So many thoughts on this: 1. Biotech is very much about who you know, well connected investors are a huge accelerant. 2. Ignorant Money is worse than bootstraping; an ill-informed investor can lead you in the wrong direction 3. Imagine, too, there is a selectivity bias. Informed investors bet on the "better" horses, because they know what to look for when doing diligence.

🔥 Week 40! 68 Bay Area Bio-economy events, Sept 28-Oct 4 🔥 🔗 docs.google.com/spreadsheets… 🔗 𝗕𝗬 𝗧𝗢𝗣𝗜𝗖 14 🚀 Startups & Ecosystem 10 🏥 Clinical Care & Health Equity 9 💊 Therapeutics & Drug Development 7 🤖 AI & Data for Science 6 🌱 Food, Ag & Climate 5 🧬 Genomics & Protein Design 5 🔬 Lab Tools & Automation 5 ⏳ Longevity, Metabolism & Wellness 4 🧫 Cell & Molecular Biology 3 🧠 Neuroscience & Brain Health 𝗕𝗬 𝗙𝗢𝗥𝗠𝗔𝗧 17 Seminar / Lecture 9 Webinar 9 Panel / Salon 6 Mixer / Social / Outdoor 5 Vendor Lunch & Learn / Expo 4 Conference / Symposium 4 Pitch / Demo Day 4 Info Session 4 Workshop / Hands-on 3 Grand Rounds (CME) 3 Patient & Community Day 𝗕𝗬 𝗟𝗢𝗖𝗔𝗧𝗜𝗢𝗡 21 Berkeley 18 San Francisco 17 Virtual 5 Oakland 3 San Carlos 2 Foster City 2 South San Francisco

If you work in bio x tech in the Bay Area, Boston, NYC, San Diego or Minneapolis, there's a free Slack posting 7+ jobs and 25+ events every week. Almost 14,000 people are already in it. It's @bitsinbio, and it's the most active I've seen it in three years. Events per week, last two months: - Bay Area: 4+ - Boston: 4+ - NYC: 2 to 3 - San Diego and Minneapolis: 1 to 2 - Everywhere else: about 6 more in #events, plus new chapters in Dallas and Nashville The jobs: - 4 in 10 are computational (ML, data, software) - 1 in 3 are wet lab and bioprocess - The rest are BD, ops and partnerships - Lots of founding roles: founding scientists, a CSO, co-founder searches - Mostly Boston and the Bay Area, plus remote, London and Copenhagen Your gateway to the nexus is here: join.slack.com/t/bitsinbio/s…

In 2010 I helped put on @NASAAmes's first workshop on what synthetic biology could do for space exploration. @lynnjrothschild, my adviser at Ames, and I spent years arguing that you could send a single cell to Mars and let it build from whatever it found there. The microbe was never the hard part. Earth already has bugs that make plastic, food, and fuel. The hard part was the dirt. Nobody knew what Martian regolith actually gives a cell to eat, or how badly the perchlorate, salts, and heavy metals would hurt it. You can't model your way out of that question. Someone has to grow things in it. That's exactly what @Pioneer__Labs has been doing, and this week they showed it works. First they turned real rover and lander data, from Phoenix's Wet Chemistry Lab and Curiosity's SAM instrument, into Defined Mars Media: a lab recipe for the nutrients and toxins in Mars dirt. Then they screened 16 Earth organisms against it and ran more than a trillion cumulative cell divisions of evolution. The result is sPL.001, a strain of Cupriavidus necator that pulls every nutrient it needs from Martian dirt, water, and air, and makes more than 3x the PHA bioplastic of its parent. It needs no fertilizer from Earth and no washing the perchlorate out first. Their reference design says one rocket's worth of equipment could build radiation-shielded habitats for a small city in about four years. It's also built to be safe. It only grows inside a bioreactor fed acetate made from Martian CO2. Even a catastrophic leak clears planetary protection limits by several hundred times. This is what data generation looks like for an interplanetary species. Rover measurements became a lab recipe, the recipe became a trillion cell divisions of experiments, and the experiments produced an organism. Pioneer is the only team I know of doing this end to end. Congrats to @erika_alden_d and co-founder @StorkDevon, and to @unattermann, Tom Pedersen, Nathan Hicks, Jordan Mancuso, Karin Isaev, Max Schubert, Fatima Martin, Jonathan Liu, Harley Greene, and Edward Sukarto. Thanks to @AsteraInstitute for backing it. @lynnjrothschild, Pete Worden, Stephanie Langhoff, Chad Paavola: this is where that 2010 workshop was pointing. Last year I joined Erika, Chris McKay, Pamela Silver, @edwinkite, Robin Wordsworth, @mason_lab, and others on a @NatureAstronomy paper making the case for Mars terraforming research. Organism one of five is already here. Erika has spoken at @SynBioBeta, and so has @NikoMcCarty, whose @AsimovPress interview with her is the best 51 minutes you can spend on this. It is a joy to see this research making headways and I am excited to see what the future holds.

