Imagine knowing not only which diseases you are genetically predisposed to, but also how your body is likely to respond to a particular treatment, how your molecular profile is changing with age and which biological processes are driving those changes. This is one of the more interesting possibilities emerging from the combination of genomics, molecular profiling and computational modelling.

The idea is often described as a digital twin of a human being. In engineering, a digital twin is a computational model of a physical system that is updated with real measurements and used to understand or predict its behaviour. Applying this concept to biology is considerably more difficult. A human being is not a machine with a fixed set of components. Cells change their behaviour, tissues adapt to their environment and the same intervention can produce different outcomes in different people.

Nevertheless, recent developments suggest that we can begin building useful models of individual biology. The question is what information such a model would need and whether it could tell us something we cannot already learn from conventional medical testing.

Whole genome sequencing would provide a starting point. It reveals inherited variants, structural changes and other differences in DNA that may influence disease susceptibility or drug response. But the genome alone tells us relatively little about what is happening inside the body at a particular moment. Two people with similar genetic predispositions can have very different molecular profiles because of their age, environment, medication, lifestyle and medical history.

This is where other forms of molecular measurement become important. Transcriptomics reveals which genes are being expressed. DNA methylation profiling provides information about epigenetic regulation and changes associated with ageing and disease. Proteomics and metabolomics measure molecules closer to the biochemical processes taking place in cells and tissues.

Combining these measurements could provide a much more detailed picture of an individual's biology. However, collecting more data does not automatically produce a better understanding of the person. Each measurement captures only part of a complex system, and some apparent differences may reflect technical variation rather than meaningful biological change.

Consider DNA methylation as an example. Researchers often use methylation patterns to estimate biological age, but these measurements are influenced by the composition of the tissue being analysed. Blood contains different types of immune cells, and their proportions change with age, infection and other conditions. A methylation difference between two samples may therefore reflect a change in cell populations rather than a change occurring within the same cell type. Without accounting for this, a model could mistake a change in tissue composition for a change in the underlying ageing process.

Similar problems arise when combining data from different tissues. A blood sample cannot be assumed to represent what is happening in the liver, brain or skin. Even measurements taken from the same tissue can differ depending on the time of day, recent food intake, medication and other factors. A useful biological model must account for these differences rather than treat every measurement as an equally reliable description of the entire organism.

There is another important distinction between describing someone's biology and predicting what will happen to it. A system that combines a genome, methylation profile and blood biomarkers might identify people who resemble individuals at higher risk of a particular disease. That could be useful, but it would not necessarily explain why the disease develops or which intervention would prevent it.

This distinction is central to the emerging research on medical digital twins. Researchers working in this area are exploring ways to combine data driven models with mechanistic descriptions of biological processes. Instead of relying entirely on patterns found in previous patients, a model could incorporate what is already known about how a system works. In principle, this would make it possible to investigate how changing one variable might affect others.

For example, a pharmacogenomic model might combine genetic variants affecting drug metabolism with information about kidney function, other medications and relevant clinical measurements. Its purpose would be to estimate how a particular person is likely to process a drug and whether the usual dose may need reconsideration. This is a more realistic starting point than attempting to simulate the entire human body because the biological question is clearly defined and the relevant mechanisms can be studied.

Cancer research offers another useful example. A tumour changes over time as mutations accumulate and different cell populations expand or disappear. A biopsy captures only part of this process at a particular moment. Researchers are investigating whether computational models that combine tumour genomics, molecular measurements and treatment history could help predict how a tumour might respond to different therapies. The difficulty is that a model must account for tumour heterogeneity and evolution, not simply identify mutations associated with treatment response.

One of the most promising directions is to connect computational models with experiments using cells obtained from the patient. Organoids and other laboratory models can reproduce selected features of human tissues and allow researchers to test how cells respond to different treatments. They do not recreate an entire person, and their behaviour may differ from that of the original tissue. Nevertheless, they offer a way to test some predictions experimentally rather than relying entirely on computer simulations.

This could create a more useful relationship between modelling and experimentation. A computational model would propose which intervention is most likely to produce a desired effect. An experiment would test that prediction, and the results would be used to improve the model. Repeating this process could gradually produce more reliable predictions for a particular biological system.

Longitudinal measurements would be equally important. Most medical tests provide information about a person at a single point in time. Repeated measurements can reveal whether a molecular change is temporary, persistent or part of a longer trajectory. They may also help distinguish a person's usual biological variation from changes associated with disease or treatment.

Imagine a system that follows changes in gene expression, inflammatory markers and other molecular measurements over several years. If a particular pattern repeatedly appears before a measurable deterioration in health, it could provide an early warning signal. But even then, the model would need to establish whether the pattern predicts the outcome in new individuals and whether acting on that prediction improves health. An early signal is not necessarily a cause of disease, and identifying one does not automatically reveal the correct intervention.

This is why validation is likely to be one of the hardest problems in developing biological twins. A model can perform well on the data used to train it while failing when applied to a different population. It may also predict a biomarker accurately without predicting a clinically meaningful outcome. The strongest evidence would come from testing predictions prospectively and comparing them with what actually happens after an intervention.

There is also a practical question about how much information is really necessary. Sequencing an entire genome, measuring thousands of proteins and profiling gene expression across multiple tissues would be expensive and difficult to repeat frequently. Some measurements are invasive, and others provide only indirect information about processes occurring elsewhere in the body.

The goal should not be to collect every possible measurement. It should be to identify which measurements improve predictions enough to justify their cost and complexity. A model designed to investigate skin ageing may require a different set of measurements from one intended to predict drug response. The most useful system may combine a relatively stable genetic profile with a smaller number of measurements that are updated as the person's biology changes.

Over time, these models could become more individualised. New measurements would reveal whether the predicted trajectory matches reality, and the model could be adjusted when it does not. Rather than treating a digital twin as a perfect copy of a person, it may be more accurate to think of it as a continuously updated model that becomes useful within specific limits.

There is a risk of overstating what this technology can achieve. Human biology contains too many interacting processes for us to expect a complete and reliable simulation in the near future. A model will always depend on assumptions, incomplete measurements and the quality of the evidence used to develop it. Some biological responses may remain difficult to predict even when large amounts of data are available.

Yet a complete simulation may not be necessary. We do not need to model every process in the body to improve a specific medical decision. If a model can reliably predict which treatment is more likely to work for a particular patient, or identify a biological change that warrants further investigation, it could have considerable value without explaining the entire organism.

The longer term possibility is that several specialised models could eventually be connected. One could describe how genetic variation influences drug metabolism, another could track changes in cellular states, and another could estimate how those changes relate to a particular disease. Together, they might provide a more coherent account of an individual's biology than any single test can offer.

For this to become clinically useful, the predictions must be reliable, the limitations must be understood and the benefits must justify the cost. Biological information is also highly personal, particularly when it is collected repeatedly and combined across different sources. Questions about privacy, consent and access will become increasingly important as these systems develop.

The real promise of genetic twins is not that we will eventually be able to reproduce a human being inside a computer. It is that we may be able to understand individual biological systems well enough to anticipate some of their responses to disease, ageing and treatment. Genomics gives us information about where we start, while other molecular measurements reveal aspects of our current state. The challenge is to connect these observations into models that can make predictions and demonstrate that those predictions are correct.

That is a more modest ambition than a complete digital copy of a person, but it is also a more useful one. The value of a biological twin will ultimately depend not on how much data it contains, but on whether it can tell us something accurate and actionable that we would not otherwise have known.