Tyler O'Neal, Staff Editor

Dutch supercomputer simulations show fundamental interactions inside the cell

A first clear picture of actin-binding to cell membrane lipids

Actin filaments have several important functions inside cells. For one, they support the cell membrane by binding to it. However, scientists did not know exactly how the actin interacts with the membrane lipids. Supercomputer simulations performed at the University of Groningen, a public research university in the city of Groningen in the Netherlands, supported by experiments, provide a molecular view on this very fundamental process. The results were published in an academic journal.

Actin filaments are involved in many important processes in eukaryotic cells, from motility to division to the contraction of muscle fibers. Actin can also form a network underneath the lipid bilayer. Here, it provides additional support for this structure, while curved filaments also play a role in cell division, when the membrane needs to constrict. Despite its importance, the molecular processes underlying the binding of actin to cell membranes is still not clear.

Supercomputer simulations CAPTION Simulation of actin-binding to the lipid membrane with negatively charged lipids (orange). Blue color indicates calcium concentrations. CREDIT S-J Marrink, University of Groningen{module INSIDE STORY}

'The literature provides conflicting results,' says the University of Groningen Professor of Molecular Dynamics Siewert-Jan Marrink. "ll agree that actin, which is negatively charged, could bind to positively charged lipids and some say that they can even bind to membranes with no positive or even a negative charge." The latter is relevant for the biological situation as normal cell membranes carry a negative charge on the inside.

Marrink and his colleagues, therefore, performed molecular dynamics simulations of the interaction between lipids and actin. They used the Martini coarse-grained forcefield, which was developed by Marrink and is now used worldwide.

Molecular glue

By varying the components in the simulation, the scientists discovered that the ions that are present define the binding process. Marrink: 'Actin could only bind to negatively charged lipids in the presence of calcium ions. The two positive charges of calcium act as a kind of molecular glue.' In contrast, in the presence of positively charged lipids, calcium ions inhibited the binding of actin. The results of this simulation were confirmed by experiments performed in Professor Gijsje Koenderink's lab at the Delft University of Technology.

'The concentrations of calcium that were required in the simulations are higher than you would find inside cells,' says Marrink. "However, calcium ions tend to bind to membrane lipids, so the local concentration could be high enough."

Synthetic cell

The results provide the first clear picture of actin-binding to membrane lipids. "We want to use this in a national effort to build an artificial cell,' Marrink explains. The Dutch BaSyC (Building a Synthetic Cell) project involves many different steps and one of them is constructing membranes. 'These need to be reinforced with actin, so we have to understand how to control the interaction between the filaments and the lipid membrane. And we need to guide division of the artificial cell, where actin is needed for constriction."

Pitt engineer wins $500K NSF Career Award for advancing AI neural network technology

Tyler O'Neal, Staff Editor

In science fiction stories from "I, Robot" to "Star Trek," an android's "positronic brain" enables it to function as a human, but with tremendously more processing power and speed. In reality, the opposite is true: a human brain - which today is still more proficient than CPUs at cognitive tasks like pattern recognition - needs only 20 watts of power to complete a task, while a supercomputer requires more than 50,000 times that amount of energy.

For that reason, researchers are turning to a neuromorphic computer and artificial neural networks that work more like the human brain. However, with current technology, it is both challenging and expensive to replicate the Spatio-temporal processes native to the brain, like short-term and long-term memory, in artificial spiking neural networks (SNN).

Feng Xiong, PhD, assistant professor of electrical and computer engineering at the University of Pittsburgh's Swanson School of Engineering, received a $500,000 CAREER Award from the National Science Foundation (NSF) for his work developing the missing element, a dynamic synapse, that will dramatically improve energy efficiency, bandwidth and cognitive capabilities of SNNs. Feng Xiong, Ph.D., assistant professor of electrical and computer engineering.{module INSIDE STORY}

"When the human brain sees rain and then feels wetness, or sees fire and feels the heat, the brain's synapses link the two ideas, so in the future, it will associate rain with wetness and fire with warmth. The two ideas are strongly linked in the brain," explains Xiong. "Computers, on the other hand, need to be fed massive datasets to do the same task. Our dynamic synapse would mimic the brain's ability to create neuronal connections as a function of the timing differences between stimulations, significantly improving the energy efficiency required to perform a task."