It's the end of one era at @nuclera and the start of another. Michael Chen, who co-founded Nuclera in 2013 and has led it ever since, is stepping down as CEO. He moves into the role of Chief Scientific and Business Officer. Carl Raimond takes over as CEO to lead the company's next phase of growth. Carl comes from @thermofisher, where he ran Proteomic Sciences. Before that he was President of @OlinkProteomics and helped lead it through its $3.1 billion sale to Thermo Fisher. He also led teams at @PerkinElmer, @Agilent, and Affymetrix. He knows how to take a lab tool and get it into labs everywhere. That is exactly the job now. But I want to say something about Michael. He is a structural biologist who got tired of waiting months for proteins. So he built a machine to make them faster. Over more than ten years, that idea became a team of more than 100 people in Cambridge and Boston, and a product in labs around the world. Very few founders get to see that. Michael did, and he stayed generous with this community the whole way. Here is what Nuclera does. Its benchtop system, eProtein Discovery, takes in DNA and tests many versions of a protein at once. Then it makes the ones that work. Purified protein comes out in under 48 hours instead of weeks. - membrane proteins, including hard drug targets - full-length antibody screening - a path to test AI-designed proteins fast Nuclera and @SynBioBeta go back years. They exhibited with us, including at SynBioBeta 2024, and their team has always been a joy to have in the room. They have supported us, and we have been proud to support them. Michael, thank you for everything you built. Carl, welcome. I cannot wait to see you carry Michael's mission forward. #SynBio #ProteinEngineering #AIforScience

Excited to introduce @ReactorfieldAI Cohort 1: @merge - brain-computer interfaces with no implants, $250M seed led by OpenAI and Bain @episteme - a new R&D institution, cofounded by Louis Andre and Sam Altman @RetroBio_ - unicorn longevity startup, adding 10 healthy years to human life. In phase 1 trials @NorthwellHealth - NY's largest healthcare system, with Chief Biotech Officer @itchdoctor @StrandTx - programmable mRNA medicines out of MIT, lead cancer drug already in patients @MayoClinic - turning leading clinical science into new ventures represented by @vera_mucaj @xiliotx - publicly traded biotech making cancer therapies to activate inside tumors @marathonfusion - producing the world's most valuable isotopes from compact fusion reactors @KopraBio - viruses that turn cancer cells into factories for their own destruction @radifymetals - hydrogen-plasma reactors that turn rare-earth oxides directly into metal @FoundationManf - rapid domestic CNC machining for frontier hardware @MassMagnetics - US-made high-performance magnets from recycled rare earths @algabiosciences - turning cattle methane into calories, peer-reviewed cuts of up to 63% @lumehealth - building a wearable that measures cortisol continuously from sweat @liu_liu_lemon - smart materials for med tech and robotics, @Harvard PhD, Fulbright Fellow

I read the @AnthropicAI preprint this morning with a second cup of coffee, and somewhere around page six I stopped reading and started rereading. The setup is simple enough. Anthropic built a molecular biology lab in the Bay Area this spring, staffed it with people who have spent their careers on strange proteins, and pointed Claude at a large database of DNA. About 950 agents worked for 21 hours. They pulled more than 200,000 reverse transcriptases, the enzymes that copy RNA back into DNA, and sorted through them looking for anything that didn't fit. One of them was reading the raw sequence beside an RT from a jumbo phage when it wrote, more or less to itself, that it could see a tandem repeat array by eye. Then it counted the repeats. It measured the spacing. It went back to the literature to see whether anyone had described this before, and found that the enzyme was known but the array and a small partner gene next to it were not. The team named the system ART. When they expressed it in the lab, the array came back as a set of short RNAs, which is the sort of detail that makes a CRISPR person sit up. That is where the certainty ends. Nobody has shown the enzyme is active. Nobody knows what the system does for the phage, or whether it does anything useful for us. A microbiologist at WashU said plainly that nothing here points to a rival for CRISPR, and he may be right. Dario said much the same. The part I keep turning over is in the methods: Anthropic ran the same search ten more times, and not one of those runs looked upstream of the enzyme. They all missed it. So one agent noticed, and ten did not. People are going to read that as a knock or a caveat. I read it as the most honest line in the paper. Most real discovery looks like that. Someone happens to look at the right stretch of sequence on the right afternoon, and the rest of us spend years finding out whether it mattered. Congrats to @DarioAmodei, @eric_kabrams, and the team. I'll be watching what comes out of that lab.

