For the first time, MIT researchers have performed a large-scale, high-resolution study of the cells in breast milk, allowing them to track how these cells change over time in nursing mothers.
By analyzing human breast milk produced between three days and nearly two years after childbirth, the researchers were able to identify a variety of changes in gene expression in mammary gland cells. Some of these changes were linked to factors such as hormone levels, illness of the mother or baby, the mother starting birth control, and the baby starting daycare.
“We were able to take this really long view of lactation that other studies haven’t really done, and we showed that milk does change over the entire course of lactation, even after years of milk production,” says Brittany Goods, a former MIT postdoc who is now an assistant professor of engineering at Dartmouth College, and one of the senior authors of the study… Continue reading.
Synthetic biology offers a way to engineer cells to perform novel functions, such as glowing with fluorescent light when they detect a certain chemical. Usually, this is done by altering cells so they express genes that can be triggered by a certain input.
However, there is often a long lag time between an event such as detecting a molecule and the resulting output, because of the time required for cells to transcribe and translate the necessary genes. MIT synthetic biologists have now developed an alternative approach to designing such circuits, which relies exclusively on fast, reversible protein-protein interactions. This means that there’s no waiting for genes to be transcribed or translated into proteins, so circuits can be turned on much faster — within seconds… Continue reading.
By bridging the conceptual divide between human language and viral evolution, MIT researchers have developed a powerful new computational tool for predicting the mutations that allow viruses to “escape” human immunity or vaccines. Its use could negate the need for high-throughput experimental techniques that are currently employed to identify potential mutations that could allow a virus to escape recognition. The computational model, based on models that were originally developed to analyze language, can predict which sections of viral surface proteins are more likely to mutate in a way that would enable viral escape, and it can also identify sections that are less likely to mutate, which would represent good targets for new vaccines.
“Viral escape is a big problem,” said Bonnie Berger, PhD, the Simons Professor of Mathematics and head of the Computation and Biology group at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory. “Viral escape of the surface protein of influenza and the envelope surface protein of HIV are both highly responsible for the fact that we don’t have a universal flu vaccine, nor do we have a vaccine for HIV, both of which cause hundreds of thousands of deaths a year… Continue reading.
On April 27, the National Academy of Sciences elected 120 new members and 26 international associates, including three professors from MIT — Abhijit Banerjee, Bonnie Berger, and Roger Summons — recognizing their “distinguished and continuing achievements in original research.” Current membership totals 2,403 active members and 501 international associates, including 190 Nobel Prize recipients.
The National Academy of Sciences is a private, nonprofit institution for scientific advancement established in 1863 by congressional charter and signed into law by President Abraham Lincoln. Together, with the National Academy of Engineering and the National Academy of Medicine, the 157-year-old society provides science, engineering, and health policy advice to the federal government and other organizations.
Bonnie Berger is the Simons Professor of Mathematics and holds a joint appointment in the Department of Electrical Engineering and Computer Science. She is the head of the Computation and Biology group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). She is also a faculty member of the Harvard-MIT Program in Health Sciences and Technology and an associate member of the Broad Institute of MIT and Harvard… Continue reading.
A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein’s function, which could help researchers design and test new proteins for drug development or biological research.
Proteins are linear chains of amino acids, connected by peptide bonds, that fold into exceedingly complex three-dimensional structures, depending on the sequence and physical interactions within the chain. That structure, in turn, determines the protein’s biological function. Knowing a protein’s 3-D structure, therefore, is valuable for, say, predicting how proteins may respond to certain drugs.
However, despite decades of research and the development of multiple imaging techniques, we know only a very small fraction of possible protein structures — tens of thousands out of millions. Researchers are beginning to use machine-learning models to predict protein structures based on their amino acid sequences, which could enable the discovery of new protein structures. But this is challenging, as diverse amino acid sequences can form very similar structures. And there aren’t many structures on which to train the models… Continue reading.
MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.
Datasets of drug-target interactions (DTI), which show whether candidate compounds act on target proteins, are critical in helping researchers develop new medications. Models can be trained to crunch datasets of known DTIs and then, using that information, find novel drug candidates.
In recent years, pharmaceutical firms, universities, and other entities have become open to pooling pharmacological data into larger databases that can greatly improve training of these models. Due to intellectual property matters and other privacy concerns, however, these datasets remain limited in scope. Cryptography methods to secure the data are so computationally intensive they don’t scale well to datasets beyond, say, tens of thousands of DTIs, which is relatively small… Continue reading.
Genome-wide association studies, which look for links between particular genetic variants and incidence of disease, are the basis of much modern biomedical research.
But databases of genomic information pose privacy risks. From people’s raw genomic data, it may be possible to infer their surnames and perhaps even the shapes of their faces. Many people are reluctant to contribute their genomic data to biomedical research projects, and an organization hosting a large repository of genomic data might conduct a months-long review before deciding whether to grant a researcher’s request for access.
In a paper appearing today in Nature Biotechnology, researchers from MIT and Stanford University present a new system for protecting the privacy of people who contribute their genomic data to large-scale biomedical studies. Where earlier cryptographic methods were so computationally intensive that they became prohibitively time consuming for more than a few thousand genomes, the new system promises efficient privacy protection for studies conducted over as many as a million genomes.
“As biomedical researchers, we’re frustrated by the lack of data and by the access-controlled repositories,” says Bonnie Berger, the Simons Professor of Mathematics at MIT and corresponding author on the paper… Continue reading.
WASHINGTON, D.C.— The American Institute for Medical and Biological Engineering (AIMBE) has announced the pending induction of Bonnie Berger, Ph.D., Professor of Applied Math and Computer Science at MIT, and head of the Computation and Biology group, , Massachusetts Institute of Technology, to its College of Fellows. Dr. Berger was nominated, reviewed, and elected by peers and members of the College of Fellows For outstanding research contributions to computational biology and mentoring of future bioinformatics leaders.