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How to join Journal Club
Hey! If you are new here and looking to attend the journal club, just click on Calendar and join the live call at 7PM AEST every Saturday! See you there!
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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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Biology Unlocked Article #5
Two patients. Same disease. Completely different gene expression data. Same diagnosis. Same mutation. Same treatment plan. But Patient X responds to the drug. Patient Y does not. This happens every day in clinical research. And it confuses data scientists, bioinformaticians, and clinicians alike because if the mutation is the same, the data should look the same. It does not. Here is why. All humans carry essentially the same set of genes. But carrying a gene and expressing a gene are two completely different things. The expression of every gene in your body is controlled by a complex web of intrinsic factors like your age, your sex, and your other genes, and extrinsic factors like your environment, your diet, your stress levels, and your history of illness. This means the same gene, in two different people, under two different conditions, can behave in completely opposite ways. Now here is where most data scientists make the critical mistake. They look at Patient X and Patient Y’s gene expression data and ask: why is gene ABCD more highly expressed in Patient X than in Patient Y? That is the wrong question. Genes do not work alone. They work in networks. A single gene is almost never responsible for a disease, a drug response, or a clinical outcome. What matters is the pathway, which is the group of genes working together to produce a biological effect. The right question is: why are genes ABCD, EFGH, and HIJK together more highly expressed in Patient X than in Patient Y? Do they form a pathway? Is something regulating that pathway differently in these two patients? That shift, from looking at genes individually to looking at genes as networks, is the difference between a data scientist who produces results and a data scientist who produces insights. And it is not something any AI tool will tell you unprompted. It requires biological understanding. This is exactly what the Biology Unlocked Journal Club is built for. Every week we take a real paper and ask the right questions together.
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Thank you for being part of Journal Club No. 03.
Whether you tuned in live, dropped a question in chat, or are catching the recording now, thank you for spending part of your Saturday reading a real paper with us. We covered a lot of ground. Cas13, the RNA-targeting member of the CRISPR family. Where and how it actually cuts, mapped down to the single nucleotide. Why its collateral cleavage, the part that looked like a flaw, is the exact mechanism behind rapid diagnostic tests. And RNA segment editing, a new tool built from all of that, including a proof of concept on a Huntington's disease RNA. If you have a question that did not make it into the live chat, drop it below. I will work through everything posted here over the course of the week. The full paper is linked below if you want to read it yourself. It is open access. Lam et al. (2026), Molecular basis of target RNA cleavage by Cas13, Nature Communications.DOI: 10.1038/s41467-026-71578-7 Journal Club No. 04 is already picked. We are reading Sang and colleagues, a 2026 paper on a molecular glue complex breaking down, and how that drives resistance to RAS inhibitor drugs in cancer. If you work anywhere near oncology, drug resistance, or targeted therapy, this one is worth being in the room for. See you at the next one.
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