Markus Covert Lab

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Markus Covert Lab

Markus Covert Lab

@MarkusCovertLab

Building computer models of cellular systems to predict complex behaviors. And having fun doing it! Also department chair and #1 fan, @bioe_stanford ❤️

Stanford University Bergabung Haziran 2024
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Markus Covert Lab
Markus Covert Lab@MarkusCovertLab·
@annewoj23 I remember when you showed us your orange hands in bio class and we all freaked out 🥕🖐️🤣
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Anne Wojcicki
Anne Wojcicki@annewoj23·
Back in the late 90s I had a job where I could get free carrot juice. 32 ounces a day. I learned the hard way that yes in fact you can turn orange!! I do not recommend it!! yahoo.com/health/wellnes…
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Markus Covert Lab
Markus Covert Lab@MarkusCovertLab·
Great take from @NikoMcCarty on a terrific cell modeling study that came out in Cell this week. I was asked to comment for a news piece about it in Nature; here's the full text of what I wrote (with thoughts on AI-driven models vs more mechanistic models) in case it sparks further discussion: Dear Ewen, thanks for reaching out and very exciting to see Zan and Zane's newest work! This work is a significant advance - the key part is including the kinetics, which most of the all-molecule simulations are not able to do. With regard to AI virtual cells, so far they are mostly promise and very little actual product, so it's hard to go into too much detail. That said, one way to think about Zan's accomplishment is in the context of artificial life, or AL. Her lab and mine, and others, are focused on actually trying to build detailed representations of a living cell in silico. They are dynamic, holistic, and mechanistic, all of which adds many distinct advantages in contrast to what is being considered with AI. For example, AL model simulations are explainable (that is, the simulated behaviors can be explained in terms of their mechanistic underpinnings), they actually require less data (but well-targeted, and of several different kinds), they are better suited to extrapolation, and interestingly, transfer learning is achieved through evolutionary theory (matching E. coli to a mycoplasma, for example), instead of (or in addition to) the traditional transfer learning methods that would be used for AI. I anticipate that in the future, some of the best things we learn from AI and this next generation AL (first generation would be Conway's Game of Life, and stuff like that - but these new models move far beyond that) will be integrated, and that is an interesting future to consider. As my lab is currently demonstrating (to appear soon), these models can lead us to scientific questions that were never considered before, and making an impact in basic as well as applied science. Very exciting times! * Note that several of these ideas came up in discussions with my lab members, they didn't all originate with me. Lucky to have a great team❤️
Niko McCarty.@NikoMcCarty

If you like this, then you may like my blog about biology: nikomc.com.

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Markus Covert Lab
Markus Covert Lab@MarkusCovertLab·
Count me in here - other seminal mathematical and statistical approaches have been invented while pursuing large-scale modeling goals (ie chaos theory and data assimilation in predicting weather) but biology has the most potential. What will we discover, and what will it enable?
The Atlantic@TheAtlantic

Many physicists have come to believe that a mystery is unfolding in every microbe, animal, and human—one that could redefine the field for the next generation, @AdamFrank4 writes. theatlantic.com/science/2025/1…

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Markus Covert Lab
Markus Covert Lab@MarkusCovertLab·
So honored to receive this award, can't wait to visit @UniMelb! And thanks a million to my incredible lab, a brilliant and wonderful group of people who inspire me every day ❤️🦠💻
Stanford Bioengineering@bioe_stanford

Markus Covert has been awarded the University of Melbourne’s 2025 Grimwade Medal! 🌟 The honor recognizes his pioneering work for constructing the first "whole-cell" computational model. @MarkusCovertLab @UniMelb biomedicalsciences.unimelb.edu.au/news-and-event…

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Anshul Kundaje
Anshul Kundaje@anshulkundaje·
I've run challenges before and I came to the conclusion that they are extremely hard to get right especially for biological problems that simply cannot be distilled to a few metrics. They just get gamed. 1/
dalloliogm@dalloliogm

My second post on the Arc Virtual Cell Challenge. The challenge’s Discord forums are in turmoil. Some participants have discovered a trick to get to the top of the leaderboard. gmdbioinformatics.substack.com/p/arc-virtual-… #arc_virtual_cell_challenge #foundation_models

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Markus Covert Lab
Markus Covert Lab@MarkusCovertLab·
@ElowitzLab @NikoMcCarty Wait... "intrinsically" more interesting? Or do you mean "extrinsically"? 🤣 but in all seriousness, an awesome paper - catalyzed lots of great follow-on work as well @ElowitzLab
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ElowitzLab
ElowitzLab@ElowitzLab·
This experiment came to life when I realized the negative control (two genes, same regulation) was intrinsically more interesting—and easier!—than the "real" experiment I had been planning... Thanks @NikoMcCarty!
Niko McCarty.@NikoMcCarty

