Guest Editors
Fayaz S. M.
Manipal Institute of Technology
Dr. Fayaz is an associate professor in the Department of Biotechnology at the Manipal Institute of Technology, MAHE, Manipal. He holds a PhD in computational biology and a master’s degree in bioinformatics. With more than a decade of academic and research experience, he specializes in computational biology, multi-omics data integration, systems biology, and computational drug and vaccine design. He has secured competitive funding from national agencies such as ICMR and has contributed to high-impact publications, patents, and interdisciplinary research collaborations across biomedical and engineering domains. His research spans neurodegenerative diseases, metabolic disorders, pathogen genomics, and machine-learning-driven biomedical data analysis. An active reviewer and editorial board member, he continues to advance computational approaches for understanding disease mechanisms and developing therapeutic strategies.
Archana Mahadev Rao
Manipal Institute of Technology
Dr. Archana is an assistant professor in the Department of Biotechnology at the Manipal Institute of Technology, MAHE, Manipal. She is a researcher specializing in molecular diagnostics and therapeutics, with a primary focus on infectious diseases such as herpes simplex virus (HSV) infection and aspergillosis. Her work employs advanced molecular tools, including siRNA-mediated gene silencing and in silico platforms for the rational design of small-molecule drugs. These computational predictions are systematically validated through in vitro methodologies. Her integrated approach bridges bioinformatics with experimental biology and aims to develop precise and effective strategies against persistent and drug-resistant infections.
Collection Overview
Recent advances in large-scale biological datasets, multimodal omics profiling, and high-capacity AI models have enabled a new paradigm: AI-native systems biology, in which biological insights emerge from the integration of foundation models, network science, and mechanistic simulation. The AI-native approach moves beyond traditional computational methods by positioning advanced AI frameworks as the primary methodology for modeling and simulating complex biological systems. The goal is to uncover biological mechanisms directly through AI, rather than using AI solely as an auxiliary analysis tool.
This rapidly progressing field is driven by developments in protein/RNA/DNA foundation models; spatial and single-cell multi-omics; knowledge-graph–based biological reasoning; digital twins and mechanistic simulations; and reinforcement-learning and generative biology approaches. A dedicated journal issue will help consolidate these innovations and define a coherent research theme for the community.
This Topical Collection will highlight systems biology methods that integrate cutting-edge computational approaches using deep learning, generative models, and high-dimensional multi-omics data to uncover mechanisms, predict biological and therapeutic interventions, and advance personalized medicine.
Articles
Nuclear Magnetic Resonance Metabolomic Analysis of Spent Human Embryo Culture Media: Method Validation and Technical Considerations
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2026
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1Department of Reproductive Medicine and Gynaecological Endocrinology, University Medical Centre Maribor, 2Faculty of Medicine, University of Ljubljana, 3National Institute of Chemistry, 4EN-FIST Centre of Excellence, 5Faculty of Chemistry and Chemical Technology, University of Ljubljana, 6Department of Reproductive Science, Manipal Academy of Higher Education