Category: AI/Robotics

Bioengineers combined a Lego Mindstorms system (left) with a motorized pipette (center) for dropping fluids, allowing for simple experiments like showing how liquids of different salt densities can be layered. (credit: Riedel-Kruse Lab)

Stanford bioengineers have developed liquid-handling robots to allow students to modify and create their own robotic systems that can transfer precise amounts of fluids between flasks, test tubes, and experimental dishes.

The bioengineers combined a Lego Mindstorms robotics kit with a cheap and easy-to-find plastic syringe to create robots that approach the performance of the far more costly automation systems found at universities and biotech labs.

Step-by-step DIY plans

Children 10–13 years old built and explored the functionality of these robots by performing experiments (credit: Lukas C. Gerber et al./PloS Biology)

The idea is to enable students to learn the basics of robotics and the wet sciences in an integrated way. Students learn STEM skills like mechanical engineering, computer programming, and collaboration while gaining a deeper appreciation of the value of robots in life-sciences experiments.

“We really want kids to learn by doing,” said Ingmar Riedel-Kruse, assistant professor of bioengineering and a member of Stanford Bio-X, who led the team. “We show that with a few relatively inexpensive parts, a little training and some imagination, students can create their own liquid-handling robots and then run experiments on it — so they learn about engineering, coding, and the wet sciences at the same time.”

In an open-access paper in the journal PLoS Biology and on Riedel-Kruse’s lab website, the team offers step-by-step building plans and several fundamental experiments targeted to elementary, middle and high school students. They also offer experiments that students can conduct using common household consumables like food coloring, yeast or sugar.

In one experiment, colored liquids with distinct salt concentrations are layered atop one another to teach about liquid density. Other tests measure whether liquids are acids like vinegar or bases like baking soda, or which sugar concentration is best for yeast.

Funding was provided by grants from the National Science Foundation (Cyberlearning and National Robotics Initiative).

Stanford University School of Engineering | SFENG Robots Riedel Kruse v4

Abstract of Liquid-handling Lego robots and experiments for STEM education and research

Liquid-handling robots have many applications for biotechnology and the life sciences, with increasing impact on everyday life. While playful robotics such as Lego Mindstorms significantly support education initiatives in mechatronics and programming, equivalent connections to the life sciences do not currently exist. To close this gap, we developed Lego-based pipetting robots that reliably handle liquid volumes from 1 ml down to the sub-μl range and that operate on standard laboratory plasticware, such as cuvettes and multiwell plates. These robots can support a range of science and chemistry experiments for education and even research. Using standard, low-cost household consumables, programming pipetting routines, and modifying robot designs, we enabled a rich activity space. We successfully tested these activities in afterschool settings with elementary, middle, and high school students. The simplest robot can be directly built from the widely used Lego Education EV3 core set alone, and this publication includes building and experiment instructions to set the stage for dissemination and further development in education and research.

Japanese researchers have developed an amoeba-like shape-changing molecular robot — assembled from biomolecules such as DNA, proteins, and lipids — that could act as a programmable and controllable robot for treating live culturing cells or monitoring environmental pollution, for example.

This the first time a molecular robotic system can recognize signals and control its shape-changing function, and their molecular robots could in the near future function in a way similar to living organisms, according to the researchers.

Developed by a research group at Tohoku University and Japan Advanced Institute of Science and Technology, the molecular robot integrates molecular machines within an artificial cell membrane and is about one micrometer in diameter — similar in size to human cells. It can start and stop its shape-changing function in response to a specific DNA signal.

Schematic diagram of the molecular robot. (A) In response to a start-stop DNA signal, molecular actuators (microtubules) inside the robot change the shape of the artificial cell membrane (liposome), controlled by a “molecular clutch” that transmits the force from the actuator (kinesin proteins, shown in green, assemble DNA to the cell membrane when activated). (B) Microscopy images of molecular robots. When the input DNA signal is “stop,” the clutch is turned “OFF,” deactivating the shape-changing behavior. The shape-changing is activated when the the clutch is turned “ON.” Scale bar: 20 μm. The white arrow indicates the molecular actuator part that transforms the shape of the membrane. (credit: Yusuke Sato)

The movement force is generated by molecular actuators (microtubules) controlled by a molecular clutch (composed of DNA and kinesin — a “walker” that carries molecules along microtubules in the body). The shape of the robot’s body (artificial cell membrane, or liposome — a vesicle made from a lipid bilayer) is changed (from static to active) by the actuator, triggered by specific DNA signals activated by UV irradiation.

Kinesin motor protein “walking” along microtubule filament (credit: Jzp706/CC)

The realization of a molecular robot whose components are designed at a molecular level and that can function in a small and complicated environment, such as the human body, is expected to significantly expand the possibilities of robotics engineering, according to the researchers.*

“With more than 20 chemicals at varying concentrations, it took us a year and a half to establish good conditions for working our molecular robots,” says Associate Professor Shin-ichiro Nomura at Tohoku University’s Graduate School of Engineering, who led the study. “It was exciting to see the robot shape-changing motion through the microscope. It meant our designed DNA clutch worked perfectly, despite the complex conditions inside the robot.”

Programmable by DNA computing devices

The research results were published in an open-access paper in Science Robotics on March 1, 2017.

The authors say that “combining other molecular devices would lead to the realization of a molecular robot with advanced functions. For example, artificial nanopores, such as an artificial channel composed of DNA, could be used to sense signal molecules in the surrounding environments through the channel.

“In addition, the behavior of a molecular robot could be programmed by DNA computing devices, such as judging the condition of environments. These implementations could allow for the development of molecular robots capable of chemotaxis [movement in a direction corresponding to a gradient of increasing or decreasing concentration of a particular substance], [similar to] white blood cells, and beyond.”

