🌍 Where would we be without science and engineering? Yesterday, I had the pleasure of an interesting conversation at the Technical University of Denmark (DTU), which reminded me of an old LinkedIn post of mine: “In my next life, I want to be an engineer.” The conversation reminded me of the synergy between biological scientists and engineers: 🔬 Biologists use cutting-edge technologies developed by engineers to explore and understand the world around us. ⚙️ Engineers, in turn, apply their expertise to craft new solutions that help address unanswered biological questions. The result? A continuous cycle where new discoveries prompt new technologies, fueling even more discoveries. This dynamic is thriving in Copenhagen's biotech and pharma ecosystems—and I love being part of it! Just for fun I prompted an AI image generator based on above text (slightly edited). Hightlighting how AI will surely (and hopefully) accelerate drug discovery in the next 10 years. Another P.S. I realize it's a bit old-fashioned to distinguish between these fields, especially with the rise of hybrid disciplines like biomedical engineering, biomaterials science, bioinformatics, and bioprocess engineering. But the core message remains: collaboration drives innovation. Original post: https://lnkd.in/denFBX2f
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"Applied science brings a more analytical approach to combination product development. It's about getting to the nitty-gritty of the physics and biology of what's going on with these therapies, how they interact with the body, and how we can improve treatments with less physical and animal testing." In our latest Crux Spotlight, we sit down with Ben Wade, Senior Applied Sciences Engineer, to explore the transformative role computational modelling plays in the medical sector. Ben explores high-concentration suspension injectable de-risking and inhalation device optimisation, explaining how analytical approaches and advanced simulation techniques: ▪️ Optimise device performance without the need for physical testing ▪️ Enhance precision in drug delivery ▪️ Speed up development cycles to improve patient outcomes Don’t miss this fascinating conversation about the cutting-edge methods shaping the future of healthcare. Watch now! https://lnkd.in/eM4sWCYH
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Sometimes, all you need to shift your perspective is a change of circumstance. Recently, Markus Gershater tells Don Davis PhD, MBA in Life Science Success Podcast how he didn't appreciate the value in DOE until he found himself in an industrial setting: "When I was in industry, I was doing biotransformations, where you’re doing chemistry, but with biological systems. That’s either an enzyme to catalyze the reaction, or even whole systems, where you put in a substrate and it gets converted by that microbe into whatever the outcome is. Early on, I was challenged by a process that had been developed in an academic environment, which I then needed to industrialize. I sat down and worked out all the different things that I might want to investigate. Naturally you start thinking about all the things that could affect your process. You keep listing them and listing them, and then you realize that there’s way more things than you can ever hope to investigate. I was very fortunate, as my dad originally worked in process development and had been talking to me about multivariate experiments—a method where you investigate multiple things simultaneously—for a long time. He’d been trying to convince me to work this way during my PhD. At the time, I thought, 'He’s my dad, what does he know?' ... But faced with an industrial setting, I realized that whereas with a PhD you have 3 or 4 years to chip away at all that biological complexity and make it work, in industry I only had 4 months. It was then that I thought, 'Maybe my dad might have known something after all.'" Want to learn more about how Markus ended up improving the yield of his process 7-fold, and what else he discussed with Don? Listen to the full recording here: https://lnkd.in/ehtsWkv2 #DesignOfExperiments #DOE #DOEForBiology
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R&D has evolved from solo efforts to dynamic, collaborative teams driving knowledge production. Organizations are now investing in technological infrastructure to support cross-disciplinary teams, speeding up experimentation and discovery. In this #webinar Brian K. Buntz, Editor-in-Chief of R&D World will interview Matt Armbrust, a Research Associate in Protein Engineering for Sarafan ChEM-H | Stanford University, and Jenny Hu of Labstep, a STARLIMS company, to discuss: 🔬 The current state of scientific experimentation from practitioners, and how R&D has changed throughout the years. 🔬 How organizations are breaking down barriers between R&D labs and scientific disciplines. 🔬 Strategies for collaborating more effectively within and across labs and scientific disciplines. Register now: https://lnkd.in/g8wW2Kxd #laboratories #research #researchanddevelopment #crossdiscipline
