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By including immune cells in their model, Perelson and Conway determined that an In his work toward understanding the early diagnosis and prompt initiation of infection dynamics of HIV, the Laboratory’s treatment can lead to a smaller “latent Alan Perelson works exclusively with model reservoir”—a hidden population of infected patients. Literally. He builds computer cells. These cells, as long as they aren’t models of HIV infection to help make sense of producing more viruses, are invisible to the puzzling clinical data. immune system. But they can become active When asked whether he is a virologist or a later and suddenly begin producing viruses. mathematician, Perelson quips, “Yes.” Pressed The more cells there are in the latent reservoir, for more, he clarifies, “I am a mathematical the harder it is for the immune response modeler of viral systems and immune processes.” to contain them once they start producing Experimental and clinical studies viruses. Very early treatment for the study provide the numbers, such as the number of patients capped their virus numbers at low HIV-susceptible cells in a volume of blood or levels, keeping their latent reservoirs small, the average number of viruses circulating in thus allowing their immune systems a chance the blood. Perelson takes those numbers and to get ahead of the infection. devises complex mathematical equations to The next question Perelson wanted to describe their relationships to one another, answer had to do with the nature of the then fits his model to the data. If it’s a good immune response. Certain types of antibodies, fit, he can illuminate portions of the virus- called broad neutralizing antibodies (BnAbs), host relationship that are otherwise murky. are effective at preventing the spread of infec- And during the past year, his team has made tion from one cell to the next. But because some compelling new discoveries. BnAbs only appear years into an HIV infection, When a French study reported that a their utility is usually handily overwhelmed dozen or so patients had quit antiviral drug by the virus. Perelson wanted to find a way therapy yet maintained undetectable virus to make BnAbs appear early in the infection, levels—representing a functional cure to when they can do the most good. an incurable disease—Perelson wanted to To do this, he and postdoctoral researcher know why. If researchers can understand how Shishi Luo built a model of virus-antibody that happened, then clinicians might be able coevolution. HIV mutates liberally to evade to help more patients achieve a functional neutralization by antibodies. Similarly, the cure. Perelson and postdoctoral researcher immune system uses mutation to produce a Jessica Conway hatched a theory that these spectrum of antibodies in hopes that some of patients, who had each been diagnosed and them will be strongly matched to the virus. treated very soon after infection, had in so The result is an evolutionary arms race with doing given their immune systems a leg up, both sides trying to stay nimble while casting which kept down the number of viruses in a wide net. The model revealed that antibody their blood. production is a zero-sum game, with BnAbs coming at a cost to other types of antibodies and vice versa. “So, if you’re going to put all your antibody eggs in one basket,” Perelson says, “it had better be the right basket.” The best antibody basket is indeed BnAbs if—and it’s a big “if”—the BnAbs come along early enough. The way to do that is through viral genetic diversity. More variation early on—say, from an intentionally diverse vaccine preparation—leads to more BnAbs sooner, shifting the infection dynamic in favor of the host. But Perelson isn’t concentrating solely on the host response. Recently he and postdoc- toral researcher Ruian Ke, along with several external collaborators, have been exploring ways of working the other side of the arms race: the latent reservoir, that sleeper cell of sleeper cells. A popular strategy, dramati- cally called “shock and kill,” attempts to first stimulate latently infected cells into becoming productive and then target them for destruction. Recent clinical results from a latency-reversing agent (LRA) called Vorinostat were inconsistent, with varying extents and durations of activation. Perelson’s task was again to suss out why such variation occurred and also to quantify the impact of Vorinostat. The model that fit the clinical data put LRA-activated cells into a different category than ordinary virus-producing cells and also allowed them to return to latency after a period of time, which makes Vorinostat less an agent of “shock and kill” and more one of “surprise and confuse.” So Vorinostat turns out to be a lackluster LRA. But when new, better LRAs come along, Perelson and his models will be set to crunch the numbers. Since drug discovery and clinical trials take years, it’s a relief to know that, for model patients at least, a cure may be just a few clicks away. —Eleanor Hutterer Nuclear War Against Cancer Exposure to nuclear radiation causes cancer—and sometimes cures it. But radia- tion, like chemotherapy, can be an indiscrimi- nate killer, attacking cancerous and healthy cells alike. The damage to healthy cells can be quite widespread, which is why the prospect of cancer treatment often generates appre- hension nearly on par with the cancer itself. However, a treatment called radioim- munotherapy (RIT) delivers specialized, radioactive isotopes, or radioisotopes, directly to cancerous tumors within a patient’s body. There, cell-killing radiation from the radioiso- tope bombards cancer cells while minimizing damage to the surrounding healthy tissue. 1663 March 2016 27