Summary: Virtual You: How Building Your Digital Twin Will Revolutionize Medicine and Change Your Life by Peter Coveney and Roger Highfield

• What if you could have a digital version of yourself, a virtual you, that could help you improve your health and your life? This is the intriguing question that the book [Virtual You: How Building Your Digital Twin Will Revolutionize Medicine and Change Your Life] by [Peter Coveney and Roger Highfield] answers, by showing how scientists are working to create digital human twins that will enhance our personalized medicine and our future.
• If you want to discover how your digital twin can help you prevent and treat diseases, optimize your diet and lifestyle, and extend your lifespan, as well as the challenges and opportunities that this new technology poses, then you should read this book. It will open your eyes to the amazing possibilities and the ethical dilemmas of creating and using your virtual you.

Recommendation

In the future, doctors may use “digital twins” – sophisticated computer simulations of your body – to provide personalized medical care. Your digital twin would react as you would to a drug or other treatment. It could also alert your doctor to health issues that might trouble you in the future. Professors Peter Coveney and Roger Highfield illuminate the history and future of biological simulations. Though they target a general audience, their citations of mathematics, biology and computer science make their book a challenging, stimulating read for laypeople.

Take-Aways

• The engineering concept of “digital twins” is an emergent medical tool.
• Creating Virtual You starts with amassing health data.
• Researchers will transform data into theory.
• Creating Virtual You will require new levels of computer power.
• Simulated cell research could lead to new understandings of molecular biology and to new medications.
• Researchers are working to simulate and study how human organs function..
• Virtual You simulations require new types of computers.

Summary

The engineering concept of “digital twins” is an emergent medical tool.

Engineers have long used digital twin computer models to aid in the design of machinery and manufacturing processes. These simulations played a role in the design of everything from cars to spacecraft.

Now, people apply the concept to the biological processes of living organisms, including human beings. Scientists hope to go beyond simulating a generic human body to creating detailed simulations of individual patients. This “Virtual You” would model the functions of your body – from molecular interactions to the workings of your heart, lungs, bones and brain – and the infrastructure that binds them.

“The convergence of many branches of science – patient data, theory, algorithms, AI and powerful computers – is taking medicine in a new direction, one that is quantitative and predictive.”

With Virtual You, doctors may personalize treatment to an unprecedented degree by using your digital twin to test the potential effects of drugs and other therapies. Similar to how meteorologists use computer modeling to forecast the weather, doctors may be able to offer “healthcasts” to predict and prevent a patient’s specific illnesses – rather than react to illnesses after they manifest. These healthcasts could describe your general health outlook on the basis of variables such as diet and lifestyle.

Creating Virtual You starts with amassing health data.

An enormous store of data is already available from sources such as patients’ routine lab tests, advances in DNA sequencing and discoveries in molecular biology. Scientists crafting digital twins must determine how many of these details a useful simulation requires. Scientists and researchers seek to learn whether the model should attempt to replicate the intricacies of all the trillions of cells in the human body.

They must also deal with the body’s emergent properties – situations in which a system produces a result that is “qualitatively different” than the sum of its constituents. Life itself is an emergent phenomenon arising from an aggregation of nonliving molecules.

“To understand how the nonlinear whole of the body emerges from its physiological, cellular and molecular parts is central to the problem of how to create Virtual You.”

Computer power restricts the level of practical detail. A simulation that attempted to incorporate every detail of a beating heart, for example, would take thousands of years to run. But as with geographic maps, the necessary granularity depends on your reason for using the map. A hiker needs a map that shows individual trails, while an airplane pilot needs one that displays the wider topography.

Researchers will transform data into theory.

The life sciences, including medical science, have remained less dependent on theory than the other branches of science. Theory – a “mathematical expression of the laws of nature” – requires making predictions, a fundamental element of the scientific method. With theory, for example, Albert Einstein predicted gravitational waves 100 years before scientists found them.

“The level of detail we require to take the first step to create Virtual You depends on what questions we want to ask.”

But in the life sciences, even such an important landmark as the theory of evolution lacks a mathematical expression. No one can predict the path of evolution in a “quantitative manner.”

In the 1950s, British scientists Alan Hodgkin and Andrew Huxley advanced biological theory by describing, in mathematical form, the function of the axon – the part of a nerve cell that carries impulses – in longfin inshore squid.

