Alex Frankell

Research Group Leader @EarlyCancerCam @CRUKCamCentre and Wellcome Career Developement Fellow studying somatic evolution using non-invasive sampling

[Alex Frankell @AFrankell]

📢 We're hiring! Two posts available in SEM lab @CRUKCamCentre to drive a @wellcometrust funded £2.6M tech dev programme in evolutionary tracking and diagnostics with world-class clinical studies and based in a newly revamped @EarlyCancerCam institute. Deadline Nov 10th! (1/3)

[Sonya Hessey @SonyaHessey]

Thrilled that our manuscript ‘Evolutionary characterization of Lung Cancer metastasis’ in #TRACERx & #PEACE was published in @Nature today. Here is a summary:

[Will Hill @DrWilliamHill]

Really elegant work. Endogenous formaldehyde → DNA damage → HSC attrition → stochastic drift to monoclonality — no driver mutations needed.

I like the analogy of how average runners can win if enough contestants drop out. There's hope yet!

[Niko McCarty. @NikoMcCarty]

A non-hyped explainer of the “cell simulation” paper.

The recent study about the “4D” simulation of a minimal cell has been getting a lot of attention on social media. Unfortunately, most posts about it have serious errors. I’ve seen people claim that the model simulates every chemical reaction in the cell, for example, which is not true.

Some biomolecules and reactions *are* tracked individually in the simulation, including proteins and RNA (and ribosomes), and the chromosome. But the simulation does not track individual metabolites (like ATP or glucose), water, nucleotide precursors, lipds, and so on. These "other" molecules are represented, instead, as concentrations (using ordinary differential equations).

But anyway, here goes my quick explanation:

Researchers built a computational model that simulates roughly 100 minutes of biological time, or one cell division, for a single bacterial cell. Each simulation takes 4–6 days to run on two NVIDIA A100 GPUs, and the authors ran it 50 times in replicate. The cell simulation includes some elements of randomness, so each replication attempt leads to a slightly different outcome. When they plotted out these replicates and averaged results, they found that the model could predict a few things without being fitted to experimental data: The simulated cells “divided” every 105 minutes, on average, which matches experimental results; and the mRNA molecules had an average half-life of 3.63 minutes, which is roughly what we’d expect from experiments, too.

The cell they are modeling is called JCVI-syn3A, and it is not a naturally-occurring organism. It’s a bacterium that has been engineered, over many years, to have a small genome. It only has 493 genes (compared to 4,000+ for E. coli), all of which are housed on a single chromosome. The Syn3A cell was made by taking a natural organism, called Mycoplasma mycoides, and then slashing out non-essential genes. Its entire proteome, transcriptome, and metabolism have been studied in depth, which is why it’s being used to build these whole-cell simulations.

The actual *simulation*, though, is not a single thing! Instead, the authors wrote down all the “stuff” that happens inside a cell (transcription! translation! metabolism! lipid biosynthesis!) and decided which type of mathematical model would be best-suited to describe each thing. Some cell processes were modelled deterministically, others had “spatial” elements, and other parts were relatively random.

More specifically, they used four different types of models to build this simulation:

1. A Reaction-Diffusion Master Equation, which was used to model the individual proteins, RNAs, and ribosomes.
2. A Chemical Master Equation, which was used to model things where spatial location doesn’t matter as much (it basically treats the whole cell as one mixed entity); including tRNA charging.
3. Ordinary Differential Equations, which you may be familiar with from Calculus class, were used to model changes in ATP concentration, lipids, and so on.
4. Brownian Dynamics, which simulated the chromosome as a physical chain of beads, where each bead represents 10 base pairs of DNA.

The Reaction-Diffusion Equation works like this: Basically, they chopped up the entire digital cell into a 3D grid of cubes. Each cube measures 10 nanometers on each side. The whole cell is about 500 nanometers across, so there are tens of thousands of cubes in the cell's interior. (This is a useful way to coarse grain the simulation; if the cubes were smaller, the simulation would take much longer to run.)

