Living organisms consist to a large extent of carbon (C) and nitrogen (N) compounds. These have to be taken in with food or, in the case of plants, produced through photosynthesis.

A previously mysterious extension of a starch-degrading enzyme in algae could be a kind of sensor to determine how much nitrogen is currently available. If there is plenty of it, the algal cells quickly release many building blocks for their growth.

The research team led by Dr. Anja Hemschemeier and Dr. Lisa Scholtysek from the Photobiotechnology Group at Ruhr University Bochum, Germany, report their new findings in the journal Plant Direct.

A starch-degrading biocatalyst as a nitrogen sensor

The optimal composition of a living cell is made up of a certain ratio of C and N, but the quantities of these elements in our diet and in the environment of plants and algae are usually not that perfectly balanced. Therefore, living organisms must tune their metabolism and chemical composition to the availability of these—and other—chemical building blocks.

In plant-like organisms, C-containing molecules that are not immediately utilized are stored as starch. Various types of biocatalysts—also termed enzymes—release C skeletons from starch when they are needed as building blocks or as energy source. One of these enzymes is alpha-amylase, which Hemschemeier’s research team investigated from the microalga Chlamydomonas reinhardtii.

In the process, the team made a surprising discovery. “The enzyme has an extension that is not needed for starch degradation,” explains Hemschemeier, who headed the study. “This protein part has already been discovered in a similar form in many different enzymes, where it usually regulates the function of the biocatalyst.

“Commonly, this protein part senses small compounds that play a role in the corresponding metabolic pathway, so that its speed can be adjusted and coordinated with other pathways.”

Lisa Scholtysek, lead author of the study, tested the effect of many different substances on the activity of this amylase. Finally, she identified one that noticeably increased the activity of the enzyme, namely the amino acid glutamine.

This N-containing compound is a building block of proteins. In many organisms, glutamine is also the first product of N assimilation and serves both as the primary N source and as a signal for how much N is available for biosynthetic pathways.

An alpha-amylase as growth booster?

To date, this combination of starch-degrading enzyme and glutamine sensor has not been described in literature. Still, based on bioinformatic analyses conducted by the researchers, many microalgae appear to possess this specific combination.

“Our research is still in its infancy,” says Hemschemeier. “So far, we have studied this effect only at the level of the isolated biocatalyst from Chlamydomonas. An important next step is to study it in living algae.”

However, the researchers have a hypothesis. “It is conceivable that this alpha-amylase registers when a lot of nitrogen is present. Then, it accelerates the release of C scaffolds from starch for the production of N- and C-containing cell components.” This could optimize cell growth when the algae encounter optimal conditions.

New research from the University of Massachusetts Amherst shows that programming robots to create their own teams and voluntarily wait for their teammates results in faster task completion, with the potential to improve manufacturing, agriculture and warehouse automation.

This research was recognized as a finalist for Best Paper Award on Multi-Robot Systems at the IEEE International Conference on Robotics and Automation 2024.

“There’s a long history of debate on whether we want to build a single, powerful humanoid robot that can do all the jobs, or we have a team of robots that can collaborate,” says one of the study authors, Hao Zhang, associate professor in the UMass Amherst Manning College of Information and Computer Sciences and director of the Human-Centered Robotics Lab.

In a manufacturing setting, a robot team can be less expensive because it maximizes the capability of each robot. The challenge then becomes: how do you coordinate a diverse set of robots? Some may be fixed in place, others mobile; some can lift heavy materials, while others are suited to smaller tasks.

As a solution, Zhang and his team created a learning-based approach for scheduling robots called learning for voluntary waiting and subteaming (LVWS).

“Robots have big tasks, just like humans,” says Zhang. “For example, they have a large box that cannot be carried by a single robot. The scenario will need multiple robots to collaboratively work on that.”

The other behavior is voluntary waiting. “We want the robot to be able to actively wait because, if they just choose a greedy solution to always perform smaller tasks that are immediately available, sometimes the bigger task will never be executed,” Zhang explains.

To test their LVWS approach, they gave six robots 18 tasks in a computer simulation and compared their LVWS approach to four other methods. In this computer model, there is a known, perfect solution for completing the scenario in the fastest amount of time.

The researchers ran the different models through the simulation and calculated how much worse each method was compared to this perfect solution, a measure known as suboptimality.

The comparison methods ranged from 11.8% to 23% suboptimal. The new LVWS method was 0.8% suboptimal. “So the solution is close to the best possible or theoretical solution,” says Williard Jose, an author on the paper and a doctoral student in computer science at the Human-Centered Robotics Lab.

How does making a robot wait make the whole team faster? Consider this scenario: You have three robots—two that can lift four pounds each and one that can lift 10 pounds. One of the small robots is busy with a different task and there is a seven-pound box that needs to be moved.

“Instead of that big robot performing that task, it would be more beneficial for the small robot to wait for the other small robot and then they do that big task together because that bigger robot’s resource is better suited to do a different large task,” says Jose.

If it’s possible to determine an optimal answer in the first place, why do robots even need a scheduler? “The issue with using that exact solution is to compute that it takes a really long time,” explains Jose. “With larger numbers of robots and tasks, it’s exponential. You can’t get the optimal solution in a reasonable amount of time.”

When looking at models using 100 tasks, where it is intractable to calculate an exact solution, they found that their method completed the tasks in 22 timesteps compared to 23.05 to 25.85 timesteps for the comparison models.

Zhang hopes this work will help further the progress of these teams of automated robots, particularly when the question of scale comes into play. For instance, he says that a single, humanoid robot may be a better fit in the small footprint of a single-family home, while multi-robot systems are better options for a large industry environment that requires specialized tasks.