Modeling Human Immunity with AI and Data Platforms

What if we could model the immune system — and use that to predict or even prevent disease? That’s the idea behind Biohub’s new Virtual Immune System project—a flagship effort to model the incredible complexity of the human immune system. By building AI models that can reason about how cells interact and respond, we aim to simulate immune therapies, reprogram cells, and ultimately prevent disease before it begins. https://lnkd.in/gfn96_Dh

The Virtual Immune System is an ambitious new initiative with transformative potential combining Biohub's high-throughput data generation platforms with powerful AI models. Our goal is to create a dynamic, systems-level model of the immune system that captures its complexity and adaptability in health and disease. By integrating multimodal datasets with advanced machine learning, we aim to generate actionable insights into immune cell behavior and interactions. Stay tuned - won't be long until we have more to share!

Folks, I’m leaving this here for posterity. Take a look at the Biohub and Aly Khan’s vision for modeling the immune system. Once this framework is operational, you could feed in old, dilapidated flow cytometry (protein) data (say from blood, collected in the clinic) and ask: What are the genetic, epigenetic, cell-to-cell interactions, cell-to-microenvironment interactions, etc that will lead to this end-state protein phenotype we collected from this patient 10-20 years ago (or 5 minutes ago)? The AI model could then reverse-engineer the possible permutations that will give rise to your particular cell-states. Which means that with a computer, the models, and some (old or new) protein data from blood or tissue you could diagnose disease with precision across the entire-continuum of immunology!   For researchers, once you know what’s wrong, you can perturb the cells in-silico to research a cure all the while understanding the off-target effects before it ever touches the patient! Truly remarkable!

AI-Powered Single-Cell Biology: The Future Is Here Big news from the Chan Zuckerberg Initiative (CZI): they’re doubling down on single-cell biology and putting their Biohub innovation network front and center. This isn’t just a funding announcement—it’s a signal that the fusion of single-cell science and AI is no longer optional. It’s the engine driving the next wave of biomedical breakthroughs. FOMO yet? No question the game is changing: ·       Single-cell + AI = Speed and Precision: Decoding cellular complexity and predicting therapeutic response at scale is becoming the norm, not the exception. ·      Data Is Getting Multimodal: Single-cell, spatial, proteomics—all integrated with AI to uncover biomarkers and accelerate translational insights. ·      Infrastructure Is the New Competitive Edge: High-performance computing, generative AI, and massive biological datasets are shaping how fast we can move from hypothesis to cure. CZI’s approach—collaborative labs, GPU clusters, cell models—sets a clear tone: the organizations that master this intersection will lead. The leaders will be those who invest boldly, scale smartly, and build clarity into their strategy. Those that don’t? They’ll be playing catch-up sooner than they think. AI-powered single-cell biology isn’t a fad or trend. It’s here, and it’s reshaping timelines for disease prevention and treatment.

Artificial intelligence is reshaping how we fight infectious diseases. At Vanderbilt University Medical Center, Prof. Ivelin Georgiev and his team have developed MAGE – an AI model capable of designing functional human antibodies entirely from scratch. By using “protein language models,” MAGE can recognize and generate antibodies against viral strains it has never encountered before. This could transform pandemic preparedness, cutting development times from months to days and enabling faster responses to emerging viruses. Published in Cell, the study marks a crucial step toward AI-driven biologics for infections, cancer and autoimmune diseases. As highlighted by Marion Koopmans (Erasmus MC) and Frank Aarestrup (DTU - Technical University of Denmark) in The Lancet Infectious Diseases, AI is becoming an indispensable “co-scientist”, integrating human, animal and environmental data to detect early warning signs of outbreaks. When computation meets biology, the result is not just speed, but foresight. #healthcare

𝐀𝐈 𝐜𝐥𝐚𝐫𝐢𝐟𝐢𝐞𝐬 𝐍𝐎𝐃𝟐 𝐩𝐚𝐭𝐡𝐰𝐚𝐲 𝐝𝐫𝐢𝐯𝐢𝐧𝐠 𝐂𝐫𝐨𝐡𝐧’𝐬 𝐢𝐧𝐟𝐥𝐚𝐦𝐦𝐚𝐭𝐢𝐨𝐧 UC San Diego investigators report that an artificial intelligence–guided analysis resolves a decades-long question about NOD2 in Crohn’s disease by defining macrophage programs that tip the gut toward inflammation or repair. Findings integrate machine learning with molecular validation to explain how specific NOD2–girdin interactions maintain immune balance. • AI signature (53 genes) separates inflammatory from tissue-healing macrophages in human colon samples; identifies girdin as a key partner for NOD2 signaling. • Loss of the NOD2 region that binds girdin explains failure to neutralize pathogens and the shift toward chronic gut inflammation in Crohn’s disease. • In vivo confirmation: Mice lacking girdin develop dysbiosis, small-intestinal inflammation, and sepsis, supporting a mechanistic link. 𝐈 𝐛𝐫𝐢𝐝𝐠𝐞 𝐭𝐡𝐞 𝐠𝐚𝐩 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐦𝐞𝐝𝐢𝐜𝐢𝐧𝐞, 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲, 𝐚𝐧𝐝 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧, 𝐞𝐦𝐩𝐨𝐰𝐞𝐫𝐢𝐧𝐠 𝐩𝐞𝐨𝐩𝐥𝐞 𝐚𝐧𝐝 𝐢𝐝𝐞𝐚𝐬 𝐭𝐡𝐚𝐭 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐠𝐥𝐨𝐛𝐚𝐥 𝐡𝐞𝐚𝐥𝐭𝐡. 📊 Follow for verified medical updates and evidence-based insights 🌱 Share to support data-driven care and innovation 💬 Connect or DM to collaborate or feature your breakthrough #gastroenterology #IBD #immunology #AIinMedicine

