Virtual biology is a reader-friendly umbrella term for using computational representations and simulations to investigate living systems. Researchers model selected parts, processes, or scales of biology—not necessarily an entire cell or organism—and compare model results with observations or experiments.
What does “virtual biology” mean?
“Virtual biology” is not established in the sources cited here as a standardized technical discipline with one agreed definition. Researchers more often use specific terms such as computational biological models, virtual cells, and biological digital twins. In practical terms, virtual biology means representing some part of a biological system in a computer so researchers can analyze it, simulate its behavior, and examine how well its outputs fit evidence.
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A model is selective by design. It may represent molecules interacting inside a cell, a network of cells, an organism’s behavior, or a population. Its usefulness depends on the question it was built to address; a simulation should not be mistaken for a complete virtual organism.
How researchers use computational models
- Define the question and scope. Researchers decide which biological system, scale, processes, and outcomes matter for the problem.
- Build a representation. They encode relevant parts and relationships using a suitable mathematical or computational approach.
- Analyze or simulate. The model can be used to explore mechanisms, organize evidence, or generate predictions about the represented system.
- Compare with evidence. Researchers assess model outputs against relevant observations or experimental data, then revise the model or identify where it fails.
Depending on the question, a model may use ordinary differential equations, Boolean functions, graphs, stochastic systems, or constraint-based methods. These forms are not interchangeable: each makes different choices about what to represent and how the system can behave.
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Types and scales of biological models
Mechanistic and data-driven approaches
Mechanistic models describe biological components and the processes connecting them. Data-driven machine-learning approaches instead learn patterns from data. In practice, model construction can draw on both biological assumptions and observed data; neither category alone guarantees a better model.
Different biological scales
Models can address molecular, cellular, organismal, or population-level questions. Multiscale modeling links more than one level, but linking scales does not make a model complete: its scope still reflects the processes and evidence included.
Virtual cells and digital twins
“Virtual cell” is used for computational representations of cellular systems, but the label does not mean every such model reproduces an entire cell. A 7 April 2026 Nature Biotechnology editorial, “Minimal life by computer,” says current AI systems described as virtual-cell models are not yet representations of an entire cell.
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A biological digital twin is a more specific idea: the 4 June 2026 PLOS Computational Biology perspective, “Predictive modeling in biology and medicine: Digital twins and multi-scale modeling,” describes one as dynamically calibrated so it evolves with the biological system it represents. The label should not be taken as proof that a model is a complete or clinically established replica.
A concrete example: the OpenWorm project
OpenWorm is an international, open-source collaboration building multiscale models of the nematode Caenorhabditis elegans. A 2018 report in Philosophical Transactions of the Royal Society B, “Towards systematic, data-driven validation of a collaborative, multi-scale model of Caenorhabditis elegans,” describes models spanning subcellular, cellular, network, and behavioral levels.
The researchers used quantitative, data-driven tests to compare modeled behavior with experimental data. Those comparisons helped identify features the models did not yet reproduce adequately. The example illustrates why validation is not just a final stamp of approval: testing can reveal where a model is useful and where it needs improvement.
How to judge whether a model is useful
A model’s credibility is specific to the task it is meant to support. The 2026 npj Systems Biology and Applications perspective, “From FAIR to CURE: guidelines for computational models of biological systems,” emphasizes verification, validation, and uncertainty quantification (UQ). Its authors write: “For credibility, we recommend the use of verification, validation and UQ.”
- Verification: Check that the model has been implemented and computed as intended.
- Validation: Compare its behavior with relevant evidence for the intended purpose. Agreement in one test does not establish reliability for every use.
- Uncertainty quantification: Assess how uncertainty in data, assumptions, or model choices affects results.
- Transparency and reproducibility: Make assumptions, data provenance, annotations, and methods inspectable so others can understand, reproduce, or extend the work.
When comparing two models, look at the biological scale and system represented; whether construction is mechanistic, data-driven, or a combination; the supporting data and assumptions; the intended task; the verification and validation performed; the documented uncertainty and limitations; and whether the model is annotated and reproducible. CURE guidance highlights credibility, understandability, reproducibility, and extensibility as useful qualities.
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What virtual biology does not mean
Computational models do not make experiments unnecessary. They are representations to be checked against observations, and their conclusions apply within the scope and evidence of the model. Nor does “virtual cell” or “digital twin” automatically mean a complete, continuously accurate replica of a living system. The 2026 CURE perspective notes that biological modeling generally has not reached the sophistication seen in some digital-twin fields, with protein folding and molecular dynamics possible exceptions.
For scale, the 2026 Nature Biotechnology editorial describes JCVI-syn3A as a synthetic bacterium with 493 genes and discusses a simulation visualizing replication and segregation, including heterogeneity across 50 replicate models. Those figures describe that specific example as reported in the editorial; they are not measures of virtual biology as a whole.
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