News story our APS fellowship.
A testament to the great people we work with (and the unwavering rigor of Bayesian approaches, impervious to hype) 😊 🙏
news.asu.edu/b/20251021-a...
For full paper:
doi.org/10.7554/eLif...
In our work, we introduce REPOP, a Bayesian computational framework that more accurately quantifies bacterial populations from plate counts by modeling the experimental noise introduced through dilution and plating.
Hello everyone,
Tomorrow I’ll be giving a chalk talk on our new eLife:
“REPOP: bacterial population quantification from plate counts”
elifesciences.org/reviewed-pre...
Looking forward to seeing you!!
#eLife #datascience #biophysics #bioinformatics #Bayesian #REPOP
Hello all,
If you do #PlateCounting, you may want to take a look at our new eLife @elife.bsky.social
If you don't, I still encourage you to join for an interesting discussion.
Follow the thread 🧵
elifesciences.org/reviewed-pre...
#Microbiology #DataScience #PyTorch #QuantitativeBiology #REPOP
So… I was googling myself and made quite a discovery
🎧 There's an AI-generated podcast of my #TimeSeriesForecasting paper 🤔🤔
www.youtube.com/watch?v=3BNz...
Not sure whether to feel flattered, creeped out, or alarmed.
That is the future I guess 🤷♂️🤷♂️
To read the full paper: doi.org/10.1088/2632...
6/6
✅ Simulate complex, non-Markovian biological dynamics
✅ Train conditional normalizing flows to approximate intractable likelihoods
✅ Perform full #Bayesian inference on anything you can simulate.
arxiv.org/abs/2506.09374
5/6
We apply this to yeast expressing GFP under the glc3 promoter.
🌱 At first glance, high fluorescence seems like gene activation. But when you model protein inheritance across divisions...
Most cells are actually inactive — just glowing their ancestors GFP.
4/6
Despite the complexity, these dynamics are easy to simulate — protein production, cell division, fluorescence, all of it.
So we flipped the problem: We train neural networks on simulations to learn the likelihood function itself.
3/6
Because of that clock, division times aren’t memoryless -- they’re not exponential.
This breaks standard models of gene expression, that is:
NO Master Equations
NO Fokker-Planck equations
We had rethink how we do inference.
2/6
In this new preprint, we analyze #flowcytometry data of stress regulation in yeast
🧬 We indirectly observe protein levels through fluorescence.
But here's the catch:
1 - Proteins live much longer than a single cell cycle
2 - Cell division follows a biological clock
No likelihood? No problem
The class of stochastic models we can simulate is A LOT larger than the ones we can write likelihoods.
What if we could learn the likelihood directly from simulation? See the 🧵👇
arxiv.org/abs/2506.09374
#SimulationBasedInference #Neuralnetworks #AI
5/6
This is why we built REPOP, an #opensource tool to REconstruct POpulations from plates.
Straightforward to use and with tutorials available on #GitHub
github.com/PessoaP/REPOP
With all the #Bayesian Rigor and #PyTorch speed
4/6
As we show in the preprint, this
- Overestimatese variability
- Can miss real structure in your population: Subpopulations and/or multimodality as biological differences across samples,
2/6
Plate counting is a simple:
You dilute a sample, plate a small volume, and count colonies.
Say you dilute by 200×, and count 50 colonies.
Easy just multiply 50 × 200 = 10k bacteria, right?
NOT QUITE...
Hello all, 📣📣📣
If you do #PlateCounting , I want you to take a look at this new preprint.🧫🧫🧫
If you don't, I still encourage you to join for an interesting discussion.
Follow the thread 🧵
doi.org/10.1101/2025...
#Microbiology #DataScience #PyTorch #QuantitativeBiology #REPOP
6/6
If you plate, you need REPOP.
Preprint -- doi.org/10.1101/2025...
Software -- github.com/PessoaP/REPOP
Special thanks to the Lab Members - Pedro Pessoa, Carol Lu and Stanimir Tashev
As well as Rory Kruithoff and Douglas P Shepherd
#Biophysics #QuantitativeBiology
3/6
This assumes:
– No randomness in how many bacteria end up on the plate
– No randomness in the original swab
In reality, every step is noisy.
Unadulterated images of my talk at #Biophest today
But how do we know how accurate our estimate of π really is? 🤔
There’s a way to do it right: Combining it with Bayesian inference. Instead of just getting a rough guess, we can properly quantify uncertainty.
That is what I have written in my blog today. Check it out
Happy #PiDay, everybody! 🥧🥧🥧🥧🥧🥧
Today, we celebrate π with a fun (but dubious) way to calculate it:
1️⃣ Toss random points into a square.
2️⃣ Count how many land inside the inscribed circle.
3️⃣ Use the ratio to approximate π/4
labpresse.com/2053-2/
#Bayes #DataScience #MonteCarlo #Probability
"What is a GPTase?"
Answer: Protein that destroys large language models
😂 😂 😂
#AI #biophysics #BPS2025
It’s my favorite time of the year where I see my colleagues and hear about their amazing work at the annual Biophysical society meeting in LA! The Biological Fluorescence Symposium subgroup session is in full swing! #BPS2025
#bps2025
Attending #BPS2025? Want to know more about tangible steps you can take to challenge the attacks on science in the U.S.? Please attend an Emergency Town Hall Meeting on Tuesday at 1:30! @biophysicalsoc.bsky.social @blackinbiophys.bsky.social Please spread the word!
B503: Bayesian Single-Particle Tracking Using Normalizing Flows (Presented by Jay Spendlove)
B488: Learning Memory Kernel Parameters for Coarse-Grained Simulation (Presented by Nikhil Ramesh)
LB90: Scalable Likelihood Approximation in Biophysics via Normalizing Flows (presented by yours truly)
B475: Capturing Quantitative Bacterial Population Kinetics Within Individual C. elegans (Presented by Carol Lu)
Hello everyone 📣📣📣
This week, I'm attending the #BPS2025 Annual Meeting in Los Angeles.
Interestingly, all the works I'm co-authoring are scheduled at the same time: Tuesday, February 18, from 1:45 PM to 3:45 PM.
#Biophysics #Bayesian #AI #Statistics #Research #Science
In science, while we do often guess at underlying patterns, the process involves testing these patterns in different contexts and only accepting them as valid once they consistently make accurate predictions.
Empirical validation is crucial and is absent in the context of these math "puzzles".
Without additional context or constraints, the task becomes vain guesswork rather than a demonstration of logical reasoning/mathematical skill.
