TL;DR
Molecular biology is the science of biological information encoded in DNA, RNA, and proteins. Modern experiments generate this information at massive scale, and making sense of it requires computation. Bioinformatics does not sit beside molecular biology: it is one of its core ways of being practised today. A bioinformatician studies molecular mechanisms, regulation, and function using algorithms instead of pipettes, but the biological questions are the same. Different tools, same science.
The Full Story
Bioinformatics Is Molecular Biology - Just Practised at Scale
There is a persistent misunderstanding in life sciences: that bioinformatics is something adjacent to molecular biology rather than part of it. That a bioinformatician is a “data person” supporting “real biology” done at the bench. This view is not only outdated; it fundamentally misrepresents what molecular biology has become.
Bioinformatics is not a service layer around molecular biology. It is molecular biology, practised through different instruments.
Molecular biology has always been about information
At its core, molecular biology studies how biological information is encoded, transmitted, regulated, and altered at the molecular level. DNA, RNA, and proteins are not just molecules; they are informational entities. Genes are sequences. Regulation is pattern and timing. Mutations are edits. Expression is signal.
From the moment the genetic code was deciphered, molecular biology became inseparable from abstraction, modelling, and interpretation. The bench never delivered meaning on its own; meaning always emerged from analysis.
Bioinformatics did not appear because biology became computational. It appeared because biology became measurable at informational scale.
The modern bench produces questions, not answers
High-throughput sequencing, mass spectrometry, single-cell technologies, CRISPR screens, and spatial omics do not yield conclusions. They yield raw measurements - millions to billions of data points per experiment.
No amount of pipetting can tell you:
- which genes are differentially regulated,
- which pathways are perturbed,
- which variants are functional,
- which regulatory programs drive a phenotype,
- or which molecular mechanisms are causal rather than correlative.
Those questions are answered through statistical reasoning, modelling, and computational interpretation. That is molecular biology happening after the measurement, not outside it.
Bioinformatics asks molecular biology questions
What distinguishes a field is not the tools it uses, but the questions it asks.
Bioinformaticians routinely ask:
- How does a mutation alter protein structure or function?
- How do transcriptional programs change across development or disease?
- Which molecular pathways explain a phenotype?
- How do regulatory networks respond to perturbation?
- How do molecular interactions scale across cell types and conditions?
These are not computational questions. They are molecular biology questions addressed with computational methods.
A bioinformatician does not stop being a molecular biologist because the microscope is replaced by an algorithm. The object of study remains the same.
The algorithm is the new experimental apparatus
In classical molecular biology, insight came from carefully designed experiments at the bench. In modern molecular biology, insight comes from carefully designed analyses.
An RNA-seq differential expression model is not a technical afterthought; it is an experiment. A variant-calling pipeline is not “data cleaning”; it is molecular inference. A network model is not visualisation; it is a hypothesis about biological organisation.
Just as poor experimental design leads to wrong conclusions at the bench, poor modelling leads to wrong biology in silico. Both require biological understanding to get right.
Separation of “wet” and “dry” is logistical, not intellectual
The idea that molecular biology happens in the lab and bioinformatics happens “afterwards” is a convenience of workflow, not a reflection of scientific reality.
The split exists because:
- instruments are expensive and centralised,
- skills take time to acquire,
- and modern projects are collaborative.
But intellectual ownership does not stop at the centrifuge. Interpretation is not optional; it is the science.
A molecular biologist who cannot interpret high-throughput data is incomplete. A bioinformatician who cannot reason biologically is ineffective. The boundary is artificial.
Many foundational discoveries are computational
Some of the most influential molecular biology discoveries of the last decades were driven primarily by bioinformatics:
- gene regulatory networks,
- non-coding RNA function,
- alternative splicing landscapes,
- tumour mutational signatures,
- molecular subtypes of disease,
- evolutionary constraints on genes and pathways.
These were not discovered by a single experiment, but by integrating many experiments into coherent molecular explanations.
Training does not define identity: practice does
Many bioinformaticians were trained in molecular biology, genetics, biochemistry, or medicine. Others came from physics, mathematics, or computer science and became molecular biologists through practice.
What matters is not where someone started, but what biological problems they solve, how they reason about mechanisms, and whether their conclusions advance understanding of molecular systems.
If a person spends their professional life explaining how molecules behave in cells, they are a molecular biologist.
The future of molecular biology is inseparable from bioinformatics
As datasets grow in size, resolution, and complexity, molecular biology without computation becomes increasingly narrow. The future is not “wet versus dry”. It is integrated, iterative, and model-driven.
In that future, bioinformatics is not an accessory skill. It is one of the core ways molecular biology is done.
A clearer definition
A useful way to put it is this:
Molecular biology studies biological systems at the molecular level. Bioinformatics is one of the primary ways molecular biology is practised today.
Recognising this is not about status or labels. It is about accurately describing how biological knowledge is produced in the 21st century.
The BioLogical Footnote
The pipette and the algorithm are simply different instruments pointing at the same question: how does life work, molecule by molecule?
To Explore Further
Bioinformatics: from molecules to systems
Translational Bioinformatics: Linking the Molecular World to the Clinical World