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computational biology

The Amazing Brain: Visualizing Data to Understand Brain Networks

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The NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative continues to teach us about the world’s most sophisticated computer: the human brain. This striking image offers a spectacular case in point, thanks to a new tool called Visual Neuronal Dynamics (VND).

VND is not a camera. It is a powerful software program that can display, animate, and analyze models of neurons and their connections, or networks, using 3D graphics. What you’re seeing in this colorful image is a strip of mouse primary visual cortex, the area in the brain where incoming sensory information gets processed into vision.

This strip contains more than 230,000 neurons of 17 different cell types. Long and spindly excitatory neurons that point upward (purple, blue, red, orange) are intermingled with short and stubby inhibitory neurons (green, cyan, magenta). Slicing through the neuronal landscape is a neuropixels probe (silver): a tiny flexible silicon detector that can record brain activity in awake animals [1].

Developed by Emad Tajkhorshid and his team at University of Illinois at Urbana-Champaign, along with Anton Arkhipov of the Allen Institute, Seattle, VND represents a scientific milestone for neuroscience: using an adept software tool to see and analyze massive neuronal datasets on a computer. What’s also nice is the computer doesn’t have to be a fancy one, and VND’s instructions, or code, are publicly available for anyone to use.

VND is the neuroscience-adapted cousin of Visual Molecular Dynamics (VMD), a popular molecular biology visualization tool to see life up close in 3D, also developed by Tajkhorshid’s group [2]. By modeling and visualizing neurons and their connections, VND helps neuroscientists understand at their desktops how neural networks are organized and what happens when they are manipulated. Those visualizations then lay the groundwork for follow-up lab studies to validate the data and build upon them.

Through the Allen Institute, the NIH BRAIN Initiative is compiling a comprehensive whole-brain atlas of cell types in the mouse, and Arkhipov’s work integrates these data into computer models. In May 2020, his group published comprehensive models of the mouse primary visual cortex [3].

Arkhipov and team are now working to understand how the primary visual cortex’s physical structure (the cell shapes and connections within its complicated circuits) determines its outputs. For example, how do specific connections determine network activity? Or, how fast do cells fire under different conditions?

Ultimately, such computational research may help us understand how brain injuries or disease affect the structure and function of these neural networks. VND should also propel understanding of many other areas of the brain, for which the data are accumulating rapidly, to answer similar questions that still remain mysterious to scientists.

In the meantime, VND is also creating some award-winning art. The image above was the second-place photo in the 2021 “Show us Your BRAINs!” Photo and Video Contest sponsored by the NIH BRAIN Initiative.


[1] Fully integrated silicon probes for high-density recording of neural activity. Jun JJ, Steinmetz NA, Siegle JH, Denman DJ, Bauza M, Barbarits B, Lee AK, Anastassiou CA, Andrei A, Aydın Ç, Barbic M, Blanche TJ, Bonin V, Couto J, Dutta B, Gratiy SL, Gutnisky DA, Häusser M, Karsh B, Ledochowitsch P, Lopez CM, Mitelut C, Musa S, Okun M, Pachitariu M, Putzeys J, Rich PD, Rossant C, Sun WL, Svoboda K, Carandini M, Harris KD, Koch C, O’Keefe J, Harris TD. Nature. 2017 Nov 8;551(7679):232-236.

[2] VMD: visual molecular dynamics. Humphrey W, Dalke A, Schulten K. J Mol Graph. 1996 Feb;14(1):33-8, 27-8.

[3] Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex. Billeh YN, Cai B, Gratiy SL, Dai K, Iyer R, Gouwens NW, Abbasi-Asl R, Jia X, Siegle JH, Olsen SR, Koch C, Mihalas S, Arkhipov A. Neuron. 2020 May 6;106(3):388-403.e18


The Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)

Models of the Mouse Primary Visual Cortex (Allen Institute, Seattle)

Visual Neuronal Dynamics (NIH Center for Macromolecular Modeling and Bioinformatics, University of Illinois at Urbana-Champaign)

Tajkhorshid Lab (University of Illinois at Urbana-Champaign)

Arkhipov Lab (Allen Institute)

Show Us Your BRAINs! Photo & Video Contest (BRAIN Initiative/NIH)

NIH Support: National Institute of Neurological Disorders and Stroke

Dynamic View of Spike Protein Reveals Prime Targets for COVID-19 Treatments

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SARS-CoV-2’s spike protein showing attached glycans and regions for antibody binding.
Credit: Sikora M, PLoS Comput Biol, 2021

This striking portrait features the spike protein that crowns SARS-CoV-2, the coronavirus that causes COVID-19. This highly flexible protein has settled here into one of its many possible conformations during the process of docking onto a human cell before infecting it.

