aims to summarize the current state of knowledge in the field of digital bioengineering and to outline the prospects for implementing these approaches in the Ukrainian scientific landscape, particularly in education, applied research, and industrial biotechnology development.