Today, Sculpta releases the first demonstration of bioorthogonal barcoding ("bobcoding"), the process by which we: 1) covalently attach oligonucleotide barcodes to RNA at multiple internal positions with two-step chemistry, and 2) perform a multiplexed reverse transcription reaction to generate barcoded cDNA libraries with >99% barcoding accuracy. To the best of our knowledge, it is the first time ever that either of these steps have ever been performed by anyone. No one has ever broken the barrier of multiplexing prior to any enzymatic step in library preparation, driving simplicity and higher data quality. Instead of other methods that add 0 or 1 barcode, we add one barcode every ~300bp, adding redundancy and fidelity to RNA measurements. In our first application described in our pre-print below, we used bobcodes to create the first RNA isoform-resolved drug screening platform. It beats Novartis' DRUG-seq platform by: - generating full length transcript capture of RNA isoforms, instead of just 3' end counting - capturing 25-fold more RNA splicing events - reducing barcode swapping by 10-fold - using 18 fewer PCR cycles (~250,000 less amplification) - reducing sample-to-sample variability in gene expression measurements - eliminating costly and cumbersome library fragmentation/tagmentation steps completely - reducing workflow complexity and number of steps - reducing overall protocol duration by ~25% Though our chemical barcoding method improved transcriptomic data quality while also being simpler and faster than all existing methods, the implications of this work go well beyond this particular use case: The gate to greater applications of AI in transcriptomics is not a lack of compute. Nor is it a lack of data volume. The elephant in the room has always been our limited ability to faithfully and accurately measure cellular RNAs. Sculpta now has line of sight to build what we call the first ground truth transcriptomics platform [for single cell, spatial and more] for the future of biological research, drug development and AI-enabled discoveries for the betterment of human health and longevity. [PS Until the kind folks at bioRxiv get through the apparent backlog of AI slop submissions, Sculpta will host the pre-print PDF on our website. You can sign up for product offerings and other updates at this link too] sculpta.bio/preprint

$451 million into two AI biotech companies in a single morning. @enveda closed a $311M Series E led by @CatalioCapital. Their PRISM platform identifies biologically active molecules made by plants, microbes, and the human body, then ranks them by therapeutic potential so Enveda's chemists can turn the best ones into oral medicines. Since 2019 that approach has produced 17 development candidates, three now in human trials. Two of those read out positively this year. ENV-294 improved eczema severity by 85% on average after 42 days in Phase 1b, with more data coming at EADV on October 1. ENV-308, based on a molecule the body releases during intense exercise, was well tolerated in 88 volunteers with a very clean GI profile, and it's being developed to help people keep weight off after stopping GLP-1s. ENV-6946 for inflammatory bowel disease is in Phase 1. Enveda has now raised more than $845M total, and @georgecpetro of Catalio joins the board. The syndicate is basically a who's who across tech and healthcare: Durable Capital Partners, ICONIQ, @lightspeedvp, Surveyor Capital, @TRowePrice, Digitalis Ventures, Alderline Group, and a sovereign wealth fund as new investors, alongside existing backers @BaillieGifford, @PremjiInvest, FPV Ventures, @trueventures, @KinnevikAB, Dimension, Lifeforce Capital, and @Lux_Capital. Alex Gorsky, former J&J CEO and now at ICONIQ and Alderline, is part of it too. @Basecamp_Res closed an oversubscribed $140M Series C led by @S32_VC, with Andy Conrad, former CEO of Verily, joining the board. Their EDEN models are trained on the Trillion Gene Atlas, biological data collected through benefit-sharing partnerships in more than 30 countries and built with partners including @nvidia, @AnthropicAI, @PacBio, and Ultima Genomics. First target is in vivo cell therapy: pairing EDEN-designed DNA with large serine recombinases to write complex sequences into a patient's cells inside the body, which could take a lot of the cost out of today's cell therapies. André Hoffmann, Vice-Chairman of @Roche, invested personally, and Richard Pearce joins from @biogen as Chief Business Officer. That round brought in Anthology Fund, @CatalioCapital, European Tech Collective, Firebrand River Capital, Inception Fund, King Philanthropies, NATO Innovation Fund, @nvidia, PostScriptum, Redalpine, @RockefellerFdn, Singular, Sovereign AI, and @trueventures. Reading the two announcements back to back, both companies are built on the same bet. The best starting material for new medicines is what evolution already made, and AI can finally read it at scale. Enveda cites an estimate that 99% of nature's chemical diversity has never been examined. That's the part I keep coming back to. Also kind of wild: Catalio and True Ventures are in both rounds, announced the same day. Congratulations to @viswacolluru, Nadeem Sarwar, Michael Charlton, Doug Maslin, Soumoditya Dey, and the whole Enveda team, and to @glen_gowers, Richard Pearce, and everyone at Basecamp Research.