Current non-volatile memory devices that have been studied for use as artificial synapses in SNNs haven't measured up: they are designed to retain data permanently and aren't suited for the Spatio-temporal dynamics and high precision that the human brain is capable of. In the brain, it's not only the information that matters but also the timing of the information--for example, in some situations, the closer two pieces of information are in time, the stronger the synaptic strand between them.

By programming the conductor to conduct more electricity for a stronger neural connection, it can function more like the synapses of the human brain, giving more weight to items that are more closely linked as it learns.

"The resulted change in the electrical conductance (representing the synaptic weight or the synaptic connection strength) in the dynamic synapse will have both a short-term and a long-term component, mimicking the short-term and long-term memory/learning in the human brain," says Xiong.

Though researchers have demonstrated this kind of technology before in the lab, this project is the first time it will be applied to an SNN. The application could lead to the wide use of AI and revolutionary advances in cognitive computing, self-driving vehicles, and autonomous manufacturing.

In addition to the research component of the project, Xiong will use the opportunity to engage future engineers in his research. He plans to develop an after-school outreach program, host nanotech workshops with the Pennsylvania Junior Academy of Science, and welcome undergraduate engineering majors at Pitt to engage with the research.

The project is titled "Scalable Ionic Gated 2D Synapse (IG-2DS) with Programmable Spatio-Temporal Dynamics for Spiking Neural Networks" and will begin on March 1, 2020.

Carnegie Mellon engineers build AI agents to construct useful new designs using visual cues as humans do

Tyler O'Neal, Staff Editor

Trained AI agents can adopt human design strategies to solve problems, according to findings published in the ASME Journal of Mechanical Design.

Big design problems require creative and exploratory decision making, a skill in which humans excel. When engineers use artificial intelligence (AI), they have traditionally applied it to a problem within a defined set of rules rather than having it generally follow human strategies to create something new. This novel research considers an AI framework that learns human design strategies through observation of human data to generate new designs without explicit goal information, bias, or guidance.

The study was co-authored by Jonathan Cagan, professor of mechanical engineering and interim dean of Carnegie Mellon University's College of Engineering, Ayush Raina, a Ph.D. candidate in mechanical engineering at Carnegie Mellon, and Chris McComb, an assistant professor of engineering design at the Pennsylvania State University.

"The AI is not just mimicking or regurgitating solutions that already exist," said Cagan. "It's learning how people solve a specific type of problem and creating new design solutions from scratch." How good can AI be? "The answer is quite good." A photo of a bridge{module INSIDE STORY}

The study focuses on truss problems because they represent complex engineering design challenges. Commonly seen in bridges, a truss is an assembly of rods forming a complete structure. The AI agents were trained to observe the progression in design modification sequences that had been followed in creating a truss based on the same visual information that engineers use--pixels on a screen--but without further context. When it was the agents' turn to design, they imagined design progressions that were similar to those used by humans and then generated design moves to realize them. The researchers emphasized visualization in the process because vision is an integral part of how humans perceive the world and go about solving problems.

The framework was made up of multiple deep neural networks that worked together in a prediction-based situation. Using a neural network, the AI looked through a set of five sequential images and predicted the next design using the information it gathered from these images.

"We were trying to have the agents create designs similar to how humans do it, imitating the process they use: how they look at the design, how they take the next action and then create a new design, step by step," said Raina.

The researchers tested the AI agents on similar problems and found that on average, they performed better than humans. Yet, this success came without many of the advantages humans have available when they are solving problems. Unlike humans, the agents were not working with a specific goal (like making something lightweight) and did not receive feedback on how well they were doing. Instead, they only used the vision-based human strategy techniques they had been trained to use.

"It's tempting to think that this AI will replace engineers, but that's simply not true," said McComb. "Instead, it can fundamentally change how engineers work. If we can offload boring, time-consuming tasks to an AI, as we did in the work, then we free engineers up to think big and solve problems creatively."