Bioengineering Simulation Platforms

BioEmu-1, developed by Microsoft Research, is a deep learning model that predicts dynamic structural ensembles of proteins, addressing the limitations of static models like AlphaFold and computationally intensive molecular dynamics (MD) simulations. Unlike traditional MD simultion, which struggles with scalability, BioEmu-1 combines data from AlphaFold, MD trajectories, and experimental stability metrics to generate thousands of conformations rapidly (10,000–100,000x faster) on a single GPU. It employs a diffusion-based generative approach to explore free-energy landscapes, revealing intermediate states and transient binding pockets critical for drug design. Validated against MD benchmarks, it accurately predicts folding free energies (R²=0.85) and allosteric pathways, aiding applications like kinase inhibitor development. Current limitations include handling novel folds and large multi-domain proteins, but future updates aim to integrate cryo-EM/NMR data and expand to RNA dynamics. Open-sourced to the community which is a great open source contribution to biology, BioEmu-1 accelerates research in drug discovery and protein engineering by bridging static structure analysis with dynamic functional insights. #ProteinDynamics #StructuralEnsembles #DeepLearning #AIInBiology #DrugDiscovery #Bioinformatics #MicrosoftResearch #OpenScience #MolecularDynamicsComparison #Allostery #ConformationalChanges #GenerativeAI #FreeEnergyLandscapes #CryoEM #KinaseInhibitors #ComputationalBiology #TherapeuticDesign

🟥 Strategies for Constructing Organ-Specific Organoid Chips The construction of organ-specific organoid chips (also known as organoid-on-a-chip systems) requires an integrated approach combining stem cell biology, tissue engineering, and microfluidic design. These platforms are designed to replicate the microenvironment, function, and spatial organization of human organs in vitro and are used for disease modeling, drug screening, and regenerative applications. The first key strategy is to use patient-derived organoids cultured from pluripotent stem cells or adult stem cells that are able to recapitulate the cellular diversity and tissue architecture of specific organs (e.g., liver, brain, intestine, kidney, or lung). These organoids are then embedded in biocompatible scaffolds or hydrogels to support their three-dimensional growth and maintain physiological functions. Second, microfluidic systems need to be incorporated to simulate dynamic physiological conditions, such as fluid shear stress, perfusion, and nutrient exchange. These chips often contain microchannels lined with endothelial cells to simulate blood flow and enable vascular-organoid interactions. Third, mechanical and biochemical manipulations need to be utilized to enhance organ-specific differentiation and maturation. This may involve stretching (for lung or intestinal models), pulsatile flow (for heart or vascular models), or chemical gradients to guide tissue patterning. Fourth, sensor integration is increasingly important for building organ-specific organoid chips, enabling real-time monitoring of key parameters such as pH, oxygen content, metabolic activity, and drug response. Finally, modular design strategies allow multiple organoid connections on a single chip, such as the gut-liver system or the brain-retina system, enabling inter-organ communication studies. In summary, organ-specific organoid chips are designed through a multidisciplinary strategy involving stem cell-derived organoids, biomaterials, microfluidic perfusion, physiological stimulation, and biosensing. These systems are rapidly evolving into powerful platforms for precision medicine, toxicology testing, and modeling of human biological functions. Reference [1] Shun Zhang et al., Lab Chip 2021 (doi: 10.1039/d0lc01186j) #OrganoidonChip #OrganoidEngineering #Microfluidics #TissueEngineering #PrecisionMedicine #StemCellTechnology #RegenerativeMedicine #LabonChip #DrugScreening #DiseaseModeling #BiotechInnovation #NextGenHealthcare #BiomedicalEngineering #OrganChip #PersonalizedMedicine #CSTEAMBiotech

What if you could harness the properties of living brain cells? Our latest article in Nature Reviews Bioengineering (Nature Portfolio) explores the CL1, a first-of-its-kind platform that allows real-time, closed-loop electrophysiological interactions with living neural networks making it possible to test, train, and explore the basis of biological intelligence. From drug screening and disease modelling to alternative computing and intelligent systems, the CL1 opens up an entirely new experimental spaces, allowing a bridge between biology and code. Built from the ground up the CL1 is available through either unit purchases or remote cloud access. We believe this CL-1 has the potential in many areas, from reducing reliance on animal testing to exploring the foundations of cognition and intelligence. Check out the exclusive full access link in the comments below. #Neurotech #SyntheticBiologicalIntelligence #Biocomputing #InVitroNeuroscience #CL1 #BrainOnAChip #AltComputing #CorticalLabs

BioEmu-1, or Biomolecular Emulator-1, is an **open-source** deep-learning model developed by Microsoft Research. It is designed to generate thousands of protein structures per hour, providing insights into the various conformations that proteins can adopt. COMPARISON WITH MOLECULAR DYNAMICS (MD) SIMULATIONS: 1️⃣ Faster and more efficient: BioEmu-1 can generate thousands of protein structures per hour on a single GPU 2️⃣ Leaner computational needs ➡️ Cost efficiency: BioEmu-1 uses about 0.001% of the GPU processing time required by standard MD simulations 3️⃣ Scalability: BioEmu-1's efficiency allows for large-scale studies of protein dynamics that were previously impractical due to resource constraints 4️⃣ Dynamic Insights: BioEmu-1 can simulate the dynamic behavior of proteins 5️⃣ Open-Source Accessibility: BioEmu-1 is available as an open-source tool APPLICATIONS IN RESEARCH ▪️ Drug Discovery: By simulating the dynamic behavior of proteins, BioEmu-1 helps researchers identify potential drug targets and design more effective drugs. ▪️ Protein Engineering: Researchers can use BioEmu-1 to design proteins with specific functions. This is particularly useful in developing enzymes for industrial processes or creating proteins with therapeutic properties ▪️ Disease Research: BioEmu-1 aids in studying diseases caused by protein misfolding or structural abnormalities such as Alzheimer's and Parkinson's ▪️ Structural Biology: The model provides a deeper understanding of protein structures and their functions, complementing experimental techniques like cryo-electron microscopy and X-ray crystallography ▪️ Biophysics: BioEmu-1 allows researchers to study the fundamental principles of protein dynamics and stability, contributing to our overall knowledge of molecular biology Learn more: https://lnkd.in/eeX4Cec7