Synthetic Biology Innovations

Brittle materials are often associated with weakness. Nature suggests a different perspective. Many insect wings are assemblies of flexible membranes connected through compliant, interlocking joints. These interfaces allow large deformations, distribute stresses, and prevent catastrophic failure. Perhaps, when engineering brittle materials, the challenge is not only making stronger materials, but designing smarter interfaces. This is where computational design can open a new outlook: not copying nature, but translating biological principles into manufacturable architectures. #ComputationalDesign #Biomimetics #MaterialsScience #AdvancedMaterials #Ceramics #AdditiveManufacturing #TopologyOptimization #EngineeringDesign #NatureInspiredEngineering #ExtremeEnvironments

Biology is inherently multi-scale: understanding life requires models that can seamlessly reason across molecules, genes, cells, entire genomes, and clinical knowledge. Traditional deep learning models often struggle with these diverse scales - much like how LLMs struggle with very long contexts without specialized compression techniques. Excitingly, we’re witnessing the rise of powerful multi-scale models (note: multi-scale doesn’t necessarily mean multi-modal!). Some examples: 1. STATE (Arc Institute) uses learned single-cell embeddings to realistically simulate cellular responses to treatments at the population level. 2. AlphaGenome (Google DeepMind) predicts genomic structures at multiple resolutions, from base-pair level to chromatin contact maps. 3. Evo2 (Arc Institute) generalizes across diverse genomic domains, learning DNA representations applicable across multiple branches of life. 4. ModelGenerator (GenBio AI) enables integration of different biological modalities, unifying the gradient updates across those modalities. 5. Boltz 2 (Massachusetts Institute of Technology) democratizes biomolecular interaction modeling by efficiently predicting both protein structure and small molecule binding affinity. 6. Cell2Sentence (Google and Yale University) bridges single-cell biology with natural language, leveraging the vast clinical and biomedical knowledge encoded in text to enhance biological understanding and reasoning. and many more. Just as a picture is worth a thousand words, incorporating features or signals that naturally capture diverse biological scales will significantly enhance model understanding and reasoning. References: 1. https://lnkd.in/gHkBQ96K 2. https://lnkd.in/gtZithTf 3. https://lnkd.in/gU4rAqzC 4. https://lnkd.in/gvNvbAJB 5. https://lnkd.in/gQzsm3YY 6. https://lnkd.in/gHS_G4YS

In new research we show how matter can be both process and archive - a living record of forces, environments, and functions. This blurs boundaries between hardware and cognition where infrastructure, implants, and devices "think" and evolve through their own changing manifestation across all scales, from atoms to ecosystems and beyond, a form of "scalogenesis". Check out this new paper in MRS Bulletin "Frontiers of Biological Material Intelligence" (link below), led by my student Lee Marom. A key thesis behind this work is that for too long we treated "matter" and "mind", hardware and theory, or science and art as separate, when what we really needed was a set of constructional principles, shared rules of structure, interaction, and evolution that connect them into one continuous fabric! We explore how the convergence of deep biological insight, computational modeling & advanced fabrication is driving a shift from static synthetic materials to systems capable of sensing, adapting & self-optimizing. Key insights: 1️⃣ Definition of material intelligence: We argue that intelligence is not limited to cognitive systems but can be embedded within a material's physical structure, across all scales (from electrons to the world). Unlike traditional "smart" materials that rely on external sensors or control, intelligent materials possess "agency" - the capacity to initiate context-sensitive action through intrinsic chemical and structural properties. 2️⃣ Three Core Biological Principles: We identify three mechanisms nature uses to achieve this intelligence: 1: Sensing and Responding: Illustrated by sea cucumbers that reversibly alter their stiffness for defense. 2: Self-optimization: Seen across scales (for example in bone, trees or cellular remodeling), where structure is continuously refined based on mechanical stress. 3: Memory encoding: Demonstrated by tree rings and mollusk shells that physically archive environmental history, but extending to evolution of DNA and proteins as populations and ecosystems adapt and realize never-before-seen functions. 3️⃣ Formalizing Nature: To translate these biological behaviors into engineering, we highlight the need for computational tools like Category Theory & graph-based reasoning systems (neural networks extract features; and symbolic logic reason over them for abstraction and explanation). These frameworks allow us to abstract the complex, hierarchical logic of biological systems and predict emergent behaviors. We also explore the future of fabrication to incorporate 4D printing and biofabrication are essential for physically realizing these designs. Altogether we envision a future where materials function as "semi-autonomous experimenters" capable of learning from their environment and evolving their properties in a continuous loop (independent of human intervention). Congrats to Lee on an amazing paper and excited to hear the feedback from the community! Materials Research Society #MRSFall2025

