Foundational courses for learning systems biology
A step-by-step guide to learning systems biology for biologists, bioinformaticians, and biomedical data scientists.
Introduction
Systems biology is a powerful interdisciplinary approach in the field of bioinformatics, enabling a deeper understanding of complex biological systems and their role in disease. Systems biology approaches are used to illuminate the unknown principles governing biological systems by integrating diverse data types, such as genomics, proteomics, metabolomics, and clinical data.
One of the key areas where systems biology has made significant contributions is disease modeling. By integrating various biological data, researchers have developed mathematical models that capture the complex dynamics of diseases. These models have provided insights into disease mechanisms, identification of therapeutic targets, and prediction of treatment outcomes.
Courses
In this curated list, I have selected courses that approach the subject matter from a bioinformatician’s perspective, beginning with foundational courses and gradually progressing toward more advanced concepts from a network theory standpoint.
- Network analysis of protein interaction data — https://www.ebi.ac.uk/training/online/courses/network-analysis-of-protein-interaction-data-an-introduction/
- Protein interaction and their importance — https://www.ebi.ac.uk/training/online/courses/protein-interactions-and-their-importance/interaction-databases/different-types-of-molecular-interaction-databases-clone/
- Systems biology — from large datasets to biological insights — https://www.ebi.ac.uk/training/materials/systems-biology-large-datasets-biological-insight-materials/
- Functional genomics — https://www.ebi.ac.uk/training/online/courses/functional-genomics-ii-common-technologies-and-data-analysis-methods/
- First-course network science in Python using networkx — https://github.com/CambridgeUniversityPress/FirstCourseNetworkScience
- An introduction to computational systems biology by Karthik Raman — Home | An Introduction to Computational Systems Biology (ramanlab.github.io)
- Networkx course — https://github.com/glingden/Protein-Protein-interaction-using-Networkx
- Good paper to read — Mathematical Modeling Of Systems Biology Aaditya Prasad Gupta1 — Mathematical Modeling Of Systems Biology (biorxiv.org)
- Link prediction using networkx — https://www.geeksforgeeks.org/link-prediction-predict-edges-in-a-network-using-networkx/
- Systems biology: from large datasets to biological insight — https://www.ebi.ac.uk/training/materials/systems-biology-large-datasets-biological-insight-materials/
Papers
- Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12(1), 56–68.
- This seminal paper introduced the concept of network medicine, which employs network-based approaches to understand the molecular basis of diseases. It highlights the importance of integrating omics data with network analysis to uncover disease mechanisms.
2. Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005). Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721), 523–529.
- This study demonstrated the construction of causal networks using single-cell data, revealing the signaling pathways underlying cell fate decisions. Such network-based approaches have become instrumental in understanding disease progression and identifying potential therapeutic targets.
Blog posts by me
You can also read some of my other articles that will teach you more advanced technologies in the network biology domain.
Advanced Applications
- A virtual Rheumatoid Arthritis fibroblast for large-scale dynamic analysis and modeling
- PerMedCoE Building Blocks and Workflows to streamline biological analysis pipelines
- Data-driven modeling of intercellular interactions in the tumor microenvironment
If you liked my article, keep watching, I plan to write on several topics in data science, problem-solving, cancer medicine, and psychology for success.
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