Cracking Cellular Motion
By applying biophysical principles to a simulated slice of a cell, researchers uncover molecular speed limits
Three centuries after English scientist Robert Hooke first observed a cell with a microscope, multiple mysteries persist about life’s primary building blocks.
It’s certain that cells are active places crowded with specialized biological components, including macromolecules. Those components communicate and otherwise interact to conduct the business of life. But we can’t yet explain, never mind mimic, precisely how it all works.
Now two Georgia Institute of Technology researchers appear to have pushed the mission forward. Using advanced computer simulations, Jeffrey Skolnick and Tadashi Ando have identified hydrodynamic interactions as a key factor influencing movement—more specifically diffusion—inside cells.
When a molecule moves, the cellular fluid surrounding it gets disturbed. That affects the movement of other molecules, the pair concluded, just as one boat’s wake affects the motion of another craft traveling in the same lake.
“It is exactly like a wake. If you start to move a solid through a liquid, you create a solvent flow. Imagine a very crowded day on Chesapeake Bay and all the sailboats are out. If you have ever been there you’ve seen that they interfere with one another. What’s remarkable is that these interactions persist almost to the molecular level,” says Skolnick, director of the Center for the Study of Systems Biology at Georgia Tech.
Skolnick and Ando, a postdoctoral scientist, reached this understanding after exploring a discrepancy. It has been long observed that macromolecules diffuse more slowly in native cytoplasm than they do in water, even though their viscosity is nearly the same. In at least one well-studied case, that of the molecular-laboratory workhorse green fluorescent protein (GFP), the rate in cytoplasm is ten times slower than the rate in water.
The Georgia Tech scientists knew that proteins, nucleic acids and other macromolecules typically occupy 20 to 40 percent of cytoplasm volume. And they assumed that a physical principle played a role in the diffusion disparity there. “We deeply believe that the principles of physics should work for biology,” Skolnick says. But they didn’t know what principle counted most.
So they developed a simplified computer model of an E. coli cytoplasm populated by 15 different macromolecule types. Then, based on measurements of the molecules’ physical properties detailed in the scientific literature, they calculated the likely effects of multiple natural forces on those macromolecules. That included estimates of attraction and repulsion between molecules, the effect of their different shapes, and their hydrodynamic interactions. Crowding alone drops the diffusion constant of GFP by a factor of three. Then, by far, the influence of hydrodynamics—which produced size-independent intermolecular effects—was the biggest. When it was considered too, the green fluorescent protein’s diffusion constant matched the rate in vivo.
The pair’s observation, published in October in the Proceedings of the National Academy of Sciences of the U.S.A., will be useful to other scientists trying to build dependable predictions of how proteins in cells interact, says David Thirumalai, director of the Biophysics Program in the Institute for Physical Science and Technology at the University of Maryland. Before anyone can estimate the timing of those interactions, it’s vital to know how quickly cells’ components can move, he says.
“Jeff and [Tadashi] have shown that to compute this stuff, you must take into account the effects of the hydrodynamic interactions. This has been known to people doing fluid dynamics for a long time. In the context of cellular biology, this helps set time scales for biological processes,” Thirumalai says. “One must know how to compute the diffusion constants to make the next step and predict how quickly some reactions will take place.”
For Skolnick and Thirumalai, findings such as these also contribute to a much larger goal: the drive to more realistically simulate whole cells with computers, or “in silico.” Multiple laboratories worldwide are chasing that very thing. Skolnick for years has sought ways to predict the structure of biologically active forms of proteins. Thirumalai is hunting for general principles that govern the folding of biomolecules.
Biologists have made huge strides in recent decades in producing “the parts list” of molecular life, Skolnick says. The challenge now is to accurately place those parts into a dynamic view of the components interacting with one another.
Once that’s done, Skolnick says, researchers can ask lots of new questions, knowing, of course, that they are working with caricatures of real cells. Still, better understanding may be coming of the mechanics of evolution, he says, or just how normal cells transform into cancer cells, among many other things.
“You have the possibility of looking at a lot of life,” Skolnick says.
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