Machine learning algorithms that can predict yeast metabolism from its protein...
Machine learning algorithms that can predict yeast metabolism from its protein content have been developed by scientists at the Francis Crick Institute. The findings could provide a basis for brewers to have greater control over the flavour of their beer, and scientists to personalise treatments for metabolic disorder patients.
Source: Rainis Venta

Machine learning predicts metabolism, helping drug developers

Scientists at The Francis Crick Institute have developed machine learning algorithms that can predict yeast metabolism from its protein content. The findings could provide a basis for scientists to personalise treatments for metabolic disorder patients – or help brewers to have greater control over the flavour of their beer.

Metabolism is the process by which organisms convert nutrients into energy and essential molecules, via a series of chemical reactions. Within a cell, metabolism produces hundreds of small molecules, called metabolites. Although yeast is evolutionarily very distant to humans, many of these metabolites are identical, and are made in a similar way. Until now, however, the mechanisms controlling metabolism have not been fully understood. “Thanks to machine learning, we now have a better understanding of what controls metabolism. That is good news for biotechnologists that use yeast to produce vaccines and other proteins that are medically important,” says Aleksej Zelezniak, first author of the paper.

Until now, scientists have been divided over whether metabolism is self-regulating or controlled by gene expression changes; partly because existing methods have failed to detect any strong correlation between the read-out of genes – proteins – and metabolites.

The scientists developed machine learning algorithms that could pick up complex relationships between changes in gene expression and metabolites produced. They found that metabolism was controlled by lots of enzymes acting in concert – with no single enzyme having a major effect by itself. “The relationship between enzyme expression and metabolism in yeast is so complex that previous models have failed to detect it,” says Markus Ralser, head of the Molecular Biology of Metabolism Lab at the Crick and senior author of the paper. “Changes in cellular metabolism are tightly bound to disorders that increase with age, including diabetes, various types of cancer, and neurodegenerative diseases. The fact that one can start to predict metabolism in simple cells like yeast cells, is a milestone for the effort to soon be able to predict metabolism also in human tissues.

“Similar computational tools are used by tech giants like Amazon and Facebook all the time. But instead of using them to tailor advertisements or recommend friends, we’ve harnessed their power to predict a yeast cell’s metabolism. These insights not only inform our understanding of the basis of beer flavouring, but also some human disorders of metabolism.”

From beer to personalised medicine

The team is hoping to transfer their findings in yeast cells to the clinic in the next few years to help patients with metabolic diseases. “For non-biologists it might seem strange that one can transfer our knowledge of yeast to humans, but in reality, many fundamental principles of what we know about human biology came from yeast research,” says Zelezniak. “We currently expand our algorithms, so that they will provide us with information also about a person’s metabolism, based on which proteins are present in their blood. This information could help doctors decide which treatment option is best for an individual patient.”

Subscribe to our newsletter

Related articles

Using AI to find new uses for existing medications

Using AI to find new uses for existing medications

Scientists have developed a machine learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed.

Network medicine makes drug repurposing effective

Network medicine makes drug repurposing effective

Artificial intelligence can increase the effectiveness of drug repositioning or repurposing research.

'Deepfaking the mind' to improve brain-computer interfaces

'Deepfaking the mind' to improve brain-computer interfaces

Researchers are using generative adversarial networks to improve brain-computer interfaces for people with disabilities.

ReSkin helps to discover a sense of touch

ReSkin helps to discover a sense of touch

Carnegie Mellon University and Meta AI (formerly Facebook AI) want to increase the sense of touch in robotics, wearables, smart clothing and AI.

AI Eve augments genetic tests

AI Eve augments genetic tests

AI model called EVE shows remarkable capacity to interpret the meaning of gene variants in humans as benign or disease-causing.

An AI that ‘thinks’ like humans

An AI that ‘thinks’ like humans

Creating human-like AI is about more than mimicking human behaviour – technology must also be able to process information, or ‘think’, if it is to be fully relied upon.

Enabling AI-driven advances without sacrificing privacy

Enabling AI-driven advances without sacrificing privacy

Secure AI Labs is expanding access to encrypted health care data to advance AI-driven innovation in the field.

Monitoring mental health in cancer survivors with "FAITH"

Monitoring mental health in cancer survivors with "FAITH"

AI-based solution FAITH is designed to monitor the mental health status of people who have undergone cancer treatment.

Researchers psychoanalyse artificial intelligence

Researchers psychoanalyse artificial intelligence

We can run tests and experiments, but we cannot always predict and understand why AI does what it does.

Popular articles

Subscribe to Newsletter