This portrait, however, isn’t painted on canvas. It was created on a computer screen from sophisticated 3D simulations of the spike protein in action. The aim was to map its many shape-shifting maneuvers accurately at the atomic level in hopes of detecting exploitable structural vulnerabilities to thwart the virus.

For example, notice the many chain-like structures (green) that adorn the protein’s surface (white). They are sugar molecules called glycans that are thought to shield the spike protein by sweeping away antibodies. Also notice areas (purple) that the simulation identified as the most-attractive targets for antibodies, based on their apparent lack of protection by those glycans.

This work, published recently in the journal PLoS Computational Biology [1], was performed by a German research team that included Mateusz Sikora, Max Planck Institute of Biophysics, Frankfurt. The researchers used a computer application called molecular dynamics (MD) simulation to power up and model the conformational changes in the spike protein on a time scale of a few microseconds. (A microsecond is 0.000001 second.)

The new simulations suggest that glycans act as a dynamic shield on the spike protein. They liken them to windshield wipers on a car. Rather than being fixed in space, those glycans sweep back and forth to protect more of the protein surface than initially meets the eye.

But just as wipers miss spots on a windshield that lie beyond their tips, glycans also miss spots of the protein just beyond their reach. It’s those spots that the researchers suggest might be prime targets on the spike protein that are especially promising for the design of future vaccines and therapeutic antibodies.

This same approach can now be applied to identifying weak spots in the coronavirus’s armor. It also may help researchers understand more fully the implications of newly emerging SARS-CoV-2 variants. The hope is that by capturing this devastating virus and its most critical proteins in action, we can continue to develop and improve upon vaccines and therapeutics.


[1] Computational epitope map of SARS-CoV-2 spike protein. Sikora M, von Bülow S, Blanc FEC, Gecht M, Covino R, Hummer G. PLoS Comput Biol. 2021 Apr 1;17(4):e1008790.


COVID-19 Research (NIH)

Mateusz Sikora (Max Planck Institute of Biophysics, Frankfurt, Germany)

The surprising properties of the coronavirus envelope (Interview with Mateusz Sikora), Scilog, November 16, 2020.

Using R2D2 to Understand RNA Folding

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If you love learning more about biology at a fundamental level, I have a great video for you! It simulates the 3D folding of RNA. RNA is a single stranded molecule, but it is still capable of forming internal loops that can be stabilized by base pairing, just like its famously double-stranded parent, DNA. Understanding more about RNA folding may be valuable in many different areas of biomedical research, including developing ways to help people with RNA-related diseases, such as certain cancers and neuromuscular disorders, and designing better mRNA vaccines against infectious disease threats (like COVID-19).

Because RNA folding starts even while an RNA is still being made in the cell, the process has proven hugely challenging to follow closely. An innovative solution, shown in this video, comes from the labs of NIH grantees Julius Lucks, Northwestern University, Evanston, IL, and Alan Chen, State University of New York at Albany. The team, led by graduate student Angela Yu and including several diehard Star Wars fans, realized that to visualize RNA folding they needed a technology platform that, like a Star Wars droid, is able to “see” things that others can’t. So, they created R2D2, which is short for Reconstructing RNA Dynamics from Data.

What’s so groundbreaking about the R2D2 approach, which was published recently in Molecular Cell, is that it combines experimental data on RNA folding at the nucleotide level with predictive algorithms at the atomic level to simulate RNA folding in ultra-slow motion [1]. While other computer simulations have been available for decades, they have lacked much-needed experimental data of this complex folding process to confirm their mathematical modeling.

As a gene is transcribed into RNA one building block, or nucleotide, at a time, the elongating RNA strand folds immediately before the whole molecule is fully assembled. But such folding can create a problem: the new strand can tie itself up into a knot-like structure that’s incompatible with the shape it needs to function in a cell.

To slip this knot, the cell has evolved immediate corrective pathways, or countermoves. In this R2D2 video, you can see one countermove called a toehold-mediated strand displacement. In this example, the maneuver is performed by an ancient molecule called a single recognition particle (SRP) RNA. Though SRP RNAs are found in all forms of life, this one comes from the bacterium Escherichia coli and is made up of 114 nucleotides.

The colors in this video highlight different domains of the RNA molecule, all at different stages in the folding process. Some (orange, turquoise) have already folded properly, while another domain (dark purple) is temporarily knotted. For this knotted domain to slip its knot, about 5 seconds into the video, another newly forming region (fuchsia) wiggles down to gain a “toehold.” About 9 seconds in, the temporarily knotted domain untangles and unwinds, and, finally, at about 23 seconds, the strand starts to get reconfigured into the shape it needs to do its job in the cell.