This tiny robot is offering scale, sustainability, and simplicity in a space that needs all three. We lost 6.7 million hectares of tropical primary forests in 2024 alone, as per a 2025 report by the University of Maryland’s GLAD lab. This is the largest annual loss on record in at least two decades, highlighting the urgent need for innovations that are simple, scalable, and cost-effective. An interesting innovation that caught my eye recently is the Erodium Copy robot by Morphing Matter Lab. It’s inspired by how the Erodium plant naturally buries its seeds. This robot copies that same behavior. It’s designed to operate with minimal human intervention. You simply place it on the ground or drop it by drone, and it drills itself into the soil, burying the attached seed at a depth optimized for survival. What caught my attention were two key aspects … 1. It works really well, even at scale. In tests, it had a 90% success rate when dropped by drones. It even supports helpful organisms like fungi and tiny soil creatures that improve the seed’s chances of growing. 2. It’s focused. It doesn’t try to do everything. It does one thing (plant seeds) and does it really well. Its 3-leg design keeps it stable, precise, and environmentally friendly. In my view, it’s a smart example of frugal, systems-aware innovation where form, function, and environmental context converge. It may not be the only answer. But it represents the kind of thinking we need more of in climate tech - focused, field-tested, and scalable. What do you think of this innovation? #Innovation #ClimateTech #Sustainability

The Ball-and-Socket Challenge 🤖 Why do humanoid robots still move like... robots? One major reason: we haven't cracked the ball-and-socket joint. Human shoulders and hips are engineering marvels that provide 3-degree-of-freedom motion in incredibly compact packages. Replicating these would unlock human-like arm manipulation and true bipedal walking. The Challenge: - 3 independent actuators in minimal space - Handle massive loads without backlash - Precise coordination across all axes Current Approaches: 🔧 Spherical Gears - Soccer ball with gear teeth controlled by 3 motors. Precise but complex manufacturing. 🚀 NASA Ultrasonic - Piezoelectric waves drive the joint at kilohertz frequencies. Ultra-compact but requires sophisticated control. 💨 Variable Stiffness - 3D-printed joints that switch from flexible to rigid via air pressure. Great for medical robots. 💪 Artificial Muscles - Heated polymer fibers contract like real muscle. Bio-inspired but slow response times. The Reality: No clear winner yet. Each trades off precision vs simplicity, power vs size, speed vs bio-mimicry. The race to solve ball-and-socket joints could be THE breakthrough that makes humanoids truly human-like in their movement. When will we crack this engineering puzzle? 🤔

Skills Bioinformatics Needs to be Future Proof Everyone’s talking about AI transforming bioinformatics. But while you are learning the latest ML frameworks, two quieter trends are fundamentally reshaping what it means to be a computational biologist. Data Engineering is Bleeding Into Biology The bioinformatics field is adopting enterprise data practices. Data lineage tracking, automated testing, CI/CD pipelines are no longer just buzzwords anymore, they are becoming core competencies. Modern job postings routinely ask for containerisation, workflow orchestration, and cloud-native thinking alongside traditional genomics skills. Why? Because as datasets scale and analyses become more complex, the infrastructure matters as much as the algorithms. Teams that master data engineering principles are building sustainable competitive advantages while others debug pipeline failures. Reproducible Analytics Isn’t Optional Anymore The five pillars of computational reproducibility (literate programming, version control, environment control, persistent data sharing, and documentation) are evolving from best practices to basic requirements. This is all about operational efficiency. When your analysis can be reliably reproduced six months later, you are not rebuilding from scratch you are building incrementally. The Skills Convergence So my conviction is that the most valuable bioinformaticians in the coming years won’t just understand biology and statistics. They will think like data engineers who happen to specialize in genomics. They will build systems that are reproducible-by-design, scalable-by-default, and maintainable-by-others. What This Means for Your Career While everyone rushes toward AI specialization, consider investing time in foundations such as: - Workflow managers (Nextflow, Snakemake) - Containerization and environment management - Data versioning and lineage tracking - Infrastructure-as-code thinking - Collaborative development practices Industry is well along this transition where we can see the teams best positioned for AI integration are those with solid data engineering foundations. You can’t build reliable AI-powered analyses on unreliable infrastructure. The future belongs to bioinformaticians who combine biological insight with engineering discipline. This builds a strong foundation to leverage emerging AI tools, rather than just following the latest AI trends. #Bioinformatics #DataEngineering #ReproducibleResearch #ComputationalBiology #CareerDevelopment #BioinformaticsSkills