Medical science generally relies on “post hoc” explanations of observed phenomena. Diagnoses generally consist of identifying and explaining symptoms of a disease a patient already manifests. With a more predictive approach, doctors and patients could devise strategies that prevent symptoms from arising in the first place.

Medical science uses this backward looking orientation in the development and utilization of drugs. Doctors evaluate a drug’s safety and effectiveness on the basis of how it affected other patients or drug trial subjects. As a result, a drug or vaccine may work well for some people, but prove ineffective or dangerous for others.

“Current medicine is akin to a glorified lookup table, where you are diagnosed and treated depending on the past experience of doctors who have treated similar patients over generations.”

Adding a predictive dimension to medical science will require transforming data into equations that help reveal the natural laws underpinning the data. Theory helps pinpoint the data that are essential to constructing predictive computer simulations. Ideally, utilizing these simulations, doctors will predict how a particular therapy will work, and will understand which diet and lifestyle choices are best for individual patients.

Creating Virtual You will require new levels of computer power.

Serious work in simulation science began with the Manhattan Project, the United States’ pioneering effort to create a nuclear weapon. Calculating the mathematics of explosive shock waves proved a slow process with the analog computers of the 1940s, so scientists employed some of the first electronic digital computers, starting with the breakthrough ENIAC (Electronic Numerical Integrator and Computer). The project spurred advances in simulating phenomena at a molecular scale.

The effort to create biological simulations dates back to 1950, when Kenneth Cole of the Naval Medical Research Institute used the Standard Eastern Automatic Computer to model the Hodgkin-Huxley equations for the function of the axon. By the end of the decade, British researcher Denis Noble began work on a virtual heart cell.

“We are indeed in the era of Big Data, but…it takes Colossal Data to describe the human body.”

More recently, advances in modeling efforts have accelerated with the increasing power of supercomputers. To speed things up further, scientists add artificial intelligence to the mix. AI analyzes the overwhelming surfeit of patient data – the raw material of simulations.

Simulated cell research could lead to new understandings of molecular biology and to new medications.

Cellular simulations could increase understanding of recent advances in molecular biology research. One route of inquiry would involve explaining the range of chemical reactions that take place within a cell under a variety of circumstances. The inquiry would seek a mathematical theory that explains such behavior and can predict how the cell would respond in new situations.

Attempts at developing cell simulations have grown in tandem with boosts in computer power and advances in molecular biology theory. In the late 1990s, researchers at Keio University in Japan created a model of the bacterium Mycoplasma genitalium that modeled the organism’s metabolism, gene transcription and gene translation.

By 2012, systems biologist Markus Covert and a team at Stanford University had announced that its M. genitalium model was the first bacterium model to address all known gene functions. The Stanford team’s work produced accurate predictions and could prove useful in adapting microorganisms for making drugs.

“In future, medicine will increasingly be led by scientific insights into health and treatments that, akin to engineering, are based on theory, data, modeling and insights about how your own body works.”

Concurrent with this modeling of bacterial (or prokaryote) cells, University of Connecticut researchers launched a multidisciplinary effort to model the more complex eukaryote cells that compose plants and animals. Researchers at Mount Sinai School of Medicine and Columbia University used these models to study the kidney, pancreas and brain.

Such simulations could be helpful in designing personalized medical treatments. They can be useful in drug development and drug trials. Researchers have made recent progress in testing drugs for adverse effects on the heart, a critical issue in drug development. Simulations may offer a more accurate alternative to animal studies.

Researchers are working to to simulate and study how human organs function.

Simulating the complexity of the human body will require more than scaling up models from the molecular and cellular levels to those of organs or the entire body. The most promising approach creates models that are “multiscale” and “multiphysics.” An “adaptive mesh refinement” integrates a “coarse” large-scale orientation with a “finer” one for examining details. The multiphysics approach means researchers combine theories from different disciplines.

“Biological systems – notably the human body – might well be more complex than the vast structures of light and dark we call the cosmos.”

Teams have been working since the 1990s to model the entire human heart, which requires the multiscale, multiphysics approach. One impressive recent advance is the Alya Red model from the Barcelona Supercomputing Center (BSC). Although it runs more slowly than researchers would prefer, Alya Red can provide insights into heart failure and raise red flags on the potential dangers of various heart medications.

Such modeling could help patients avoid angiograms and other invasive tests. The models have promoted new understandings of such problems as heart failure, atrial fibrillation and irregular heartbeats.