Each cube is a little box that contains some number of molecules. At every “step” in the simulation, only one of two things can happen to the molecules in each box: Either they react with a molecule in the same box, or they diffuse (“hop”) to an adjacent box. That’s it; the model is just rolling a die for each molecule at each time step in each box, and using those results to decide how each molecule changes over time.

(The reason this spatial model is important is because biology only works if molecules physically bump into each other. And so this spatial grid means that, unlike simpler models, a protein actually has to “diffuse” across boxes in the cell to encounter its reaction partner; only then can it react and do something useful.)

So anyway, each of these models is used to represent a different type of molecule. It’s not like there is a single, all-powerful simulation that they are running here; instead, they’re running these four models together, using a script that synchronizes their results with each other.

The Reaction-Diffusion equation is the main part of the simulation. It takes time steps of 50 microseconds of biological time. Every 12.5 milliseconds of biological time — meaning every 250 RDME steps — the simulation pauses so that the other models can synchronize based on the latest state of the simulation. The Brownian Dynamics part runs on a completely separate GPU, and only updates every four seconds of biological time.

So that's the gist here. But let's also be honest about what this simulation does NOT do:

- It does not include polysomes, which are a cluster of ribosomes that all latch onto a single mRNA and translate at the same time. Polysomes are really common inside of cells, but this simulation assumes that each mRNA can only be translated by one ribosome at a time.
- It does not include polycistronic transcription. In bacteria, genes are often grouped next to each other on the chromosome and thus “transcribed” (or turned into mRNA) all at once, together. The majority of genes in E. coli, for example, are arranged in these operons, and the authors of this paper acknowledge that many Syn3A genes are likely co-transcribed the same way. But the simulation doesn't capture it.
- The authors manually tuned many parameters to get the model to make predictions that more closely resemble experiments. Earlier simulations were waaaayyyyy off from experimental results. For example, they adjusted the ratio of mRNA binding rates to ribosomes versus degradosomes because, in earlier simulations, mRNA was being degraded too quickly, before ribosomes could translate it, causing most proteins to be severely underproduced.
- In the Brownian Dynamics model, the authors added a “fake” 12 pN physical force to push the two daughter chromosomes apart during division, because the real biological mechanism for chromosome partitioning in Syn3A is not known.
- And some other things.

That being said: This model is really cool! I love papers like this! I'm enamored by scientists who choose really difficult problems (like simulating an entire cell) and actually go after it and make progress!

This paper is amazing because it shows us what we are able to simulate well, and what we don't yet understand, and to figure out which experiments we ought to perform to reconcile the two. So instead of framing this paper as "OH MY GOSH SCIENTISTS FIGURED OUT HOW TO SIMULATE AN ENTIRE CELL!" we should frame it as proof that there is still plenty of room at the bottom, many measurements to be made, and many avenues to explore as we seek to understand biology better.

[Inigo Martincorena @imartincorena]

Somatic genomics as a discovery engine for biomedicine - a Perspective in Cell
https://t.co/hcb6I1yIjb

[Jamie Blundell @jrblundell]

In 1999, Tom Maniatis discovered something remarkable: neurons achieve self-avoidance via stochastic methylation of the protocadherin gene cluster.

We've just discovered this locus is an evolvable in-vivo barcode across the human tissues: https://t.co/DMyHA5TIS0 🧵

[Calum Gabbutt @CalumGabbutt]

If you’re interested in machine learning and mathematical modelling, we’re recruiting a PhD student to develop new methods for studying cancer evolution at @ImperialImmuno.

Link below.