🚨 Exclusive: Saku Biosciences and Sable Fermentation, Inc. have signed an exclusive US strategic alliance agreement, putting strain engineering and scale-up under one contract. @SynBioBeta has the story:👇 Most microbial products pass through four stages on the way to market: 1. Strain development: engineering and screening a microbe for titer, rate and yield. 2. Upstream processing: fermentation development and scale-up. 3. Downstream processing: recovery, separation, concentration and drying. 4. Formulation: turning the material into something a customer can apply or ship. Different vendors usually handle these stages, and each handoff means a new MSA and a new tech transfer. The costliest gap is between stages 1 and 2. Strains are usually screened in well plates or shake flasks, where oxygen transfer, mixing and substrate feeding look nothing like a stirred-tank bioreactor. The best performer on the bench is often not the best performer in the tank, and that mismatch shows up months later at pilot scale. Saku (strain development) Saku's PicoShells, developed in @dinodicarlo lab at @UCLA and published in PNAS, are picoliter-scale hydrogel particles with a porous shell. Each one holds a single cell and its descendants, fenced in but exposed to the surrounding media. That means more than a million clonal colonies can grow together in real production conditions and be sorted on standard equipment, up to 10 million in a day. Mark van Zee and John Alden picked Sable specifically because they knew what was missing was the scale up peice. Sable (upstream, downstream and formulation) Sable's Wake Forest, NC pilot plant runs submerged fermentation from 1L to 200L, with downstream processing including tangential flow filtration, centrifugation and spray drying, plus formulation and analytics. Its team comes largely from ag biologicals, including AgBiome, Marrone BioInnovations and Pasteuria Bioscience, Inc. Put together, Saku selects strains in the kind of environment Sable will run them in, and the results from Sable's runs go straight back into the next round of screening. "We saw an opportunity to solve one of the biggest inefficiencies in bioprocess development: the disconnect between strain selection and the conditions a strain will ultimately face at scale," said Toni Bucci, Ph.D., founder and CEO of Sable Fermentation. Sable manages the customer relationship end to end. Neither company takes back-end royalties or claims on customer IP. They are paid through success fees. The arrangement is aimed at food, agriculture and industrial biotech companies moving from lab scale toward contract manufacturing.

🚀 @ycombinator. 🤝 An acquisition. 💰 $2.6M+ in funding and fellowships. That's our 2026 @SynBioBeta Next-Gen Bio-leader alumni just 4 months post conference; 2027 applications just opened 👀 🔗 synbiobeta.typeform.com/to/B… Next-Gen BioLeaders is a one-day program at SynBioBeta for students, postdocs, early-career academics and pre-seed founders in programmable biology. You spend the day with biotech investors and founders who have built companies from scratch: VC masterclasses, small-group mentorship, a founders lunch and curated introductions. Here's what the 2026 cohort has done since May: ☘️ Nitroduck | Matej Zámečník, Matúš Grieš, Miroslav Rosputinský Just got into Y Combinator (F26). 🛡️ TwentyTwo (YC S25) | @_harm0n, Evan Seeyave Acquired by @LatchBio. They now lead Latch Biosecurity. 🧪 @cypherbio | @yaoyuyang $2M seed led by @MaCVentureCap for an AI operating system for lab work. ☀️ Solio Bio | Nathan Paumier Came out of stealth backed by Replicator VC. @jaykeasling just joined the scientific advisory board. 🫧 Flux Bio | Marika Ziesack Launched as a @Harvard / @wyssinstitute spinout to fix gas transfer in fermentation. 🦠 Outpost Bio | Jenny Yang Released Waypoint, a foundation model for microbial communities. 💾 BioCompute | @anagha_dna Moved from Bengaluru to San Francisco to build DNA data storage chips. 🧬 LUCAI Bio | Joaquin Ortiz Masllorens Won the South Summit pitch competition and Best Entrepreneur at the Puentes de Talento accelerator. 🐛 Yeast Bay Bio | Nazzy Pakpour Raised from the California Innovation Fund for yeast-based pest control. 🔬 On the research side: Sam Oliveira won a $302K @DARPA Director's Fellowship, one of seven awarded. 🪴 Nolux | Elizabeth Hann joined @activatefellows with her company Nolux and leads a Wyss Validation Project. @YeshDoctor was co-first author on a @NatureBiotech paper. If you're building in biology and want to meet the people who fund and scale it, this is your day. Selected participants receive a complimentary conference pass and up to $500 travel support. 🔗 synbiobeta.typeform.com/to/B…