The BSC collaborates with the company Medtronic to design simulations to further development of medical devices. These simulations may help with the implanting of such devices by revealing the best positioning for a pacemaker, for example.

Once physicians can regularly update a heart simulation with a patient’s data, such a model will serve as a resource throughout the patient’s lifetime.

Numerous projects are underway around the globe to develop schemes for simulating the entire body. At New Zealand’s Auckland Bioengineering Institute, director Peter Hunter and his team seek to expand simulation research from the heart to all of the body’s organ systems.

“When digital twins become established, each one will mark a symbiotic relationship between a person and their digital double, nourishing each other with data and insights.”

Researchers are using imaging, mathematics, laboratory experiments and computer modeling to understand how the body’s “infrastructure” uses tools such as hormones to regulate physical activities. They study the nerves connecting every organ to the brain and spinal cord. These studies could aid researchers designing “electroceuticals,” electrical signals that send messages through the nerves to the brain. Doctors could use such signals to stop inflammation or activate nerves that help suppress infection. Another area of investigation focuses on muscles, bones and connective tissues, which may provide insight into disorders affecting elderly and overweight populations.

Virtual You simulations require new types of computers.

Today’s digital twins are generalized, and simulate only parts of the human body. The project of modeling the systems of the entire body will not only need more powerful supercomputers, but also new types of computers. Modeling with digital computers faces limitations due to their reliance on algorithms: Certain elements of mathematics – and nature – are nonalgorithmic.

“For chaotic systems, which are ubiquitous, our faith in computers to generate reliable results is unfounded.”

Researchers may tackle these issues with updated versions of analog, the earliest type of computers. Instead of the mechanical gears of traditional analog computers – as in Charles Babbage’s 19th-century Difference Engine calculating machine – new analog computers might manipulate light. The novel materials “metamaterials,” for example, display a remarkable ability to influence light waves.

Another approach relies on designing analog computers on the basis of the analog operations of the human body. For example, the field of neuromorphic computing uses hardware that is inspired by the brain. Applications of this concept include Europe’s Human Brain Project, an initiative involving more than 500 scientists and engineers that has been developing the large neuromorphic computer SpiNNaker.

Another machine on the horizon is the quantum computer. Unlike digital computers, which represent each bit of information with either a one or a zero, quantum computers use qubits, which, in line with the strange behavior of quantum wave functions, represent ones and zeroes simultaneously. Because of their method of incorporating principles of quantum mechanics, quantum computers should be the best future option for work in chemistry and biochemistry.

About the Authors

The director of University College London’s Centre for Computational Science, Peter Coveney is also a professor at the Informatics Institute at the University of Amsterdam and an adjunct professor at the Yale School of Medicine. In 2023, Roger Highfield, the Science Director at the Science Museum Group, was named honorary president of the Association of British Science Writers.

Genres

Science, Technology, Nonfiction, Medicine, Health, Biology, Computing, Bioengineering, Futurism, Ethics

Review

The book is a panoramic account of efforts by scientists around the world to build digital twins of human beings, from cells and tissues to organs and whole bodies. These virtual copies will usher in a new era of personalized medicine, one in which your digital twin can help predict your risk of disease, participate in virtual drug trials, shed light on the diet and lifestyle changes that are best for you, and help identify therapies to enhance your well-being and extend your lifespan.

The book reveals what it will take to build a virtual, functional copy of a person in five steps: data collection, data integration, data analysis, data visualization, and data interpretation. Along the way, the book takes you on a fantastic voyage through the complexity of the human body, describing the latest scientific and technological advances, from multiscale modeling to extraordinary new forms of computing, that will make “virtual you” a reality. The book also considers the ethical questions inherent to realizing truly predictive medicine, such as privacy, consent, ownership, and responsibility.

The book is a fascinating and visionary exploration of the potential and challenges of creating digital human twins. The authors are experts in their fields and write with clarity, authority, and enthusiasm. They provide a comprehensive overview of the current state of the art and the future directions of this emerging field, covering both the scientific and the social aspects. The book is well-researched and well-referenced, with ample examples, illustrations, and case studies to support the arguments and claims.

The book is also engaging and accessible, with a lively and conversational tone, and a good balance of technical details and general explanations. The book is suitable for a wide audience, from students and researchers to policymakers and general readers, who are interested in learning more about the science and the implications of building your digital twin.