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.

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.

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.

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

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.