Cells don’t just exist — they talk, signal, and collaborate. Thrilled to share that our paper “GraphComm: A graph-based deep learning method for predicting cell–cell communication from single-cell RNA sequencing data” is now published in Nature Scientific Reports! 🎉 Understanding how cells “talk” to each other—how ligands and receptors connect across cell types—is key to unlocking insights in tissue organization, disease progression, and therapeutic targeting. GraphComm introduces a new graph-based deep learning framework that learns these intricate cellular conversations by integrating: --Single-cell RNA-seq data with OmniPath’s 30,000+ validated protein–protein and ligand–receptor interactions --Graph Attention Networks (GATs) to model both intracellular and intercellular communication --Context-aware predictions across diverse datasets — from embryonic mouse brain and lung cancer perturbations to spatial transcriptomics in human hearts Our results show that GraphComm can: --Recover validated ligand–receptor interactions with high biological relevance --Capture how drug perturbations reshape communication networks --Detect spatially adjacent cellular interactions linked to fibrosis and ischemia --Perform on par with or better than existing CCC tools across multiple benchmarks This work bridges graph learning and cell biology to uncover the “social networks” of cells—an important step toward computationally modeling multicellular systems. Huge congratulations to first author Emily So, and thanks to Sikander Hayat, Sisira Kadambat Nair, Benjamin Haibe-Kains, and everyone at the University Health Network Vector Institute, and University of Toronto who contributed to this collaboration. 📄 Read the open-access article here: https://lnkd.in/ghd57Jh7 💻 Code & reproducible notebooks: https://lnkd.in/gE6RxN_M

Nature Biomedical Engineering paper (4 Aug 26) names its new AI biologist after Xunzi (荀子, c. 310-235 BCE), the Confucian philosopher renowned for insisting that wisdom arises not from innate intuition but from rigorous reasoning applied to accumulated empirical knowledge. Just as Xunzi argued that broad learning synthesized through structured reasoning produces understanding no single mind can reach alone, the AI named in his honor integrates 24.4 million biomedical publications with 613.6 TB of multisource data to generate disease-modifying hypotheses with testable mechanisms across 21,008 human genes and 5,850 diseases. 🔅 XunZi comprises two modules: XunZi-R, a 7.3B-parameter LLM built on Mistral 7B pretrained on those publications and 336,108 chain-of-thought mechanistic interpretations, and XunZi-M, a graph convolutional network integrating 2.8 million protein-protein interactions, 47,922 Gene Ontology terms, and 613.2 TB of multi-omics data. Existing frontier LLMs including GPT-4o, GPT-5, o3, and the Claude Sonnet series reason over literature but hallucinate and cannot assimilate empirical omics data; multimodal foundation models fuse data but lack interpretable mechanistic reasoning. XunZi resolves both simultaneously and, at 7.3B versus up to 1,750B parameters in competing systems, does so at a fraction of their computational cost while outperforming all on gene-disease classification. 🔅 XunZi achieved AUC of 0.86 for non-small-cell lung cancer and 0.92 for Parkinson's disease kinase identification. In NSCLC, 20 candidate genes were screened; five were confirmed, including MYO1B, a PI3K/AKT and MAPK/ERK regulator not previously linked to lung cancer and not identified by GPT-4o. For Parkinson's disease, the top 20 ranked kinases were tested in MPP+-treated neurons; three showed protective effects upon knockdown: CHK2, IRAK4, and STK33. CHK2 and IRAK4 showed aberrantly elevated phosphorylation in the substantia nigra of both MPTP and alpha-synuclein preformed fibril mouse models. AAV-mediated CHK2 knockdown preserved dopaminergic neurons and attenuated motor deficits. The CHK2 inhibitor CCT241533 rescued both models and additionally suppressed LRRK2 activation, an upstream link XunZi predicted without precedent in any prior publication or AI system. 👉 Limitation: Uncharacterized gene-disease pairs are treated as negatives during training, creating unavoidable label uncertainty. Validation is limited to NSCLC and PD; performance in rare or data-sparse diseases may be unreliable. The model does not incorporate single-cell temporal data, protein structures, or clinical records. Pharmacological rescue in vivo is reported only for CHK2; IRAK4 and STK33 await animal model validation. 📑 Not open access: https://lnkd.in/gv6WVYit 📑 Source code: https://lnkd.in/guG8Awvz #ParkinsonDisease #AIBiologist #DrugDiscover

