ADVANCED COMPUTATIONAL MODELING OF REGULATORY NETWORKS IN COMPLEX BIOLOGICAL SYSTEMS

Abstract

Cellular behavior and disease mechanisms can only be deciphered by understanding the complexity of the biological regulatory networks. These networks, including gene regulatory interactions, protein protein associations, and RNA mediated control have very nonlinear, dynamic, and multi-layered properties that pose a challenge to experimental procedures. This paper introduces a state-of-the-art computational design of regulatory networks modeling in the complex biological systems via a combination of graph-theoretic descriptions, dynamical networks models, probabilistic inferences, and machine learning models. Multi-omics data sets (transcriptomic and proteomic profiles) of high throughput are used to construct and analyse networks, and extensive preprocessing and state feature selection is applied to overcome noise and high dimensionality. Correlation-based, information-theoretic, and regression-based approaches are used to perform network inference, and regulatory dynamics are simulated using the models of differentiable equations and graph neural networks. The presented framework is tested against benchmark datasets and measured against the usual performance measures, which proves the better quality and strength of the framework in the representation of biologically significant interactions. An example of disease-related regulatory network sheds light on the power of the model to determine important regulatory nodes as well as therapeutic opportunities. The combination of artificial intelligence and systems biology is also a potent paradigm of comprehending the complex biological processes despite the difficulties associated with the heterogeneity, scalability, and interpretability of the data. This article highlights how computational modeling can further research precision medicine, biomarker discovery, and next-generation biomedical research.