Deem wants to cut the amount of time between the analysis of flu strains and the manufacture of vaccines to fight them. He and graduate student Keyao Pan can predict the efficacy of H1N1 vaccines by estimating the antigenic "distance" -- the degree of difference between the epitopes -- for any two strains of virus.

Deem's technique assigns a numerical value to the antigenic distance between two strains. That tells researchers just how effective a virus might be. But it also offers a tipping point: If a value of zero is the perfect H1N1 vaccine, a value above roughly 0.4 indicates a vaccine that offers no protection at all.

That means there's a real incentive to formulating the vaccine as close to flu season as possible. It also means choosing strains of the virus that can be produced in high quantities but which are also as close as possible to the virus strain expected to hit. The current process is time-consuming: The novel H1N1 vaccines are incubating in hens' eggs -- the traditional method -- right now, and the United States expects to have 40 million doses in hand by mid-October, with 20 million doses arriving weekly thereafter, said Deem.

His calculations provide incentive to refine cell-based approaches that could shorten manufacture time. "In the United States government, this has been recognized, and there's investment now in new technologies," he said.

"I think modeling has already had an impact on the World Health Organization, and this type of modeling -- and our model in particular -- will have an impact."

Source: Rice University