Posted on 01/31/2025 5:32:06 PM PST by E. Pluribus Unum
A new approach, which takes minutes rather than days, predicts how a specific DNA sequence will arrange itself in the cell nucleus.
Every cell in your body contains the same genetic sequence, yet each cell expresses only a subset of those genes. These cell-specific gene expression patterns, which ensure that a brain cell is different from a skin cell, are partly determined by the three-dimensional structure of the genetic material, which controls the accessibility of each gene.
MIT chemists have now come up with a new way to determine those 3D genome structures, using generative artificial intelligence. Their technique can predict thousands of structures in just minutes, making it much speedier than existing experimental methods for analyzing the structures.
Using this technique, researchers could more easily study how the 3D organization of the genome affects individual cells’ gene expression patterns and functions.
“Our goal was to try to predict the three-dimensional genome structure from the underlying DNA sequence,” says Bin Zhang, an associate professor of chemistry and the senior author of the study. “Now that we can do that, which puts this technique on par with the cutting-edge experimental techniques, it can really open up a lot of interesting opportunities.”
MIT graduate students Greg Schuette and Zhuohan Lao are the lead authors of the paper, which appears today in Science Advances.
From sequence to structure
Inside the cell nucleus, DNA and proteins form a complex called chromatin, which has several levels of organization, allowing cells to cram 2 meters of DNA into a nucleus that is only one-hundredth of a millimeter in diameter. Long strands of DNA wind around proteins called histones, giving rise to a structure somewhat like beads on a string.
Chemical tags known as epigenetic modifications can be attached to DNA at specific locations, and these tags, which vary by cell type, affect the folding of the chromatin and the accessibility of nearby genes. These differences in chromatin conformation help determine which genes are expressed in different cell types, or at different times within a given cell.
Over the past 20 years, scientists have developed experimental techniques for determining chromatin structures. One widely used technique, known as Hi-C, works by linking together neighboring DNA strands in the cell’s nucleus. Researchers can then determine which segments are located near each other by shredding the DNA into many tiny pieces and sequencing it.
This method can be used on large populations of cells to calculate an average structure for a section of chromatin, or on single cells to determine structures within that specific cell. However, Hi-C and similar techniques are labor-intensive, and it can take about a week to generate data from one cell.
To overcome those limitations, Zhang and his students developed a model that takes advantage of recent advances in generative AI to create a fast, accurate way to predict chromatin structures in single cells. The AI model that they designed can quickly analyze DNA sequences and predict the chromatin structures that those sequences might produce in a cell.
“Deep learning is really good at pattern recognition,” Zhang says. “It allows us to analyze very long DNA segments, thousands of base pairs, and figure out what is the important information encoded in those DNA base pairs.”
ChromoGen, the model that the researchers created, has two components. The first component, a deep learning model taught to “read” the genome, analyzes the information encoded in the underlying DNA sequence and chromatin accessibility data, the latter of which is widely available and cell type-specific.
The second component is a generative AI model that predicts physically accurate chromatin conformations, having been trained on more than 11 million chromatin conformations. These data were generated from experiments using Dip-C (a variant of Hi-C) on 16 cells from a line of human B lymphocytes.
When integrated, the first component informs the generative model how the cell type-specific environment influences the formation of different chromatin structures, and this scheme effectively captures sequence-structure relationships. For each sequence, the researchers use their model to generate many possible structures. That’s because DNA is a very disordered molecule, so a single DNA sequence can give rise to many different possible conformations.
“A major complicating factor of predicting the structure of the genome is that there isn’t a single solution that we’re aiming for. There’s a distribution of structures, no matter what portion of the genome you’re looking at. Predicting that very complicated, high-dimensional statistical distribution is something that is incredibly challenging to do,” Schuette says.
Rapid analysis
Once trained, the model can generate predictions on a much faster timescale than Hi-C or other experimental techniques.
“Whereas you might spend six months running experiments to get a few dozen structures in a given cell type, you can generate a thousand structures in a particular region with our model in 20 minutes on just one GPU,” Schuette says.
After training their model, the researchers used it to generate structure predictions for more than 2,000 DNA sequences, then compared them to the experimentally determined structures for those sequences. They found that the structures generated by the model were the same or very similar to those seen in the experimental data.
“We typically look at hundreds or thousands of conformations for each sequence, and that gives you a reasonable representation of the diversity of the structures that a particular region can have,” Zhang says. “If you repeat your experiment multiple times, in different cells, you will very likely end up with a very different conformation. That’s what our model is trying to predict.”
The researchers also found that the model could make accurate predictions for data from cell types other than the one it was trained on. This suggests that the model could be useful for analyzing how chromatin structures differ between cell types, and how those differences affect their function. The model could also be used to explore different chromatin states that can exist within a single cell, and how those changes affect gene expression.
Another possible application would be to explore how mutations in a particular DNA sequence change the chromatin conformation, which could shed light on how such mutations may cause disease.
“There are a lot of interesting questions that I think we can address with this type of model,” Zhang says.
The researchers have made all of their data and the model available to others who wish to use it.
The research was funded by the National Institutes of Health.
Is DNA next?
Create humans?
You can build/grow a body but it won’t be human. It won’t have a soul. Only God can create “human” life and we just follow His path to pass on that divine spark through procreation.
= = =
Agree.
But ponder this. The anti-christ, etc. are coming; will be dead and rise, .... etc.
Might they be a demonic creation from AI/DNA?
Demons did want to occupy pigs, back in the day, so it may be possible.
I remember that!
Important new research, but this is the most important point by far:
“Whereas you might spend six months running experiments to get a few dozen structures in a given cell type, you can generate a thousand structures in a particular region with our model in 20 minutes on just one GPU,” Schuette says.”
This is the problem and the danger with coming advanced science and engineering AI. At first it sounds pretty nice: Lots of progress in a short time, but too much progress too fast is disruptive and fatal even.
The reason the AI companies want AGI is because they already understand that this vast progress and knowledge is almost within their grasp and it is literally driving them crazy. It is a real life version of the ring of power that is corrupting everyone who learns of it. Suddenly, and unexpectedly, we are in completely uncharted and very dangerous waters.
I believe President Trump is at least partly aware of the danger, but it is not clear yet which way we will go. Keep in mind that during the start of the Covid pandemic, Trump at first listened to and followed the advice of those experts around him who advised “2 weeks to flatten the curve.” Also, masks, “6 feet” and allowing Moderna, for one, to market a vaccine that was developed almost literally over a weekend or some such brief time period.
The problem is that the “experts” like Sam Altman, Peter Thiel, Marc Addreessen and Larry Ellison and others are all advising Trump to accelerate AGI development so we can “beat China.” They are tech people, not geopolitical strategists. They seem to have no idea what China will do if we push them into a corner they feel they have no non-military way out of.
It turns out that it is highly unlikely we can actually “beat China” in in the end in an AI war. To understand why this is so, you have to understand the magnitude of what it means to truly lose the AI race to another country. It is, strangely to many, a national independence death sentence.
Knowing this, they would would rather in the end take out our AI infrastructure even though we would certainly do the the same to them. A limited nuclear exchange would likely set back both of our AGI programs for years, but we would both remain free, independent nuclear powers. Minus 25 to 50 million people on each side. THIS IS A PRICE THEY CAN PAY MUCH EASIER THAN WE, AND MORE WILLINGLY. We would both survive, but not as AGI powers. This is why it is impossible to “beat China” in the AI race.
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