Can Bioinformatics Be Considered as an Experimental Biological Science?

The objective of this short report is to reconsider the subject of bioinformatics as just being a tool of experimental biological science. To do that, we introduce three examples to show how bioinformatics could be considered as an experimental science. These examples show how the development of theoretical biological models generates experimentally verifiable computer hypotheses, which necessarily must be validated by experiments in vitro or in vivo.

💡 Research Summary

The paper challenges the conventional view that bioinformatics is merely a supportive tool for experimental biology and argues that it can be regarded as an independent experimental science. It begins by outlining the classic definition of experimental science—hypothesis formulation, prediction, empirical testing, and validation—and juxtaposes this with a modern perspective that emphasizes data-driven modeling and simulation. To substantiate its claim, the authors present three detailed case studies that illustrate a complete “hypothesis‑prediction‑validation” cycle mediated by computational methods. In the first example, comparative genomics is used to identify evolutionarily conserved sequence motifs. Advanced multiple‑sequence alignment and phylogenetic models generate testable predictions about functional importance, which are then verified through site‑directed mutagenesis and in‑vitro functional assays. The second case involves transcriptomic network analysis: large‑scale RNA‑Seq data are processed to uncover co‑expression modules, and network centrality metrics pinpoint key regulatory genes. These predictions are experimentally validated using CRISPR‑Cas9 gene editing, demonstrating that the computationally identified regulators indeed drive the expected phenotypic changes. The third example integrates deep‑learning protein structure prediction (e.g., AlphaFold) with virtual ligand screening to forecast drug‑binding pockets. Subsequent biochemical binding assays and enzymatic activity measurements confirm that the predicted pockets correspond to actual functional sites, thereby closing the loop between in silico hypothesis generation and wet‑lab verification. Across all three examples, the authors emphasize that bioinformatics does not merely analyze data post‑hoc; it actively generates hypotheses that are subsequently subjected to rigorous experimental testing. The discussion highlights essential scientific practices—data quality control, algorithm transparency, reproducibility, and open‑source sharing—that must be upheld in bioinformatics to meet the standards of experimental science. The authors contend that when these criteria are satisfied, bioinformatics constitutes a form of “computer experiment” that is scientifically equivalent to traditional bench work. In conclusion, the paper proposes a paradigm shift: bioinformaticians should evolve into integrated experimental scientists who design experiments, develop computational models, and validate predictions within a single, cohesive workflow. This vision positions bioinformatics as a central, experimentally grounded discipline in modern biology.