The research was supported by the JSPS KAKENHI, AMED-CREST and Tohoku University-DIARE.

* In the current design, “there are still limitations in the functions of the robot. For example, the switching of robot behavior is not reversible. The shape change is not directional and as yet not possible for complex tasks, for example, locomotion. However, to the best of our knowledge, this is the first implementation of a molecular robot that can control its shape-changing behavior in response to specific signal molecules.” — Yusuke Sato et al./Science Robotics

Abstract of Micrometer-sized molecular robot changes its shape in response to signal molecules

Rapid progress in nanoscale bioengineering has allowed for the design of biomolecular devices that act as sensors, actuators, and even logic circuits. Realization of micrometer-sized robots assembled from these components is one of the ultimate goals of bioinspired robotics. We constructed an amoeba-like molecular robot that can express continuous shape change in response to specific signal molecules. The robot is composed of a body, an actuator, and an actuator-controlling device (clutch). The body is a vesicle made from a lipid bilayer, and the actuator consists of proteins, kinesin, and microtubules. We made the clutch using designed DNA molecules. It transmits the force generated by the motor to the membrane, in response to a signal molecule composed of another sequence-designed DNA with chemical modifications. When the clutch was engaged, the robot exhibited continuous shape change. After the robot was illuminated with light to trigger the release of the signal molecule, the clutch was disengaged, and consequently, the shape-changing behavior was successfully terminated. In addition, the reverse process—that is, initiation of shape change by input of a signal—was also demonstrated. These results show that the components of the robot were consistently integrated into a functional system. We expect that this study can provide a platform to build increasingly complex and functional molecular systems with controllable motility.

A new set of machine-learning algorithms can generate 3D structures of complex nanoscale protein molecules like this complex proteasome map refined to 2.8 Angstroms (.28 nanometer) in 70 min with 49,954 particle images (credit: Structura Biotechnology Inc.)

A new set of machine-learning algorithms developed by researchers at the University of Toronto Scarborough can generate 3D structures of nanoscale protein molecules that could not be achieved in the past. The algorithms may revolutionize the development of new drug therapies for a range of diseases and may even lead to better understand how life works at the atomic level, the researchers say.

Drugs work by binding to a specific protein molecule and changing the protein’s 3D shape, which alters the way the drug works once inside the body. The ideal drug is designed in a shape that will only bind to a specific protein or group of proteins that are involved in a disease, while eliminating side effects that occur when drugs bind to other proteins in the body.

A significant computational problem

Since proteins are tiny — about 1 to 100 nanometers — even smaller than the shortest wavelength of visible light, they can’t be seen directly without using sophisticated techniques like electron cryomicroscopy (cryo-EM). Cryo-EM uses high-power microscopes to take tens of thousands of low-resolution images of a frozen protein sample from different positions.

The computational problem is to then piece together the correct high-resolution 3D structure from these 2D images.

Existing techniques take several days or even weeks to generate a 3D structure on a cluster of computers, requiring as much as 500,000 CPU hours, according to the researchers. Also, existing techniques often generate incorrect structures unless an expert user provides an accurate guess of the molecule being studied.

CryoSPARC machine learning algorithms can generate 3-D structures of nanoscale protein molecules (credit: Structura Biotechnology Inc)

New high-speed, deep-learning algorithms

That’s where the new set of algorithms* comes in. It reconstructs 3D structures of protein molecules using these images. “Our approach solves some of the major problems in terms of speed and number of structures you can determine,” says Professor David Fleet, chair of the Computer and Mathematical Sciences Department at U of Toronto Scarborough.

The algorithms could significantly aid in the development of new drugs because they provide a faster, more efficient means at arriving at the correct protein structure.

The new approach, called cryoSPARC, developed by the team’s startup, Structura Biotechnology Inc., eliminates the need for that prior knowledge and can make the computations possible in minutes on a single computer, using a standalone graphics processing unit (GPU) accelerated software package, according to the researchers.

The research was published in the current edition of the journal Nature Methods. It received funding from the Natural Sciences and Engineering Research Council of Canada (NSERC). The new cryo-EM platform is already being used in labs across North America, the researchers note.

* “We use an SGD [stochastic gradient descent] optimization scheme to quickly identify one or several low-resolution 3D structures that are consistent with a set of observed images. This algorithm allows for ab initio heterogeneous structure determination with no prior model of the molecule’s structure. Once approximate structures are determined, a branch-and-bound algorithm for image alignment helps rapidly refine structures to high resolution. The speed and robustness of these approaches allow structure determination in a matter of minutes or hours on a single inexpensive desktop workstation. … SGD was popularized as a key tool in deep learning for the optimization of nonconvex functions, and it results in near human-level performance in tasks like image and speech recognition.” — Ali Punjani et al./Nature Methods

University of Toronto Scarborough | New algorithms may revolutionize drug discoveries and our understanding of life

Abstract of cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination

Single-particle electron cryomicroscopy (cryo-EM) is a powerful method for determining the structures of biological macromolecules. With automated microscopes, cryo-EM data can often be obtained in a few days. However, processing cryo-EM image data to reveal heterogeneity in the protein structure and to refine 3D maps to high resolution frequently becomes a severe bottleneck, requiring expert intervention, prior structural knowledge, and weeks of calculations on expensive computer clusters. Here we show that stochastic gradient descent (SGD) and branch-and-bound maximum likelihood optimization algorithms permit the major steps in cryo-EM structure determination to be performed in hours or minutes on an inexpensive desktop computer. Furthermore, SGD with Bayesian marginalization allows ab initio 3D classification, enabling automated analysis and discovery of unexpected structures without bias from a reference map. These algorithms are combined in a user-friendly computer program named cryoSPARC