GoKawiilAs every gamer knows, computers can plausibly simulate just about anything from the routine concerns of a household to the crises confronting a multiplanetary civilization. Simulating the fundamental unit of life — the cell — should be a walk in the park. But it’s not.
Each cell is a complex ecosystem of biomolecules that interact with one another and react to external cues in ways that remain poorly understood. And what’s true of one cell type isn’t necessarily true of another. But there is an order to the chaos.
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“The cell is a complex system, and a highly robust and resilient system,” says Emma Lundberg, a bioengineer at Stanford University in California. “But it’s also a highly structured system — the cell has an architecture.” Over the past few years, researchers have begun reverse-engineering that architecture to convert vast repositories of molecular data into ‘virtual cells’ — models that simulate the internal environment of cells both at rest and when responding to external triggers.
Several teams are now tapping into deep reservoirs of transcriptomic (gene expression) and other data sets to build models that could reveal the underlying biological bases of disease and possible angles for therapeutic intervention. “We have to think about virtual cells as a means of getting towards a specific goal, and for me, that goal is to be able to accelerate the hypothesis search process,” says Yusuf Roohani, a machine-learning researcher at the Arc Institute in Palo Alto, California.
The field remains far short of a fully functional virtual cell, however. “I don’t think people would sensibly want to claim that they have built a virtual cell unless they need to sell a start-up,” says Fabian Theis, a computational biologist at Helmholtz Centre Munich in Germany. Current models can capture static cell states but struggle to accurately predict dynamic changes. Reaching higher levels of in silico evolution will require ever-greater volumes of diverse data and smart strategies for combining them.
A strong foundation
The artificial-intelligence revolution has been a potent accelerant for enthusiasm around virtual cells, but scientists have grappled with how to build computational cell models for decades. “Even 20-something years ago, we had ‘virtual cell 1.0’, where people were trying to use differential equations to describe systems biology,” says Bo Wang, an AI specialist at the University of Toronto in Canada.
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Such models have the advantage of being grounded in measurable, well-understood biochemical and biophysical principles — threading together equations that describe cellular functions including metabolism, communication and movement. “You actually have mechanistic understanding — you can interpret them correctly, and that is very attractive,” says Lundberg.
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