相关文章：Biology 1
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What is a Wet Experiment (湿实验)?
A “wet experiment” (湿实验) refers to laboratory work that involves the physical manipulation of biological or chemical materials — liquids, cells, tissues, organisms, reagents, and other tangible substances. The name comes from the fact that most of these experiments deal with solutions, buffers, cultures, and other “wet” materials in a bench-top lab setting.
Typical wet experiment activities include: running PCR (polymerase chain reaction), growing cell cultures, performing gel electrophoresis, conducting chemical assays, DNA/RNA sequencing library preparation, protein purification, animal model studies, and drug compound screening.
What is a Dry Experiment (干实验)?
In contrast, a “dry experiment” (干实验) refers to computational or in-silico work — bioinformatics, statistical analysis, mathematical modeling, machine learning, molecular simulations, and other tasks performed entirely on a computer without physically handling biological or chemical materials.
Typical dry experiment activities include: genome assembly and annotation, protein structure prediction, phylogenetic analysis, drug-target docking simulations, single-cell RNA-seq data analysis, and building predictive models from omics data.
Why the Distinction Matters
The wet/dry distinction is especially prevalent in fields like molecular biology, genomics, structural biology, and drug discovery. Modern research teams are often organized around this divide:
- Wet lab team: generates raw experimental data (sequencing reads, assay results, imaging data).
- Dry lab team: processes, analyzes, and interprets that data computationally.
Effective collaboration between wet and dry sides is critical. Wet experiments produce the ground truth data that dry analyses depend on, while dry analyses guide which wet experiments to prioritize next — forming an iterative feedback loop that accelerates discovery.
Key Differences at a Glance
| Aspect | Wet Experiment | Dry Experiment |
|---|---|---|
| Environment | Physical laboratory (bench, hood, incubator) | Computer (workstation, cluster, cloud) |
| Materials | Reagents, cells, organisms, chemicals | Data, algorithms, code |
| Output | Raw data (sequences, images, measurements) | Processed results (models, statistics, visualizations) |
| Reproducibility challenge | Biological variability, reagent lot differences | Software versions, parameter choices |
| Cost drivers | Reagents, equipment, animal models | Compute time, storage, software licenses |
Note
- The terms “wet lab” and “dry lab” are informal but universally understood in life sciences. In job postings you will often see roles described as “wet lab scientist” or “dry lab bioinformatician.”
- Some researchers are “hybrid” — they design and run their own wet experiments and also write code to analyze the resulting data. This skill set is increasingly valued.
- The rise of high-throughput technologies (next-gen sequencing, mass spectrometry, high-content imaging) has dramatically expanded the dry lab side, making computational skills essential even for traditionally wet-lab-focused disciplines.