Today we officially welcome Yann LeCun (@ylecun), Bob Langer, Jens Nielsen and Fabian Theis (@fabian_theis) to the Cellular Intelligence Scientific Advisory Board. Langer also joins our Board as an observer. Four people who have changed what is possible in AI, biology and medicine, now working with us to learn the rules that govern how cells change, and carry that knowledge into medicine. @FortuneMagazine's @CatGioino has the story: fortune.com/2026/09/21/moder…

Huge congratulations to Micha Y. Breakstone and the Cellular Intelligence team on the recent addition of Yann LeCun and Bob Langer to their scientific advisory board, alongside Jens Nielsen of the Novo Nordisk Foundation's BioInnovation Institute (BII) Micha was on our AIxBIO stage in San Jose back in May. His keynote made the case that roughly 20 fundamental signaling pathways account for the entire diversity of human cell types, and that the real problem is the combinations and the order you apply them in. The next afternoon he joined a panel we called "Where Does Biology Compute?" with Kim Branson from GSK, Gleb Kuznetsov from Manifold Bio, Johnny Yu from Tahoe Therapeutics, and Fabio Boniolo from Polyphron. A lot has happened since. Global rights to STEM-PD, the Phase 2-ready Parkinson's cell therapy Novo Nordisk shelved when it wound down its cell therapy division, carrying FDA Fast Track designation. Novo took an equity stake in the company as part of that deal. More than $70M raised to date from Khosla Ventures, CZI, and Novo Nordisk. LeCun left Meta last December to build AMI Labs around world models. Micha's framing to Fortune is that for Cellular Intelligence, the system the model acts on is the living cell itself. Those two ideas landing on the same board is the whole thesis in one announcement. Congrats again to Micha and the entire CI team.

🔥 Week 39! 48 Bay Area Bio-economy events, Sept 21-27 🔥 🔗docs.google.com/spreadsheets… 🔗 𝗕𝗬 𝗧𝗢𝗣𝗜𝗖 Capital & Dealmaking 13 AI & Computation 6 Drug Discovery & Trials 5 Genomics, Proteins & Omics 5 Neuro & BCI 5 Industrial & Environmental 5 Clinical & Care Delivery 4 Tools, Automation & Lab Ops 3 Career & Training 2 𝗕𝗬 𝗟𝗢𝗖𝗔𝗧𝗜𝗢𝗡 Berkeley 17 San Francisco 15 Virtual 8 San Carlos 4 Palo Alto 2 Oakland 1 South San Francisco 1

We're excited to start sharing out speakers for our @SynBioBeta conference next year. Should be one for the history books.

