Aditi Arvi,
- Student, Faculty of Biotechnology, University of Allahabad, Allahabad, Uttar Pradseh, India
Abstract
Biological systems modeling has emerged as a promising tool for understanding and predicting the effects of drugs in the treatment of chronic diseases. Chronic diseases, such as diabetes, cardiovascular diseases, and neurodegenerative disorders pose significant challenges to traditional drug development due to their complex, multifactorial nature. Systems biology approaches, which integrate computational modeling with experimental data, provide a holistic view of disease mechanisms and treatment responses. This review explores recent advances in biological systems modeling, focusing on its application to drug development for chronic diseases. We discuss various modeling techniques, including mechanistic models, network-based models, and machine-learning approaches, and highlight their potential to predict drug efficacy, identify biomarkers, and optimize therapeutic strategies. Furthermore, we explore the challenges of integrating heterogeneous data from genomics, proteomics, and clinical trials, as well as the opportunities for personalized medicine. The review emphasizes the need for more refined models that can capture disease progression and drug interactions over time. In conclusion, biological systems modeling holds great promise for enhancing the efficiency and success of drug development for chronic diseases, paving the way for more effective and personalized therapeutic interventions.
Keywords: Biological systems, machine learning, chronic diseases, proteomics, genomics
[This article belongs to Research and Reviews : Journal of Computational Biology ]
Aditi Arvi. Advances in Biological Systems Modeling for Predicting Drug Effects in Chronic Disease. Research and Reviews : Journal of Computational Biology. 2025; 14(01):17-22.
Aditi Arvi. Advances in Biological Systems Modeling for Predicting Drug Effects in Chronic Disease. Research and Reviews : Journal of Computational Biology. 2025; 14(01):17-22. Available from: https://journals.stmjournals.com/rrjocb/article=2025/view=194691
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Research and Reviews : Journal of Computational Biology
| Volume | 14 |
| Issue | 01 |
| Received | 23/12/2024 |
| Accepted | 11/01/2025 |
| Published | 20/01/2025 |
| Publication Time | 28 Days |
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