Take two cells and place them side by side. Both cells have the same genome. And yet, oddly enough, they behave in different ways. They divide at different times and their RNA levels are distinct. Now let’s go one step further. Take those same two cells. But this time, imagine that they have not only the same genome, but completely identical molecules at identical concentrations. Will these two cells behave in the same way? The answer is no. This is because there are two types of "noise" inside of living cells; intrinsic and extrinsic. In the first example, the two cells act differently because of subtle differences in their gene levels. Not all genes are expressed at the same time or in the same amount, and this leads to slight differences. This is extrinsic noise, because it is “global to a single cell” but varies “from one cell to another.” In the second example, which is so statistically unlikely as to be basically impossible, the two cells would still have different gene expression patterns “because of the random microscopic events that govern which reactions occur and in what order.” This is intrinsic noise or stochasticity; it is an inalienable part of biology. I’m pulling these quotes from one of my all-time favorite papers, called “Stochastic Gene Expression in a Single Cell.” The first author is @ElowitzLab (of synthetic biology fame) and it was published in August 2022. It’s worth reading. For this paper, Elowitz & co. designed a simple experiment to separate intrinsic and extrinsic noise in a cell. Their goal was measure each source of noise to figure out which one dominates in different scenarios, like exposure to IPTG or the addition of a plasmid. So here’s what they did: First, they took E. coli cells and inserted two genes into the genome; one encoding a fluorescent cyan protein, and another encoding a fluorescent yellow protein. Each gene had the same promoter, and was placed equidistant from the genome’s origin of replication (but on opposite sides.) Next, they grew these cells in LB broth and photographed them using a microscope with color filters. The brightness of each color, in each cell, was quantified. If the variability between different E. coli cells stems from shared cellular conditions (like ribosome levels or extrinsic noise), then both colors in a single cell would fluctuate together. If the variability instead arises from random molecular events (intrinsic noise), then even within the same cell, the cyan and yellow levels would differ. If you plot these changes out on a scatterplot, then you can literally decode which “signals” or “triggers” are dominated by intrinsic or extrinsic noise, and by how much. This is a “beautiful experiment” because the experiment is so simple, yet it retrieves a huge amount of information. All they did was put two genes into an E. coli cell at symmetrical locations in the genome! And from that alone, they deconvoluted noise and its origins.

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Markus Covert Lab
Markus Covert Lab@MarkusCovertLab·
@NikoMcCarty Great article @NikoMcCarty! Since it's apropos and Halloween is coming up, I'll share my lab's amazing jack o'lantern, carved in the shape of a potassium channel, or "PUMP K+ IN." Aren't they the best?!? 🧡🎃🧡
Markus Covert Lab tweet media
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Niko McCarty.
Niko McCarty.@NikoMcCarty·
My new essay is about atomic-scale protein filters. Briefly: Cells are crowded and frenzied places. Molecular machines convert raw inputs into highly ordered structures; DNA coils into chromosomes and proteins fold into precise three-dimensional shapes. Outside the cell, by contrast, ions drift without gradients, and DNA and proteins exist mostly as scattered building blocks. Only a thin membrane separates the managed chaos inside from the high entropy outside. If a cell’s membrane is punctured, the fluid within — made mostly of water — drains away, and the cell swiftly dies. And yet, the membrane must let molecules in (food, nutrients, ions) and out (waste, toxins, messages) for the cell to survive. The membrane does so by means of hyperspecific proteins that allow only particular molecules to pass. It is thanks to these atomic-scale filters that life can exist at all. Two types of protein filters, aquaporin and the potassium ion channel, offer particularly elegant examples. Aquaporins let billions of water molecules pass in and out each second in a single file line, while blocking protons. Potassium channels conduct about 100 million K⁺ ions per second yet decisively reject Na⁺, a slightly smaller ion with the same charge. Both of these proteins rely on geometry alone (namely, the precise placement of amino acids) to achieve their selectivity. And both demonstrate how seemingly complex problems are often solved in biology simply by positioning the correct atom, with the appropriate charge, at the perfect place. GIFs by Ella Watkins-Dulaney for @AsimovPress
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Sri Kosuri
Sri Kosuri@srikosuri·
@MarkusCovertLab @eperlste Thanks! I started a section on your work, but I removed it because it was just getting way too long. Always been a big fan! Still in Memphis these days but hoping to get back late fall. Would love to take you up on the offer sometime.
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Sri Kosuri
Sri Kosuri@srikosuri·
OK. I posted a first attempt at spelling out some of my thoughts on the new race for virtual cell models. Overall, I'm excited, but also wary that we might repeat some of the same mistakes of the past.
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Markus Covert Lab
Markus Covert Lab@MarkusCovertLab·
Mixing in advances in experimental measurement tech, HPC and AI, there has never been a better time to make Crick's dream a reality. Bonus: here's his Mercedes, which I sometimes saw around town while in @ucsd_sbrg . You can't miss the license plate! 🎂 (end)
Markus Covert Lab tweet media
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Markus Covert Lab
Markus Covert Lab@MarkusCovertLab·
There are still massive challenges ahead, and I'm excited about DARPA's new Simulating Microbial Systems program, which seeks to "create generalizable and comprehensive computational simulations to predict the behavior of individual E. coli" (4/x) darpa.mil/research/progr…
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