Machine Learning Takes On Synthetic Biology: Algorithms Can Bioengineer Cells for Youhttps://phys.org/news/2020-09-machine-synthetic-biology-algorithms-bioengineer.htmlScientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it; instead, with a limited set of training data, the algorithms are able to predict how changes in a cell's DNA or biochemistry will affect its behavior, then make recommendations for the next engineering cycle along with probabilistic predictions for attaining the desired goal.
"The possibilities are revolutionary," said Hector Garcia Martin, a researcher in Berkeley Lab's Biological Systems and Engineering (BSE) Division who led the research. "Right now, bioengineering is a very slow process. It took 150 person-years to create the anti-malarial drug, artemisinin. If you're able to create new cells to specification in a couple weeks or months instead of years, you could really revolutionize what you can do with bioengineering."
Working with BSE data scientist Tijana Radivojevic and an international group of researchers, the team developed and demonstrated a patent-pending algorithm called the Automated Recommendation Tool (ART), described in a pair of papers recently published in the journal
Nature Communications.In "ART: A machine learning Automated Recommendation Tool for synthetic biology," led by Radivojevic, the researchers presented the algorithm, which is tailored to the particularities of the synthetic biology field: small training data sets, the need to quantify uncertainty, and recursive cycles. The tool's capabilities were demonstrated with simulated and historical data from previous metabolic engineering projects, such as improving the production of renewable biofuels.
The researchers say they were surprised by how little data was needed to obtain results. Yet to truly realize synthetic biology's potential, they say the algorithms will need to be trained with much more data. Garcia Martin describes synthetic biology as being only in its infancy—the equivalent of where the Industrial Revolution was in the 1790s. "It's only by investing in automation and high-throughput technologies that you'll be able to leverage the data needed to really revolutionize bioengineering," he said.
Radivojevic added: "We provided the methodology and a demonstration on a small dataset; potential applications might be revolutionary given access to large amounts of data."
... "This is a clear demonstration that bioengineering led by machine learning is feasible, and disruptive if scalable. We did it for five genes, but we believe it could be done for the full genome." ... "This is just the beginning. With this, we've shown that there's an alternative way of doing metabolic engineering. Algorithms can automatically perform the routine parts of research while you devote your time to the more creative parts of the scientific endeavor: deciding on the important questions, designing the experiments, and consolidating the obtained knowledge."
Tijana Radivojević, et.al.,
A machine learning Automated Recommendation Tool for synthetic biology,
Nature Communications, (2020)
https://www.nature.com/articles/s41467-020-18008-4------------------------------------
Scientists Persuade Nature to Make Silicon-Carbon Bondshttps://phys.org/news/2016-11-scientists-nature-silicon-carbon-bonds.htmlA new study is the first to show that living organisms can be persuaded to make silicon-carbon bonds—something only chemists had done before. Scientists at Caltech "bred" a bacterial protein to make the man-made bonds—a finding that has applications in several industries.
The study is also the first to show that nature can adapt to incorporate silicon into carbon-based molecules, the building blocks of life. Scientists have long wondered if life on Earth could have evolved to be based on silicon instead of carbon. Science-fiction authors likewise have imagined alien worlds with silicon-based life, like the lumpy Horta creatures portrayed in an episode of the 1960s TV series Star Trek. Carbon and silicon are chemically very similar. They both can form bonds to four atoms simultaneously, making them well suited to form the long chains of molecules found in life, such as proteins and DNA.
Directed Evolution of Cytochrome c for Carbon-Silicon Bond Formation: Bringing Silicon to Life,"
Sciencehttps://science.sciencemag.org/content/354/6315/1048-----------------------------
Scientists Create First Stable Semisynthetic Organismhttps://phys.org/news/2017-01-scientists-stable-semisynthetic.htmlScientists at The Scripps Research Institute (TSRI) have announced the development of the first stable semisynthetic organism. Building on their 2014 study in which they synthesized a DNA base pair, the researchers created a new bacterium that uses the four natural bases (called A, T, C and G), which every living organism possesses, but that also holds as a pair two synthetic bases called X and Y in its genetic code.
TSRI Professor Floyd Romesberg and his colleagues have now shown that their single-celled organism can hold on indefinitely to the synthetic base pair as it divides. Their research was published January 23, 2017, online ahead of print in the journal Proceedings of the National Academy of Sciences.
Next, the researchers plan to study how their new genetic code can be transcribed into RNA, the molecule in cells needed to translate DNA into proteins. "This study lays the foundation for what we want to do going forward," said Zhang.
http://www.pnas.org/content/early/2017/01/17/1616443114