Epigenetic mechanisms play a crucial role in regulating gene expression during cellular homeostasis and are closely linked to various diseases, including cardiovascular, metabolic, and inflammatory conditions. Due to the complex biological nature of these processes and the limited availability of data, mathematical modeling of these systems remains a significant challenge. The relationship between the mathematical structure of the genetic code, epigenetic features, normal cellular function, and pathology is still poorly understood.
The aim of this project is to evaluate whether the mathematical properties of the genetic code are associated with variations in genetic traits. Based on this evaluation, the project aims to propose a tool capable of predicting gene regulatory states. The study will utilize standard mathematical methods (mean, variance, entropy) and artificial neural networks, while also developing new algebraic feature extraction algorithms for genetic sequences. The extracted features will be integrated with biological data, such as DNA methylation profiles, gene expression data, cellular pathological features, and experimental metadata, resulting in a unique and comprehensive research.
Project funding:
KTU Research Fund
Project results:
Unlike many existing studies, this project will not only involve in silico modeling but will also include laboratory-based experimental validation using HUVEC (human endothelial) cell cultures in vitro. This approach will enable the assessment of the practical functionality of the developed mathematical models within a biological system. The expected outcomes of the project are to identify novel epigenetic biomarkers related to cellular states and inflammatory processes, thereby laying the groundwork for future applications in personalized medicine.
Period of project implementation: 2025-04-17 - 2026-02-27
Project partners: Lithuanian University of Health Sciences