Not every shiny ML algorithm belongs in Bioinformatics. Bioinformatics doesn’t just need AI. It needs Bio-aware AI. In the rush to apply the latest AI/ML models to every problem, there’s a reality check many overlook: 👉 Bioinformatics ≠ generic tabular data. 👉 Bioinformatics ≠ simple image recognition. 👉 Bioinformatics ≠ “just another dataset.” Genomics, proteomics, structural biology, and systems biology produce data with unique statistical distributions, noise profiles, and biological constraints. - Sequence data isn’t like stock market data. - Protein structures don’t behave like social network graphs. - Gene expression matrices are not regular spreadsheets. This is why some ML models that dominate in other fields (finance, NLP, recommender systems) break down in bioinformatics unless carefully adapted. In Bioinformatics, success comes when: Algorithms are tuned for biological priors. Models respect the physics & chemistry of life. Data preprocessing mirrors the complexity of biology, not just math. The best ML algorithm is not the “newest” one, it’s the one that truly understands biological data. Here are the top ML/LLM models in 2025: - AlphaGenome (June 2025): Gene regulation & variant impact from long DNA sequences - AlphaFold 3 (Launched 2024; widely adopted by 2025): Protein complex, ligand, DNA/RNA structure prediction - SonicParanoid2 (2024): Fast orthologous gene inference using ML & LMs - NuFold (2025): RNA 3D prediction using AlphaFold 2 architecture - trRosettaRNA (Recent): Transformer-based RNA tertiary structure modeling - esmGFP / ESM3-derived protein design (Published Jan 2025): AI-designed protein simulating evolutionary processes - Generative AI Models: DNABERT, DNAGPT, GENA LM: DNA sequence modeling and classification with LLMs - EMitool (2025): Explainable multi-omics integration for cancer subtyping - DeepGO-SE and TAWFN (2025): Enhanced protein function inference via embeddings and GNNs - Graph Neural Networks (GNNs) (Growing relevance by 2025): Modeling biological networks and spatial gene expression - Quantum-Inspired Algorithms: QSVM, QNN, VQE, QFT: Experimental bioinformatics acceleration via quantum algorithms - BioMaster (2025): Automated bioinformatics pipeline management with LLM agents Models like AlphaGenome or DeepGO-SE are purpose built for biology they understand sequence context, structure, or biological ontologies. AlphaGenome handles million-base pair sequences; ESM3 was trained on hundreds of billions of protein. NuFold, AlphaFold 3, and trRosettaRNA capture 3D structure; GNNs model networks and tissue spatial contexts. Tools like EMitool and BioMaster support interpretability and autonomous workflows. Quantum-inspired algorithms and LLM agents (e.g., BioMaster) point toward the next wave of bioinformatics automation and acceleration.

I still remember 2015 in the lab. Three months. One recombinant protein. Dozens of different host systems, vectors, strains, temperatures - everything you could possibly tweak. Most attempts? Only inclusion bodies. Or worse: NO EXPRESSION. If we hit a 10 % success rate, we celebrated like it was a publication. Fast-forward to 2025: The biggest shift in protein production since recombinant DNA technology isn’t new expression systems or bioreactors. 👉 It’s AI finally understanding what we couldn’t. When AlphaFold2 (2020) arrived, it didn’t just predict structures, it changed how we think about folding, stability, and function. And what came next has transformed expression strategy and design more than any textbook update ever did. Here are a few AI tools that changed the game for me: 🧬 SignalP 6.0: Helps you deciding between periplasmic, secretory, or eukaryotic targeting. 🧫 DeepTMHMM:  Predicts α-helical and β-barrel transmembrane topologies. 🧩 ProteinMPNN:  Designs sequences from backbone structures in seconds. 💫 Rfdiffusion:  Generates new protein backbones nature never imagined. ⚡ ESMFold:  60 × faster than AlphaFold 2 and ideal for high-throughput screening. 🧠 AlphaFold 3: Predicts protein-ligand and complex assemblies. 🔡 CodonTransformer:  AI-driven codon optimization considering tRNA abundance, mRNA folding, and ribosome kinetics. AI is no longer just a “support tool.” It’s rewriting the way we approach protein design and expression. 💬 Which AI tools have changed your workflow? 👇 Drop your favorites below. I’d love to compare notes. If you like insights that blend bench-reality with AI-powered innovation, follow me (Reinhold Horlacher) for more biotech deep dives. #ProteinEngineering #AlphaFold #AIinBiology #ComputationalBiology #ProteinProduction #ExpressionScreening #SyntheticBiology #trenzyme