The explosion of biological data generated through genomics, proteomics, transcriptomics, and metabolomics have only increased the importance of coding skills in biotech fields, particularly for data analysis, modeling, and automation in research.
Specifically, data analysis and interpretation of very large datasets, like protein interaction networks and genomic sequences, are often too complex and immense for manual analysis, making them ideal for programmatic recognition of patterns and statistical analysis. Programming languages like Python, MATLAB, and R are widely used for these purposes.
Furthermore, modeling and simulation of cellular processes and protein folding are simplified through coding, allowing scientists to more easily simulate processes with varying parameters and achieve greater efficiency through rapid prototyping. Computational models have also been used to study diverse phenomena including the spread of disease and the behavior of biomolecules.
Another benefit of coding in the biotech realm is in data visualization, which, facilitated by coding, makes it easier to convey research findings in a comprehensible way. Coding is also ideal for automating repetitive tasks like data cleaning and image analysis, and machine learning techniques can be applied to make predictions of device or drug outcomes.
Overall, not only have advances in programming aided existing scientific and engineering fields, but they have also given way to newer specialized fields like bioinformatics, at the intersection of computer science and biology. Ultimately, through coding, scientists can design and program genetic circuits and biological systems to enable precision engineering at even the molecular level, with expansive benefits from cost-efficient data analysis to modeling and data communication.
Sources:
- Bharath, Lavanya. “Do Biologists Need to Learn Coding?” Biotecnika, 2023, https://www.biotecnika.org/2023/10/do-biologists-need-to-learn-coding/. Accessed 7 Feb. 2025.
