Visualizing Biological Networks in 4D

Every great structure, from the Empire State Building to the Golden Gate Bridge, depends on specific mechanical properties to remain strong and reliable. Rigidity—a material’s stiffness—is of particular importance for maintaining the robust functionality of everything from colossal edifices to the tiniest of nanoscale structures. In biological nanostructures, like DNA networks, it has been difficult to measure this stiffness, which is essential to their properties and functions. But scientists at the California Institute of Technology (Caltech) have recently developed techniques for visualizing the behavior of biological nanostructures in both space and time, allowing them to directly measure stiffness and map its variation throughout the network.

The new method is outlined in the February 4 early edition of the Proceedings of the National Academy of Sciences (PNAS).

"This type of visualization is taking us into domains of the biological sciences that we did not explore before," says Nobel Laureate Ahmed Zewail, the Linus Pauling Professor of Chemistry and professor of physics at Caltech, who coauthored the paper with Ulrich Lorenz, a postdoctoral scholar in Zewail’s lab. "We are providing the methodology to find out—directly—the stiffness of a biological network that has nanoscale properties."

Knowing the mechanical properties of DNA structures is crucial to building sturdy biological networks, among other applications. According to Zewail, this type of visualization of biomechanics in space and time should be applicable to the study of other biological nanomaterials, including the abnormal protein assemblies that underlie diseases like Alzheimer’s and Parkinson’s.

Zewail and Lorenz were able to see, for the first time, the motion of DNA nanostructures in both space and time using the four-dimensional (4D) electron microscope developed at Caltech’s Physical Biology Center for Ultrafast Science and Technology. The center is directed by Zewail, who created it in 2005 to advance understanding of the fundamental physics of chemical and biological behavior.

Cornell Engineers Solve a Biological Mystery and Boost Artificial Intelligence

By simulating 25,000 generations of evolution within computers, Cornell University engineering and robotics researchers have discovered why biological networks tend to be organized as modules – a finding that will lead to a deeper understanding of the evolution of complexity.

The new insight also will help evolve artificial intelligence, so robot brains can acquire the grace and cunning of animals.

From brains to gene regulatory networks, many biological entities are organized into modules – dense clusters of interconnected parts within a complex network. For decades biologists have wanted to know why humans, bacteria and other organisms evolved in a modular fashion. Like engineers, nature builds things modularly by building and combining distinct parts, but that does not explain how such modularity evolved in the first place. Renowned biologists Richard Dawkins, Günter P. Wagner, and the late Stephen Jay Gould identified the question of modularity as central to the debate over “the evolution of complexity.”

For years, the prevailing assumption was simply that modules evolved because entities that were modular could respond to change more quickly, and therefore had an adaptive advantage over their non-modular competitors. But that may not be enough to explain the origin of the phenomena.

The team discovered that evolution produces modules not because they produce more adaptable designs, but because modular designs have fewer and shorter network connections, which are costly to build and maintain. As it turned out, it was enough to include a “cost of wiring” to make evolution favor modular architectures.

This theory is detailed in “The Evolutionary Origins of Modularity,” published today in the Proceedings of the Royal Society by Hod Lipson, Cornell associate professor of mechanical and aerospace engineering; Jean-Baptiste Mouret, a robotics and computer science professor at Université Pierre et Marie Curie in Paris; and by Jeff Clune, a former visiting scientist at Cornell and currently an assistant professor of computer science at the University of Wyoming.