RNA-seq to monitor minimal residual disease (MRD) in multiple myeloma? Yes, it's possible. A recent Nature study compared RNA-seq with traditional DNA-based NGS for MRD detection and found that RNA-seq achieved higher sensitivity and stronger prognostic value. Key findings: • RNA-seq detected clonal immunoglobulin sequences in 100% of samples, reaching a sensitivity of 10⁻⁶, outperforming targeted DNA-based NGS • Higher somatic hypermutation rates were linked to missed detections in DNA-based methods • MRD-negative results in peripheral blood detected by RNA-seq correlated with significantly better patient survival. MiXCR was the key tool to assemble sequencing reads into clonal sequences, enabling precise immune repertoire reconstruction and MRD tracking. Now, with Platforma, you can run a complete RNA-seq workflow - PCA, heatmaps, differential expression, volcano plots, and functional enrichment analysis - without coding. Thank you to the authors! Yuyue Ren, Ming Liu, Jiancheng Fang, Lianjie Wang, Yanming Xue, Weiwei Zhao, Juan Liu, Liqian Yu, Yinglan Jin, Liyan Chen, Yaoyao Tian, Xinyu Gao, Xiaomin Zhang, Biqing Han, Dandan Yao, Yan Song, Xi Chen, Fang Wang, Xiaoli Ma, Hongxing Liu & Wei Wang 

🦠 Codebreaker: AI tackles ‘viral hijacking’ by dangerous diseases: PAI Fellow, Dr Yashwanth Subbannayya, and his colleagues from the Centre for Integrative Omics Data Science (CIODS) Yenepoya (Deemed to be University), India, are investigating ‘Viral Hijacking’ —a process where viruses exploit gaps in their own toolbox by seizing control of human cellular machinery. 😷 This is part of a collaborative effort between the University of Surrey and Yenepoya University, India. They are examining how a serious emerging virus, called the Nipah Virus (#NiV), hijacks certain processes inside human cells to survive and cause disease. 💡 Crucially, the team has developed an AI-augmented computational pipeline to perform this investigation. The Surrey team, including Dr Subbannayya and Dr Sneha Pinto, worked alongside the CIODS team, led by Professor Rajesh Raju and Vineetha Shaji, to carry out this work. This method, which leverages machine learning-based prediction tools and data mining techniques, is highly transferable, meaning it can be quickly applied to study other dangerous, emerging viruses, accelerating the discovery of new antiviral strategies. 👩⚕️ In short, this research provides a detailed 'hit list' of human switches (kinases) that the Nipah virus is using. By identifying these specific interactions, the study offers new therapeutic targets. If scientists can develop drugs to block these specific human kinases only when they are interacting with the NiV proteins, they might be able to stop the virus from functioning without harming the healthy host cell, paving the way for targeted antiviral therapies.   The research is set to be published in the journal, Frontiers in Bioinformatics and the paper can be read here: https://lnkd.in/edYyXPGX

🚨BREAKING: Scientists Just Cracked The Code On Creating Life-Saving Antibodies From Scratch This changes everything we know about fighting deadly viruses. Vanderbilt University Medical Center researchers have achieved what seemed impossible just years ago. They've developed AI protein language models that can design functional human antibodies from scratch. No blood samples needed. No antigen proteins required. Just pure computational power. Their MAGE (Monoclonal Antibody Generator) system can create antibodies against emerging threats like RSV and avian influenza faster than traditional methods. But here's what makes this truly revolutionary: → We can now respond to pandemic threats before they fully emerge → Treatment development shifts from reactive to predictive → Applications extend beyond viruses to cancer, autoimmunity, and neurological diseases This isn't just an incremental improvement. It's a complete paradigm shift in how we approach medical treatment. With up to $30 million backing from ARPA-H, this technology could transform our response to future health emergencies. Imagine designing targeted treatments for unknown viruses using only computer algorithms. That future is now here. What implications do you see for this AI-driven approach to antibody design? Share your thoughts below! 👇

🧬 AI Just Learned the Language of Life...And It’s Designing Antibodies From Scratch In a breakthrough that could transform how we fight disease, researchers at Vanderbilt University Medical Center have shown that artificial intelligence can design monoclonal antibodies capable of preventing or reducing severe viral infections faster than ever before. Published in Cell by Cell Press (Nov 4), the study used a “protein language model” (similar to the large language models that power ChatGPT ) to teach computers how to “speak” the molecular language of antibodies. The result? 💡 AI-generated antibodies that recognized and neutralized viruses like RSV and avian influenza without needing a template from human samples. Their system, called MAGE (Monoclonal Antibody Generator), could design antibodies against emerging viruses that don’t even exist yet. “This is an early milestone toward our ultimate goal — using computers to design novel biologics from scratch,” said Dr. Ivelin Georgiev, professor of Pathology, Microbiology and Immunology, and director of the Vanderbilt Program in Computational Microbiology and Immunology. Perry Wasdin, PhD, a data scientist in the Georgiev lab, was involved in all aspects of the study and is first author of the paper. Beyond viruses, this technology could reshape treatments for cancer, autoimmune, and neurological diseases. Accelerating discovery from years to days. The future of medicine is learning to write its own code. #AIinMedicine #Biotech #Immunology #ProteinEngineering #HealthcareInnovation #Vanderbilt #DrugDiscovery #LLM #DigitalBiology #ARPAH #FutureofHealth