[Oriol Pich @oriol_pich]

Happy to share our latest study @Nature , where we evaluate somatic evolution and the effect of treatment in normal tissue using duplex sequencing 1/n

https://t.co/fr1Cbj7SDU

[Shalini.Rao @shalvrao]

This is what the last five years have been about!
Incredibly proud to share our new paper in Nature Genetics: “Transcription Factor Switching Drives Subtype Specific Pancreatic Cancer.” Thank you @JCarrollLab @PancreaticCanUK @CRUK_CI
https://t.co/tu5do3uRE7

[Alex Frankell @AFrankell]

Post 2: NGS Lab Technician (3yr + poss extensions, RD47644): Bring your expertise in NGS workflows and we'll build new things together with our interdisciplinary team. Apply: https://t.co/BMFGa6Mqp0. Questions? [email protected]. (3/3)

[Alex Frankell @AFrankell]

📢 We're hiring! Two posts available in SEM lab @CRUKCamCentre to drive a @wellcometrust funded £2.6M tech dev programme in evolutionary tracking and diagnostics with world-class clinical studies and based in a newly revamped @EarlyCancerCam institute. Deadline Nov 10th! (1/3)

[Alex Frankell @AFrankell]

Post 1: Research Assistant (2yr, RD47639): Optimise a novel duplex seq approach and build wet lab skills (+ computation if desired). This deigned as launchpad for Early-career scientists into doctoral training. Apply: https://t.co/vg5PU7rObU (2/3)

[Early Cancer Institute, University of Cambridge @EarlyCancerCam]

Thanks to all who attended #ECISymposium2025, we had a great turnout for our excellent speakers, lightning talks and posters. Fascinating to hear about the new and inspiring trials & research taking place.
@petersasieni @clare__turnbull @sarah_blagden Gonia Gozdecka @SCICambridge

[Calum Gabbutt @CalumGabbutt]

Cancer is an evolutionary disease, but does knowing a cancer’s evolutionary past help predict its future? Out today in @Nature, we learnt the evolution of 2000 lymphoid cancers and found it was highly correlated with clinical outcomes! (1/7, link below)

[Cancer Grand Challenges @CancerGrand]

This interesting piece in @TheEconomist features the pioneering work of #CancerGrandChallenges team Mutographs, Phil Jones (@sangerinstitute) and Chair of our Scientific Committee @CharlesSwanton. Worth a read if you have access.

Team Mutographs work found a plethora of oncogenic driver mutations in normal tissue and conversely that a number of known carcinogens are able to promote cancer formation without causing mutations, changing the way we think about how cancer starts. The team’s work also inspired us to set the normal phenotypes challenge in 2020, which is now being tackled by team PROMINENT, co-led by Allan Balmain, Paul Brenna and Nuria Lopez-Bigas

👉 https://t.co/UvBKEqXJY4

[Nicholas McGranahan @NickyMcGranahan]

Excited to share our new paper in @Nature, led by @PiotrekPaw_ @KristianaGrigo4 ! 🧬 We developed a new method, ALPACA, to untangle the co-evolution of single nucleotide variants and copy number changes in tumours.
Check it out: https://t.co/Aee2xcJKrI

[Will Hill @DrWilliamHill]

Thrilled to announce I'm moving to the @CRUK_MI to lead the Cancer Origins group! If you're interested in how mutations and exposures drive tumourigenesis—come join the adventure. https://t.co/hQCT6KsJ52

[JOAN BRUGGE @BruggeMe]

My two NIH cancer research grants were terminated today. One involving breast cancer prevention is an “outstanding investigator award” and had received a perfect score by reviewers. How does ending lifesaving cancer research make Americans healthier? @harvardmed @aacr

[Early Cancer Institute, University of Cambridge @EarlyCancerCam]

🥳 Huge congratulations to @EarlyCancerCam group leader @Tom_J_Mitchell on his RISING STAR award from @KidneyCancer presented at #IKCSEU25 on Saturday.
The award recognises talented young #kidneycancerexperts

[Kamila Naxerova @naxerova]

Interested in the mysteries surrounding metastasis evolution?

This perspective in @NatureRevCancer is my best attempt at proposing a framework that could help advance our thinking about the topic.

Well. At least it helped advance my own thinking. 😅

https://t.co/L173ayMNsc

[Max Diehn, MD/PhD @max_diehn]

Excited to share our study describing a new liquid biopsy method called RARE-Seq for analyzing cell-free RNA (#cfRNA) that is out today in @Nature. 1/15 https://t.co/C1dLxw2G4a