Illustration by Stephen Jeffrey

SEVEN years ago, one of the attractions at the now-defunct Millennium Dome in London was what looked like a remarkably detailed video of a beating human heart. People could admire the heart's delicate tracery of blood vessels with the muscle stripped away and hear a display of its electrical activity that would not have disgraced a disco. The voiceover described it as “one of the most powerful tools we have in the fight against disease”, but few of the visitors understood why.

Actually, the beating heart was no simple video. It was, instead, the output of a stupendously complex computer model of a heart, developed over more than 40 years. This model is an example of “systems biology”, an approach that represents a significant shift both in the way biologists think about their field and in how they go about investigating it.

A central tenet of most scientific endeavour is the notion of reductionism—the idea that things can best be understood by reducing them to their smallest components. This turns out to be immensely useful in physics and chemistry, because the smallest components coming from a particle accelerator or a test tube behave individually in predictable ways.

In biology, though, the idea has its limits. The Human Genome Project, for example, was a triumph of reductionism. But merely listing genes does not explain how they collaborate to build and run an organism. Nor do isolated cells or biological molecules give full insight into the causes and development of diseases that ravage whole organs or organisms. A complete understanding of biological processes means putting the bits back together again—and that is what systems biologists are trying to do, by using the results of a zillion analytical experiments to build software models that behave like parts of living organisms.

You can't beat the system

The pharmaceutical industry stands to gain much from this approach. Around 40% of the compounds that drug companies test cause arrhythmia, a disturbance to the normal heart rate. Drugs such as the anti-inflammatory medicine Vioxx and the diabetes treatment Avandia have been linked with an increased risk of heart disease. The result is that billions have been wiped off their makers' share prices.

Not surprisingly, the pharmaceutical industry has sought out Denis Noble of Oxford University, the creator of the beating-heart model, to help. Dr Noble is now part of a consortium involving four drug firms—Roche, Novartis, GlaxoSmithKline and AstraZeneca—that is trying to unravel how new drugs may affect the heart. Virtual drugs are introduced into the model and researchers monitor the changes they cause just as if the medicines were being applied to a real heart. The production of some proteins increases while others are throttled back; these changes affect the flow of blood and electrical activity. The drugs can then be tweaked in order to boost the beneficial effects and reduce the harmful ones.

Systems biology thus speeds up the drug-testing process. Malcolm Young is the head of a firm called e-Therapeutics, which is based in Newcastle upon Tyne. Using databases of tens of thousands of interactions between the components of a cell, his company claims to have developed the world's fastest drug-profiling system. In contrast to the two years it takes to assess the effects of a new compound using conventional research methods, Dr Young's approach takes an average of just two weeks. Moreover, the company has been looking at drugs known to have damaging side effects and has found that its method would have predicted them.

Testing for reactions in this way could also offer a more rigorous route to assessing alternative therapies, such as herbs and clinical nutrition (which seeks to control disease through the use of particular foodstuffs). These remedies are often dismissed as unscientific because they have a multitude of effects on the body that are hard to quantify. Studying multiple effects, however, is precisely what models like the virtual heart are able to do.

Nor need such models be confined to people. In biological terms, mice are better understood than men, and a team in the Netherlands is using a computer model of mouse physiology to investigate the effects of a high-fat diet, by monitoring the concentration of various components of the blood. The team, from a firm called SU BioMedicine, which is based in Zeist, found that the active ingredients of a particular concoction of Chinese herbal medicines have the same effect on blood composition as the anti-obesity drug Rimonabant. The hope is that systems-biology studies like these will eventually trace out the pathways the herbs are affecting.

Such models may also help to pin down the causes of diseases that arise from the interplay of genetic and environmental factors. Andrew Ahn of Harvard Medical School cites the example of diabetes, for which the standard clinical test is a measure of the level of glucose in the blood. But that is a single snapshot in time. Dr Ahn suggests that the way toward a fuller understanding of diabetes is to track glucose levels against other factors such as diet, sleeping habits and psychological health. He proposes to employ a systems-biology model to do so.

Ultimately, the aim is to build an entire virtual human for researchers to play with. But reductionism is still needed to get there. Human bodies are made of cells, and the best way to build a model body might be to construct a general-purpose virtual cell that can be reprogrammed into being any one of the 220 or so specialised sorts of cell of which the human body is composed. That, after all, is how real bodies develop. And a collaboration organised by the European Science Foundation is hoping to do just this, through what it calls the Blue Cell project.

Keeping track of the data needed to carry out systems biology on this scale will be a Herculean task, and may turn out to be the driver of future developments at the heavy-number-crunching end of the computer industry. Dr Noble is in negotiations with Fujitsu, a Japanese computer firm that is developing a machine capable of performing some ten thousand trillion calculations a second. That would make it the world's fastest computer, but it comes with a price tag to match—about a billion dollars. This is a little more than the $6m paid for that fictional bionic man, Steve Austin, even allowing for inflation. But it is only about a quarter of what the Human Genome Project cost. And this time, it might produce some answers that prove immediately useful.