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Virtual biology is a useful umbrella term for using computational representations and simulations to investigate living systems. Researchers model selected parts, processes, or scales—not necessarily an entire organism or cell—and compare model results with observations and experiments. The sources discussed here use more specific terms, including computational biological models, virtual cells, and digital twins; they do not establish “virtual biology” as a standardized technical label.
What virtual biology means—and what it does not
A computational model represents some aspect of biology in a form that can be analyzed or simulated. Depending on the question, that representation might describe molecular interactions, cell behavior, an organism’s nervous system, or population-level dynamics. The model is a purposeful simplification: it includes the features relevant to a particular problem and leaves others out.
That distinction matters when a model is called a “virtual cell” or a “digital twin.” Those terms describe active research ideas, not a guarantee that a simulation reproduces every feature of a living cell or organism. A 2026 Nature Biotechnology editorial says current AI systems described as virtual-cell models are not yet representations of an entire cell. A 2026 perspective in npj Systems Biology and Applications likewise cautions that biological modeling generally has not reached the sophistication found in some digital-twin fields, with protein folding and molecular dynamics possible exceptions.
How researchers build and use computational models
- Define the biological question and scope. Researchers decide which system, scale, and processes matter to the question. A model intended to explore a molecular mechanism will represent different details from one intended to study organismal behavior.
- Choose a representation. Depending on the biology and task, a model can use ordinary differential equations, Boolean functions, graphs, stochastic systems, or constraint-based methods. These are different ways to encode relationships and behavior, not competing formats with one universally best choice.
- Set up the model from evidence and assumptions. Researchers specify the parts, interactions, parameters, or rules the model will use. Its results depend on those choices and on the data that support them.
- Analyze or simulate. The model can help organize evidence, explore possible mechanisms, and generate predictions. A prediction is a result to investigate, not by itself proof that the modeled explanation is correct.
- Compare results with observations. Researchers check whether model outputs agree with relevant experimental or observational data, then use mismatches to identify limitations or features the model does not yet reproduce.
Common approaches and scales
Model type and biological scale are separate choices. A model can be mechanistic or data-driven while addressing a molecular, cellular, organismal, or population-level question.
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Mechanistic models
Mechanistic models represent biological parts and processes and how they interact. Their structure makes assumptions about components and relationships explicit, though the representation may still simplify the living system.
Data-driven and machine-learning models
Data-driven approaches, including machine learning, learn patterns from data. They can be useful when the task is to identify or predict patterns, but their outputs should be interpreted in light of the data and assumptions underlying the model.
Digital twins and multiscale models
A biological digital twin, as described in a 2026 PLOS Computational Biology perspective, is calibrated dynamically so that it evolves with the biological system it represents. Multiscale models connect representations across levels, such as molecular, cellular, and organismal processes. These labels describe scope and approach; neither is a universal ranking of model quality or evidence of a complete replica.
How to judge whether a model is useful
There is no single credibility score that applies to every biological model. A model’s credibility is specific to the problem and intended use. The 2026 CURE perspective recommends verification, validation, and uncertainty quantification (UQ): “For credibility, we recommend the use of verification, validation and UQ.”
- Verification: Check that the model and its implementation behave as intended. A coding or implementation error can undermine results even when the underlying idea is reasonable.
- Validation: Compare model outputs with relevant observations or experiments to assess how well the model represents the system for its stated purpose.
- Uncertainty quantification: Examine how uncertainty in data, parameters, or assumptions affects results and conclusions.
- Scope and assumptions: State what the model represents, what it excludes, and which question it is intended to answer.
- Provenance and reproducibility: Make data origins, annotations, assumptions, and methods inspectable so others can understand, reproduce, and potentially extend the work.
CURE guidance highlights credibility, understandability, reproducibility, and extensibility as useful qualities. When comparing two models, look at their biological scope, construction, supporting data and assumptions, intended task, verification and validation, uncertainty, documented limits, and potential for reuse—not just how detailed or visually convincing a simulation appears.
OpenWorm: testing a model against a living system
OpenWorm is an international open-source collaboration developing multiscale models of Caenorhabditis elegans. A 2018 report described models at subcellular, cellular, network, and behavioral levels. Researchers used quantitative, data-driven tests to compare model behavior with experimental data and identify features that the models did not yet reproduce adequately. The example illustrates a practical role for simulation: a model can be assessed against evidence, and its failures can point to where it needs improvement.
A virtual-cell example—and the right way to read it
A 2026 Nature Biotechnology editorial describes a simulation of JCVI-syn3A, a synthetic bacterium with 493 genes. The editorial says the simulation visualized replication and segregation, as well as heterogeneity across 50 replicate models. Those figures describe this specific example as presented by the editorial; they are not general measures of virtual-cell accuracy or field-wide progress. The example also does not change the broader qualification that current AI virtual-cell models are not yet representations of an entire cell.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What computational biology can—and cannot—establish
Computational models give researchers a structured way to explore living systems, connect evidence, and produce predictions that can be checked. Their value depends on whether their construction and results are fit for the question at hand. Simulation does not automatically replace experiments: comparison with experimental data is part of how researchers assess what a model captures and where it falls short.
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For readers, the most useful questions are concrete: What biological system and scale is represented? What evidence and assumptions support the model? What task is it designed to answer? How have its implementation and outputs been checked? What uncertainty and limitations are reported? Those answers reveal more than labels such as “virtual cell” or “digital twin” alone.
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