The killing of millions of mice and other laboratory animals—cats, dogs, frogs, birds and monkeys—is controversial, to say the least. Many who find the practice abhorrent contend that it’s unnecessary. They point to advances in testing drugs and other products without the use of animals as indications of what could be. A determined quest for alternative ways to gauge toxicity, allergies and drug interactions has eliminated virtually all animal testing for cosmetics. Now, instead of dropping a dollop of shampoo in a rabbit’s eye to check for an allergic reaction, the shampoo goes into a dish containing cultured human cells or artificial skin tissue. And during preliminary drug development, researchers may feed a compound’s chemical makeup into a computer that crunches data about how the body metabolizes compounds and predicts whether a drug would be toxic to the liver or would interact with other drugs. 

Those methods have fueled expectations that in vitro testing and computer models could also reduce the need for animals in studying diseases. With ever greater power and speed, computers should be able to sort through bewildering mazes of data about genetic and environmental factors to help determine how their effects in one organ influence other physiological systems.

“The sticking point is that, for many human diseases, we just don’t have enough information to feed into computer models,” says Rakesh K. Jain, a cancer biologist at the Massachusetts General Hospital. Jain trained as an engineer, and like Churchill, he is at the forefront of efforts to use analytical means to understand human disease. Jain has alternated between computer modeling and animal experiments in developing new strategies, including one, now in several human clinical trials, for delivering chemotherapy to tumors more effectively. But he notes that most modern killers—cancer, heart disease, diabetes and neurodegenerative diseases, not to mention neuropsychiatric disorders—involve still undeciphered interactions of multiple genes with the environment, life experiences and behavior. Each factor by itself may have just a small effect, and making sense of it all is extraordinarily difficult. Even relatively straightforward genetic diseases resist analytical modeling.

Take cystic fibrosis, a genetic disease that creates a life-threatening accumulation of mucus in the lungs and is known to result from a single mutation in a single gene. Researchers have managed to insert that gene into mice to make a living, breathing model of a human disease that mice would otherwise never acquire. That, in turn, has let the scientists tease out how the genetic mutation leads to the production of a protein that causes the physiological defects of the disease.

Theoretically, a computer model should be able to process this genetic information and predict what will happen to a child born with cystic fibrosis. But even this disease defies modeling, Churchill says. One child with cystic fibrosis may die in infancy, while another becomes a competitive gymnast, and no one yet understands what other factors account for the difference.