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May 7 •
Guidelines and Rules
This community exists for one reason — to make molecular biology genuinely accessible to people who need it for their work. Whether you're a data scientist working with genomic data, a researcher from an adjacent field, a science communicator, or someone transitioning into biology, you belong here. These guidelines exist to keep the community useful, respectful, and worth your time. Be curious, not performative There are no stupid questions here. If you don't understand something, ask. The whole point of this community is that biology can feel impenetrable from the outside — asking for clarity is exactly what this space is for. You will never be made to feel embarrassed for not knowing something. Be specific when you ask questions The more context you give, the better the answer you'll get. Instead of "I don't understand gene expression," try "I'm working with RNA-seq data and I'm not sure what normalisation method to use — can someone explain why this matters biologically?" Specific questions get specific, useful answers. Engage with the journal club Every week a real recent paper with clinical implications gets broken down here. Read it, ask questions, share what surprised you, push back if something doesn't make sense. The journal club is only as good as the conversation around it — your engagement makes it better for everyone. Self-promotion — one dedicated space only You're welcome to share your own work, papers, projects, or resources — but only in the weekly "Share Your Work" thread pinned at the top of the community. Unsolicited self-promotion posted anywhere else will be removed. This keeps the feed focused and useful. No misinformation Biology is a field where precision matters. If you share something, make sure it's accurate. If you're not sure, say so. If you see something that looks wrong, flag it respectfully rather than publicly calling it out — send a DM or tag me directly. Respect everyone's starting point Members here come from wildly different backgrounds — some have PhDs, some have never taken a biology class. Both are equally welcome. Do not condescend, do not gatekeep, and do not make anyone feel like their question is beneath the community. If you wouldn't say it in a professional meeting, don't say it here.
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May 7 •
Welcome to Biology Unlocked
Really glad you're here. This community exists because biology has a gap problem. Brilliant people from data science, engineering, chemistry, bioinformatics, and science communication are working with biological concepts every day — and nobody gave them a proper map. That's what Biology Unlocked is. Here's how to get started: Step 1 — Head to the Classroom Start with the Foundations course. It's designed for every background and every starting point. Lessons 1.1 and 1.2 are free previews — available to everyone before you dive into your specialist track. Step 2 — Choose your track Once you've completed Foundations, move into the track that fits your work: Biology for Data — if you work with biological datasets Biology for the Bench-Adjacent — if you work alongside biologists Biology for Communicators — if you write, edit, or report on biology Step 3 — Join the journal club Every week I break down a real recent research paper with clinical implications — methods, data, figures, and what it actually means. Live sessions are open to everyone. Recordings are available to Practitioner and Immersive members. Step 4 — Ask questions This is the most important step. Don't sit with a question you don't understand. Post it in the feed, bring it to a Q&A session, or DM me directly. There are no stupid questions here — only ones that haven't been answered yet. A note from me: I built this because I kept seeing the same gap after 10 years of editing 500+ manuscripts — people who were exceptional in their own fields, hitting a wall with biology. You're not behind. You just needed the right starting point. This is it. Welcome aboard. Akshi
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6d •
Journal Club Number 6 is happening tonight!
Your genes do not always remain inside your cells. Sounds weird right? We were always taught that DNA remains sealed inside your own cells and is only passed on to your own daughter cells. What we did not know is that DNA transfer between adjacent cells is possible. And that is what a 2026 paper published in Cell showed. It turns out, chromosome-sized chunks of DNA can be passed from one human cell to an adjacent one. The point to note here is that the DNA does not pass from mother cell to daughter cell through replication. This happens through a process called horizontal gene transfer. The DNA travels through these thin tubes that are briefly formed between two touching cells, enters the neighbour's nucleus, integrates into its genome, survives cell division, and switches on. What fascinates me is the proof. The authors grew male and female cells together, and found pieces of the Y chromosome inside the female cells, with the male-specific genes actively switched on. A female cell cannot manufacture a Y chromosome. This only happens under 5% of human cells, so it is not a common process of gene transfer. What is notable is that a process that we used to assume happens only in bacteria—i.e., horizontal gene transfer between bacteria is how they develop antibiotic resistance—is also possible in human cells. What is also important is that the cells most likely to do it are the genomically unstable ones. Similar to the ones in a tumor. In today's Biology Unlocked Journal Club, we discuss this paper. Here is what we will cover: · How DNA physically escapes the nucleus and crosses into another cell · The Y chromosome experiment, and why it is a masterclass in experimental design · Why "rare" is the most important word in the paper, and what it means for how we read cancer genomes · The questions this opens that nobody can answer yet The recording goes live at 7pm Sydney time today. This is also fascinating to me because of the implications of this paper in cancer research. If cells can transfer DNA to each other, how much of what we currently know about gene transfer is cancers needs to be revisited? Does this have implications in cancer plasticity, clonal expansion, and therapeutic resistance? Only time will tell.
8d •
Biology Unlocked Article #9
Anthropic just launched an AI workbench for scientists. It connects 60 scientific databases in one place. It still cannot tell you if your science is any good. Claude Science went live this week. Genomics, single-cell, proteomics, structural biology, all in one environment. Ask a question in plain language, get an answer synthesised across databases that used to take a full day to query separately. Every output carries a full audit trail. The exact code, the environment, the full history of how the result was made. That last part is important. We all know that reproducibility is a sore point for modern science, and most AI tools have made it worse, not better. This one is built to make it traceable. Here is what it does not do. It is not a new model. It is not smarter about biology than the model underneath it. It is a better interface for the tools you already use. Which means it will run whatever pipeline you give it, beautifully, whether or not that pipeline is asking a biologically sensible question. A well-organised analysis of the wrong question gives you a well-organised wrong answer. With a full audit trail attached. The tool removes the friction. It does not remove the need to know what you are doing. AI for science is not intelligence about science. It is infrastructure for science. The intelligence still has to come from you. If you connected every database you use into one interface tomorrow, what is the first question you would ask it? #bioinformatics #datascience #computationalbiology #AIinbiology
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12d •
Journal Club No. 05 drops tonight at 7pm Sydney.
This one is different from the others. We are not reading a lab discovery. We are reading a tool: AlphaGenome, the model Google DeepMind released this year that takes raw DNA sequence and predicts thousands of things about what it does, at single-letter resolution. On paper it sounds like the genome is solved. It is not, and the reason why is the most useful part of the session. Here is the detail worth sitting with before you watch. The model hands you a score. This variant probably changes gene expression, by this much, with this confidence. That score is real, and it is often right. But a score is not the same thing as knowing what a change in the genome actually does. The model can tell you that something shifts. It cannot tell you why, or what the biological story behind it is. That gap between prediction and understanding is not a footnote. It is the whole reason biological fluency still matters even when the AI is very good. We cover what AlphaGenome gets right, where it quietly breaks, and what you can and cannot trust it for if you work with genomic data yourself. No background needed. Recording goes up here at 7pm Sydney, free to watch. Just head over the to Classroom and click on “Journal Club” or click on this link (https://www.skool.com/biology-unlocked-7569/classroom/becc80e4?md=13cb2db885b94e94b7360e549df8f738) to watch! Paper: Avsec et al. (2026). Advancing regulatory variant effect prediction with AlphaGenome. Nature 649, 1206–1218. DOI: 10.1038/s41586-025-10014-0
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