(BURLINGTON, Vermont) – To persist, life must reproduce. Over billions of years, organisms have evolved many ways of replicating, from budding plants to sexual animals to invading viruses.
Now scientists at the University of Vermont, Tufts University, and the Wyss Institute for Biologically Inspired Engineering at Harvard University have discovered an entirely new form of biological reproduction—and applied their discovery to create the first-ever, self-replicating living robots.
“Our woolly mouse project drove innovations in areas combining the end to end process from our computational biology analysis tools to our multiplex precision genome engineering technologies,” Lamm told us. “These technologies enable precise and efficient genetic modifications at multiple sites within the genome at the same time, which could help with research focused on addressing the complex multi-genetic age-related diseases in the future.”
By further refining the genetic engineering techniques developed by Colossal, researchers may eventually develop therapies tailored to an individual’s genetic makeup, mitigating the effects of aging at a cellular level.
“Many diseases are multigenic in nature and require deep analysis computationally and being able to edit the genome at multiple sites with high degrees of efficiency to not cause off-target effects,” Lamm told us. “Our end to end process and the further development of our multiplex editing and DNA synthesis capabilities will lead to others being able to use our tools and system to treat these more complicated diseases. Together, these innovations are part of the science focused on developing personalized, targeted therapies to mitigate the effects of aging, accelerate the development of regenerative medicine, and extend both lifespan and healthspan.”
A plan to revive the mammoth is on track, scientists have said after creating a new species: the woolly mouse.
Scientists at the US biotechnology company Colossal Biosciences plan to “de-extinct” the prehistoric pachyderms by genetically modifying Asian elephants to give them woolly mammoth traits. They hope the first calf will be born by the end of 2028.
A team of researchers at the George R. Brown School of Engineering and Computing at Rice University has developed an innovative artificial intelligence (AI)-enabled, low-cost device that will make flow cytometry—a technique used to analyze cells or particles in a fluid using a laser beam—affordable and accessible.
The prototype identifies and counts cells from unpurified blood samples with similar accuracy as the more expensive and bulky conventional flow cytometers, provides results within minutes and is significantly cheaper and compact, making it highly attractive for point-of-care clinical applications, particularly in low-resource and rural areas.
Peter Lillehoj, the Leonard and Mary Elizabeth Shankle Associate Professor of Bioengineering, and Kevin McHugh, assistant professor of bioengineering and chemistry, led the development of this new device. The study was published in Microsystems & Nanoengineering.
A platform developed nearly 20 years ago previously used to detect protein interactions with DNA and conduct accurate COVID-19 testing has been repurposed to create a highly sensitive water contamination detection tool.
The technology merges two exciting fields—synthetic biology and nanotechnology—to create a new platform for chemical monitoring. When tuned to detect different contaminants, the technology could detect the metals lead and cadmium at concentrations down to two and one parts per billion, respectively, in a matter of minutes.
The paper was published this week in the journal ACS Nano and represents research from multiple disciplines within Northwestern’s McCormick School of Engineering.
A new algorithm, Evo 2, trained on roughly 128,000 genomes—9.3 trillion DNA letter pairs—spanning all of life’s domains, is now the largest generative AI model for biology to date. Built by scientists at the Arc Institute, Stanford University, and Nvidia, Evo 2 can write whole chromosomes and small genomes from scratch.
It also learned how DNA mutations affect proteins, RNA, and overall health, shining light on “non-coding” regions, in particular. These mysterious sections of DNA don’t make proteins but often control gene activity and are linked to diseases.
The team has released Evo 2’s software code and model parameters to the scientific community for further exploration. Researchers can also access the tool through a user-friendly web interface. With Evo 2 as a foundation, scientists may develop more specific AI models. These could predict how mutations affect a protein’s function, how genes operate differently across cell types, or even help researchers design new genomes for synthetic biology.