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Reproducibility is the cornerstone of trustworthy science, yet it remains a challenge in biomedical research. Here are key strategies scientists can adopt to improve reproducibility: 1. Clear methodology: Make detailed documentation of the experimental protocols, data collection, and analysis methods to ensure others can replicate your study. 2. Preregistration: Preregister your hypotheses, study design, and analysis plan to prevent biases and increase credibility. 3. Transparent reporting: Be transparent about any deviations from the original plan. 4. Open data: Make your data, code, and materials available to the scientific community for validation and further exploration. 5. Collaboration: Engage in cross-laboratory collaborations to test the reproducibility of key findings. Do you get the same results with a different experimenter/microbiota/setting? Bonus tip: engage with your local ReproducibiliTea journal club to keep updated about issues related to reproducibility and open science. Small changes in day-to-day research can have a big impact on reproducibility! Visit us at advansci-research.com to book a free consultation and find out how our experienced research consultants can help you plan and communicate your biomedical studies for optimum reproducibility. #Reproducibility #BiomedicalResearch #OpenScience #ResearchIntegrity
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#analyticalchemistry news Unlocking the Future of #Chemistry: How Bio-Inspired Logic Gates Could Revolutionise Medicine We're standing on the cusp of a breakthrough that melds biology with #technology in unimaginable ways. Today, I am thrilled to introduce you to a fascinating study recently published in Analytical #Chemistry by Yonghuan Chen and colleagues. Their research explores the creation of a thrombin nanochannel logic gate, a device inspired by biomemory, which represents a significant milestone in the field of chemical computing. This innovative device utilises the unique properties of thrombin, a biological enzyme, to perform complex logical operations at a molecular level. The researchers have managed to design a bio-inspired system that mimics natural memory storage processes observed in living organisms. The nanochannel functions by controlling the flow of ions, effectively making decisions and processing information based on the biochemical environment. The implications of this research are vast. Imagine medical diagnostics tools capable of executing real-time analyses inside the human body or smart drug delivery systems that activate only in the presence of specific biological markers. Such advancements could transform how we approach treatment and management of diseases, offering more precise and personalised therapeutic options. By bridging the gap between biology and traditional computing systems, the researchers pave the way for intelligent systems that could one day integrate seamlessly into biological environments. This research is not just about creating new #technology; it is about redefining how we interact with our bodies and surroundings through #science. Consider how this might impact our daily lives: more efficient healthcare services, early disease detection, and on-demand therapeutic interventions. The potential benefits of integrating bio-inspired logic gates in medicine and beyond are both exciting and promising. #Chemistry #AnalyticalChemistry #BioMemory #Innovation #Healthcare #FutureTech, If you want to know more about #analyticalchemistry news, follow me: https://lnkd.in/d29pbjb9
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🧬 Exciting news! Matthew here from Biomatter, and I'm thrilled to be presenting at SynBioBeta 2024 on May 8th from 11:00 to 11:45 am during the Lightning Talks session. 🔎 Join me for “Reimagining Enzymes for Synthetic Biology with AI-Driven Intelligent Architecture™,” where we’ll dive into the transformative potential of artificial intelligence in revolutionizing enzyme design and function. Discover how Intelligent Architecture™ platform empowers researchers to tackle the most challenging problems in synthetic biology by innovating beyond nature’s limitations. 🌐 This talk will cover the latest advancements in AI and physics-driven enzyme engineering that is not just enhancing, but truly transforming, the way we approach enzyme design for various applications in biotech. We’ll explore how Biomatter is revolutionizing protein engineering, entering an era where researchers are not constrained by nature’s initial designs but can create bespoke, fit-for-purpose enzymes. 👨🔬 Whether you're a scientist, a student, or simply fascinated by the intersection of technology and biology, this session promises to offer insightful perspectives on the future of biotechnological innovation. 🚀 Don’t miss out on this opportunity to see how AI-driven technologies are setting new standards in the field of synthetic biology! #SynBioBeta2024 #SyntheticBiology #AIInnovation #Biotech
