International Centre for Mathematics in Ukraine - ICMU ICMU supports top-level research in mathematics, with special emphasis on training younger generations of scientists and the development of mathematics in Ukraine.

In another display of incredible resilience, Ukrainian mathematicians in 2022(❗) opened a new International Centre for Mathematics in Ukraine (ICMU): icmu.ua/en

It was pleasure to give an online mini-course on Bayesian Statistics to Ukrainian students and scientists: icmu.ua/en/events/in...

We are excited to share GPN-Star, a cost-effective, biologically grounded genomic language modeling framework that achieves state-of-the-art performance across a wide range of variant effect prediction tasks relevant to human genetics.
www.biorxiv.org/content/10.1...
(1/n)

My MSc student Indrė Blagnytė has a #preprint up on @biorxivpreprint.bsky.social on influenza D virus: www.biorxiv.org/content/10.1.... Flu D was discovered back in 2011, mostly circulating in cattle. Despite lots of research, a comprehensive analysis of its phylogeography had been missing. 1/6

A short blog post about a recent preprint (work hatched as a very fun all-postdoc collaboration at @sfiscience.bsky.social)

The term 'affinity maturation' understates the influence of somatic hypermutation Three recent papers quantify how nucleotide-level mutation processes drive antibody evolution.

Why does selection feel so weak relative to mutation in affinity maturation? A new blog post giving three perspectives, including our new transformer-based model of natural selection on antibodies: matsen.group/general/202...

And @vmminin.bsky.social! Somehow I couldn't find you before.

Ultimately, direct comparison between the approaches is hard since their three models are so different, but that's what makes them so complementary. With @matsen.bsky.social @yun-s-song.bsky.social @wsdewitt.github.io, and others I can't find on here, but may have missed!

effective vs intrinsic birth rates over time for several simulated GCs using final, fitted data parameter values

they infer what we call an "effective" birth rate (left column) that is more biologically interpretable than the "intrinsic" rate inferred by deep learning (center column), but which also varies with time and across GCs.

It also turns out there's some subtleties in comparing to the more analytic traveling wave and branching process approaches:

Data results: affinity fitness response curves

The results consist of a curve, or rather, two versions of (hopefully) the same curve: one infers the parameters of a sigmoid shape, the other infers independent bin values.

diagram of inference procedure

Finally, we applied the model to real data, inferring the affinity-fitness response curves for many potential parameter values, choosing the best combination based on summary statistic matching.

diagram of simulation training workflow

We then trained a deep learning model on simulation samples with a wide variety of parameter values.

diagram of simulator workflow

Here I'll focus on the deep learning approach. We first built a birth-death-mutation simulator and carefully matched it to data to ensure we understood the processes underlying the experimental results.

Bayesian inference of antibody evolutionary dynamics using multitype branching processes When our immune system encounters foreign antigens (i.e., from pathogens), the B cells that produce our antibodies undergo a cyclic process of proliferation, mutation, and selection, improving their a...

whereas a branching process model arxiv.org/abs/2508.09519 and deep learning (this thread) zoom in to use detailed lineage trees to inform inference.

Replaying germinal center evolution on a quantified affinity landscape Darwinian evolution of immunoglobulin genes within germinal centers (GC) underlies the progressive increase in antibody affinity following antigen exposure. Whereas the mechanics of how competition be...

A traveling wave model takes a very high level view, essentially modeling histograms of affinity (Fig 6 in doi.org/10.1101/2025...)

Higher affinity antibodies, on average, have more offspring. But what exactly does this relationship look like? We used three complementary approaches to measure it:

In the weeks after we're exposed to a pathogen, our antibodies evolve toward higher affinity. The cellular mechanisms here are fairly well understood, but a recent experiment from @victora.bsky.social gave us the opportunity to also learn about the mathematical dynamics of this selection.

Inference of germinal center evolutionary dynamics via simulation-based deep learning B cells and the antibodies they produce are vital to health and survival, motivating research on the details of the mutational and evolutionary processes in the germinal centers (GC) from which mature...

In a new preprint we use deep learning on lineage trees to infer the functional form of the relationship between affinity and fitness that controls antibody evolution in germinal centers: arxiv.org/abs/2508.09871 🧵

Antibodies are highly diverse, but most possible sequences are unstable or polyreactive. In this work, just published in Cell Syst., we propose a new source of data for modeling constraints from these properties. Our models show clear improvements in predicting Ab dysfunction. (1/n)
t.co/qCZERPUMPF

Excited to share my new preprint developed with @matsen.bsky.social, in collaboration with Marius Brusselmans, Luiz Carvalho, @msuchard.bsky.social, and @guybaele.bsky.social, on the biological causes and impacts of tree space ruggedness in phylodynamic inference. 1/
www.biorxiv.org/content/10.1...

Replaying germinal center evolution on a quantified affinity landscape Darwinian evolution of immunoglobulin genes within germinal centers (GC) underlies the progressive increase in antibody affinity following antigen exposure. Whereas the mechanics of how competition be...

Wanted to highlight our latest preprint--a huge effort by multiple people and labs, but led primarily by @wsdewitt.github.io, Tatsuya Araki, and Ashni Vora, in a very close wet-dry collaboration with @matsen.bsky.social’s lab at the Hutch

www.biorxiv.org/content/10.1...

Two new approaches to learn about antibody somatic hypermutation We try to learn about SHM mechanism and do so by directly using mechanistic models, and by exploring deep architectures.

New blog post: our recent work to understand the somatic hypermutation process that enables antibodies to incrementally improve.

A story of running into the limitations of deep learning, but still gaining biological insight along the way.

matsen.group/general/2025...

Let's blog! My return to blogging, and why I hope to read your blog too.

matsen.group/general/2025...

A picture of a white-necked Jacobin hummingbird with a blue head, long beak, blue-green shoulders, and a white belly. The hummingbird is sitting on a branch and staring into the camera.

I have a new paper about these cute little guys (and some girls)!

White-necked Jacobins are interesting because all males and some but not all (!) females are brightly colored. The scientific question is why some but not all females?

(Img credit: Kate & Sam)

Kudos to NIH for getting rid of the odious 12 month delay in public access (a capitulation to the demands of scientific societies). But, as the new policy assumes grantees will continue to publish their work in traditional scientific journals, it remains unacceptable.

grants.nih.gov/grants/guide...

Where do our rights come from? Believe it or not, the United States has been rolling back our rights for 40 years. The biggest expansion of the peoples' rights in American history actually occurred between 1953 and 1969, during the period of American history known as the "Warren Court."

THREAD.