Why would evolution favor such a seemingly inefficient folding process? Well, it might not be inefficient as it first appears. In fact, as Chen noted, some nanotechnologists previously invented toehold displacement as a design principle for generating synthetic DNA and RNA circuits. Little did they know that nature may have scooped them many millennia ago!


[1] Computationally reconstructing cotranscriptional RNA folding from experimental data reveals rearrangement of non-naïve folding intermediates. Yu AM, Gasper PM Cheng L, Chen AA, Lucks JB, et. al. Molecular Cell 8, 1-14. 18 February 2021.


Ribonucleic Acid (RNA) (National Human Genome Research Institute/NIH)

Chen Lab (State University of New York at Albany)

Lucks Laboratory (Northwestern University, Evanston IL)

NIH Support: National Institute of General Medical Sciences; Common Fund

Finding New Genetic Mutations Amid Healthy Cells

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Po-Ruh Loh
Po-Ru Loh Courtesy of Loh Lab

You might recall learning in biology class that the cells constantly replicating and dividing in our bodies all carry the same DNA, inherited in equal parts from each parent. But it’s become increasingly clear in recent years that even seemingly healthy tissues contain neighborhoods of cells bearing their own acquired genetic mutations. The question is: What do all those altered cells mean for our health?

With support from a 2018 NIH Director’s New Innovator Award, Po-Ru Loh, Harvard Medical School, Boston, is on a quest to find out, though without the need for sequencing lots of DNA in his own lab. Loh will instead develop ultrasensitive computational tools to pick up on those often-subtle alterations within the vast troves of genomic data already stored in databases around the world.

How is that possible? The math behind it might be complex, but the underlying idea is surprisingly simple. His algorithms look for spots in the genome where a slight imbalance exists in the quantity of DNA inherited from mom versus dad.

Actually, Loh can’t tell from the data which parent provided any snippet of chromosomal DNA. But looking at DNA sequenced from a mixture of many cells, he can infer which stretches of DNA were most likely inherited together from a single parent.

Any slight skew in those quantities point the way to genomic territory where a tiny portion of chromosomal DNA either went missing or became duplicated in some cells. This common occurrence, especially in older adults, leads to a condition called genetic mosaicism, meaning that, contrary to most biology textbooks, all cells aren’t exactly the same.

By detecting those subtle imbalances in the data, Loh can pinpoint small DNA alterations, even when they occur in 1 in 1,000 cells collected from a person’s bloodstream, saliva, or tissues. That’s the kind of sensitivity that most scientists would not have thought possible.

Loh has already begun putting his new computational approach to work, as reported in Nature last year [1]. In DNA data from blood samples of more than 150,000 participants in the United Kingdom Biobank, his method uncovered well over 8,000 mosaic chromosomal alterations.

The study showed that some of those alterations were associated with an increased risk of developing blood cancers. However, it’s important to note that most people with evidence of mosaicism won’t go on to develop cancer. The researchers also made the unexpected discovery that some individuals carried genetic variants that made them more prone than others to pick up new mutations in their blood cells.

What’s especially exciting is Loh’s computational tools now make it possible to search for signs of mosaicism within all the genetic data that’s ever been generated. Even more importantly, these tools will allow Loh and other researchers to ask and answer important questions about the consequences of mosaicism for a wide range of diseases.


[1] Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations. Loh PR, Genovese G, Handsaker RE, Finucane HK, Reshef YA, Palamara PF, Birmann BM, Talkowski ME, Bakhoum SF, McCarroll SA, Price AL. Nature. 2018 Jul;559(7714):350-355.


Loh Lab (Harvard Medical School, Boston)

Loh Project Information (NIH RePORTER)

NIH Director’s New Innovator Award (Common Fund)

NIH Support: Common Fund; National Institute of Environmental Health Sciences

Some ‘Hospital-Acquired’ Infections Traced to Patient’s Own Microbiome

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Bacteria in both blood and gut

Caption: New computational tool determines whether a gut microbe is the source of a hospital-acquired bloodstream infection
Credit: Fiona Tamburini, Stanford University, Palo Alto, CA

While being cared for in the hospital, a disturbingly large number of people develop potentially life-threatening bloodstream infections. It’s been thought that most of the blame lies with microbes lurking on medical equipment, health-care professionals, or other patients and visitors. And certainly that is often true. But now an NIH-funded team has discovered that a significant fraction of these “hospital-acquired” infections may actually stem from a quite different source: the patient’s own body.

In a study of 30 bone-marrow transplant patients suffering from bloodstream infections, researchers used a newly developed computational tool called StrainSifter to match microbial DNA from close to one-third of the infections to bugs already living in the patients’ large intestines [1]. In contrast, the researchers found little DNA evidence to support the notion that such microbes were being passed around among patients.

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