Congratulations to the 2026 KidneyCure Grant Recipients! 34 new investigators join 22 continuing grantees in a cohort of 56 researchers. More than $4 million in research funding supports their work advancing kidney research and improving patient care. Meet the recipients: kidneycure.org/pages/qa.aspx

We’re blessed to be able to represent the maple leaf 🍁 HAPPY CANADA DAY!!! 🇨🇦

ROUND OF 16 BOUND FOR THE FIRST TIME EVER! WE DID IT, CANADA!!! 🇨🇦 #CANMNT #OurGameNow

This is really cool (and wild): Scientists simulated a complete living cell for the first time. Every molecule, every reaction, from DNA replication to cell division. The paper (Luthey-Schulten et al., Cell 2026, doi.org/10.1016/j.cell.2026.…), just out today, used JCVI-Syn3A — a synthetic minimal bacterium with fewer than 500 genes. A 3D+time simulation of the full 105-minute cell cycle: DNA replication, protein translation, metabolism, division. Every gene, protein, RNA, and chemical reaction tracked through physical space. It took years to build. Multiple GPUs. Six days of compute time per run. And this is the simplest possible cell. A human cell has ~20,000 genes. It lives in tissue. It interacts with neighbors. It differentiates. It responds to drugs in ways that depend on context we haven't fully measured. Mechanistic simulation of the minimal cell costs 6 GPU-days for 105 minutes of biology. You cannot scale that to human cells. The complexity isn't 40x harder. It's exponentially harder. This is why the field pivoted to data-driven models. You can't hand-encode the regulatory wiring of a human hepatocyte. But you can learn it — if you have the right perturbation data collected across enough diverse biological contexts. The two approaches aren't competing. Papers like this generate the ground truth that future ML models need for validation. But the path to a clinically useful virtual cell runs through foundation models, not through scaling up mechanistic simulation. Amazing work!