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𝗕𝗶𝗼𝗩𝗶𝘀𝗶𝗼𝗻𝗖𝗲𝗻𝘁𝗲𝗿 𝗮𝗻𝗱 𝗲𝗫𝗮𝗰𝘁 𝗹𝗮𝗯 𝘁𝗼 𝗮𝗱𝘃𝗮𝗻𝗰𝗲 𝗯𝗶𝗼𝗶𝗺𝗮𝗴𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀! eXact lab received a 5 years agreement from the BioVisionCenter to develop 𝗙𝗿𝗮𝗰𝘁𝗮𝗹! 🚀 This unique collaboration combines our strengths in advanced computational techniques and biological research, creating a solution designed to transform how researchers and clinicians visualize and analyze complex biological data. 🌱🔬 Fractal is a framework to process bioimaging data at scale, enabling distributed workflows that handle TBs of image data and applies processing tasks to them. More info on comments! The BioVisionCenter is the first fully computational academic center dedicated to bioimage analysis R&D in Switzerland, aiming to provide biologists state-of-the-art machine vision methods to analyze bioimage datasets 🧬 🔍 and support imaging scientists in turning their novel algorithms into broadly usable tools. #bioimageanalysis #computationalbiology #lifesciences #bioinformatics #machinevision #biotechinnovation #biomedicalresearch #advancedimaging #innovation #healthcare
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🔍 #DATAKnowledge – What Makes Data FAIR? Have you heard about the FAIR principles—Findability, Accessibility, Interoperability, and Reusability? These principles maximize the value of scientific data by ensuring it is structured, accessible, and reusable by both humans and machines. Standardized data management leads to better collaboration, innovation, and long-term impact. 🔗 Learn more about FAIR principles here: https://lnkd.in/d-a_QAk In HEREDITARY, we work to enhance the long-term usability of human-derived datasets (microbiome, imaging, clinical records) by applying FAIR principles. 💡 How do you think FAIR data can benefit projects like ours? Share your thoughts in the comments! 📖 Read more in our latest post: https://lnkd.in/dTRAE7cs University of Colorado | Aalborg Universitet | Università degli Studi di Padova | Radboudumc | Ontotext | HES-SO Valais-Wallis | Universidade Nova de Lisboa | European Bioinformatics Institute | EMBL-EBI | Technische Universität Graz | SURF | Università degli Studi di Torino | Centre for Genomic Regulation (CRG) | Centro Nacional de Análisis Genómico (CNAG) | FEUGA. Fundación Empresa Universidad Gallega | European Brain Council | Observa Science in Society | European Genome-phenome Archive (EGA) | EUpALS | KU Leuven
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🚀 𝐍𝐞𝐰 𝐩𝐚𝐩𝐞𝐫 𝐨𝐧 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐛𝐥𝐞 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠𝐬! 🚀 I'm thrilled to share our latest publication from my last year at the Robert Koch Institute: "𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐛𝐥𝐞 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠𝐬 𝐚𝐧𝐝 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐓𝐚𝐬𝐤𝐬". 🌟 In this paper, we introduce the innovative iCAN encoding method, a novel approach focusing on carbon atom neighborhoods to enhance structured input for ML models. Many thanks to my collaborators Aleksandar Anžel, Zewen Yang and Georges H.! 🔍 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: - In a large-scale biomedical peptide classification study, iCAN 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭𝐥𝐲 𝐨𝐮𝐭𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐞𝐝 𝐢𝐭𝐬 𝐩𝐫𝐞𝐝𝐞𝐜𝐞𝐬𝐬𝐨𝐫 across almost all datasets, achieving performance comparable to 𝐬𝐭𝐚𝐭𝐞-𝐨𝐟-𝐭𝐡𝐞-𝐚𝐫𝐭 encodings. - Versatility that 𝐞𝐱𝐭𝐞𝐧𝐝𝐬 𝐭𝐨 𝐚𝐥𝐥 𝐨𝐫𝐠𝐚𝐧𝐢𝐜 𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐞𝐬, from exotic amino acids to larger proteins. 🌐 - iCAN provides 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐛𝐥𝐞 𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐫𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬, allowing for the comparison of molecular neighborhoods, pattern identification, and the creation of relevance heat maps. 🔥 - Moreover, there's 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐭𝐨 𝐛𝐨𝐨𝐬𝐭 𝐢𝐂𝐀𝐍'𝐬 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐞𝐯𝐞𝐧 𝐟𝐮𝐫𝐭𝐡𝐞𝐫 by integrating more advanced machine learning models which can use the encoded spatial information. Dive into the details and potential for interpretability in Molecular Encoding down below! 📑 𝐏𝐚𝐩𝐞𝐫: https://lnkd.in/gkXVAZwp 💻 𝐂𝐨𝐝𝐞: https://lnkd.in/gaXVFMtY #MachineLearning #Interpretability #XAI #Bioinformatics #MolecularEncodings #Research #Innovation #Science
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