Excited to announce two of our keynote speakers for @SynBioBeta 2027: @geochurch, founder of more than 50 companies in the synthetic biology space, and @alexrives, who runs @biohub. Church's list includes @editasmed, Nebula Genomics, Veritas Genetics, GRO Biosciences, Rejuvenate Bio, @ManifoldBio, Gen9, Form Bio, and @itiscolossal, now at a $10.3B valuation. He developed the first direct genome sequencing method, helped initiate the Human Genome Project, started @PGorg in 2005, and runs the synthetic biology platform at @WyssInstitute. He teaches at @Harvard and @MIT. This year alone, Colossal partnered with US Fish and Wildlife to biobank every endangered species and published a gene drive plan to eliminate the New World screwworm in the US. Rives founded and led the ESM project at Meta's FAIR lab, which built the first large transformer language models for proteins, then spun @EvoscaleAI out with his former Meta colleagues after Meta shut the team down in 2023. That company raised $142M from Nat Friedman, Daniel Gross, @Lux_Capital, Amazon, and NVIDIA's NVentures and shipped ESM3, a 98B parameter model trained on 2.78B protein sequences that reasons over sequence, structure, and function at once. CZI acquired EvolutionaryScale in November 2025 and folded it into Biohub, with Rives as head of science. In May, that team released ESMFold2 and the ESM Atlas, mapping 6.8B sequences and 1.1B predicted structures to protein function. A month later they reported binder hits against five cancer and immune targets. The models are distributed through AWS Bio Discovery and SandboxAQ. He is also core faculty at @BroadInstitute and an assistant professor in EECS at @MIT. Two people making the same bet from opposite ends of the field. Rives came out of an AI lab and treats sequence, structure, and function as a single modeling problem, which is what moves a team from a disease target to a candidate binder in months instead of years. Church has been on the discovery side since 1984, when he published the first direct genome sequencing method, and has spent the four decades since turning academic work into CRISPR editing, recoded genomes, and a company list longer than most venture portfolios. That is the pairing we are programming around for 2027: the frontier AI labs that have moved inside biology, and the academic lineage that built the tools they train on. And this is just the start. We have an incredibly exciting speaker list coming together, and we will be announcing names over the coming months. Keep an eye out. SynBioBeta 2027 is May 3 to 6 at the San Jose McEnery Convention Center, and it is set to be our best conference yet. syntheticbiologysummit.com/?…

For the longest time, biotech has treated the microbiome like air resistance in an intro to physics class. Negligible, not because it's unimportant, but because it's so hard to model. Outpost Bio has shown that assumption was wrong. A model trained only on soil, ocean water, and wastewater still helps predict what happens in the human gut. Microbial communities share a common structure, the same way proteins, DNA, and RNA do. Each of those already has a foundation model. Now the microbiome has one, and it can be added to drug discovery the way air resistance gets added back to physics. The microbiome's role in oral drugs is well established. A pill spends hours in the gut before reaching the bloodstream, and gut bacteria carry enzymes that break molecules apart. Several prodrugs stay inert until a gut bacterium activates them. Checkpoint inhibitors are less obvious. They are antibodies infused directly into the blood, where they release the brakes on T cells. Gut bacteria still influence how those T cells behave. These drugs work in a minority of patients, and responders carry different bacteria than non-responders. Measuring a community is easy. 16S sequencing has been standard for twenty years. It cheaply lists which bacteria are present and in what proportion. Predicting from that list is hard. A checkpoint trial might enroll 200 patients, each with hundreds of species. With more variables than samples, a model memorizes the patients instead of learning anything general. Foundation models solve this by learning the biology first. Outpost trained theirs on half a million unlabeled samples, so it learned which species tend to appear together before seeing any outcome data. Fine-tuned on 200 patients, it only has to learn how those communities map to response. This depends on a shared structure existing. Communities from different environments can have no species, temperature, or chemistry in common, so treating them as separate problems was reasonable. Outpost tested it. They retrained with every gut sample removed, then with every human sample removed. Both versions still outperformed an untrained model on gut prediction tasks. The model, data, and benchmark are open source. Read the full article here: synbiobeta.com/read/one-mode…

Funders, funders everywhere, yet not a cent to spend. Roughly 87% of investors don't actually have any capital to deploy. They're either a zombie fund that's already deployed its capital, still raising money from LPs, or just using the title as a vanity metric. For many traveling to our Mecca of fundraising, where more than a 1/3rd of global biotech funding originates, founders often exhaust runway and resources in the wrong places chasing people that have nary a penny to proffer. That's why this October, during the one week every year @a16z reminds people it invested in the wrong event platform, I've been invited to speak at a @Partiful event dedicated to the billions of dollars in alternative funding that don't require you to sell your soul. The art of the partnership is an essential tool for any company, but often overlooked because they require you to actually deliver on your promises. Join me at 11am, October 6th at @Techweek_ partiful.com/e/GdpTkCtxHx5vE…

Another Definitive List from @SynBioBeta's own Bay Area Community Manager!

🧬 𝗪𝗲𝗲𝗸 𝟯𝟲 (𝗔𝘂𝗴 𝟯𝟭–𝗦𝗲𝗽 𝟲) · 𝟮𝟳 𝗕𝗮𝘆 𝗔𝗿𝗲𝗮 𝗕𝗶𝗼𝗲𝗰𝗼𝗻𝗼𝗺𝘆 𝗲𝘃𝗲𝗻𝘁𝘀 🧬 🌁 Bay Area Weather: high of 69, low of 58, no rain all week 🌁 Full list of event links: docs.google.com/spreadsheets…