those entwined networks to predict at a molecular level how individual organisms will respond under specific conditions,
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.
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,
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, for example, 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, director of the Institute for Systems Biology in Seattle.
Data dump
Scientists have long studied how genes influence traits. 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.
“There are so many places that a system can go awry,
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,” Baliga says. When dozens of genes are working together,
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, 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,
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, because sometimes that traffic jam at one intersection is being caused

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