those entwined networks to predict at a molecular level how individual organisms will respond under specific conditions.
By linking these layers and others to expand from genomics to multi-omics,
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, scientists might be able to meet the goals of personalized medicine: to figure out, for example,
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, what treatment a particular cancer patient will best respond to,
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, based on the network dynamics responsible for a tumor. Or predict whether an experimental vaccine will work before moving into expensive clinical tests. Or help crops grow better during a drought.
And while many of those applications are still in the future, researchers are laying the groundwork right now.
“Biology is being done in a way that’s never been done before,
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,” says Nitin Baliga,
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, director of the Institute for Systems Biology in Seattle.
Data dump
Scientists have long studied how genes influence traits,
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. Researchers have figured out important connections between genes and the proteins they encode and have scoured the genome for associations between particular genetic mutations and diseases. But a gene-by-gene view of the body is like trying to diagnose a citywide traffic problem by looking at just one backed-up intersection,
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.
“There are so many places that a system can go awry,
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,” Baliga says. When dozens of genes are working together, it’s tricky to tease out which one is misfiring in a particular instance.
Baliga is among a growing group of scientists who want to study life through a systems lens, because sometimes that traffic jam at one intersection is being caused

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