Advanced Biotech Research Techniques

Big breakthrough: A few months my lab at MIT introduced SPARKS, our autonomous scientific discovery model. Since then we have demonstrated applicability to broad problem spaces across domains from proteins, bio-inspired materials to inorganic materials. SPARKS learns by doing, thinks by critiquing itself & creates knowledge through recursive interaction; not just with data, but with the physical & logical consequences of its own ideas. It closes the entire scientific loop - hypothesis generation, data retrieval, coding, simulation, critique, refinement, & detailed manuscript drafting - without prompts, manual tuning, or human oversight. SPARKS is fundamentally different from frontier models. While models like o3-pro and o3 deep research can produce summaries, they stop short of full discovery. SPARKS conducts the entire scientific process autonomously, generating & validating falsifiable hypotheses, interpreting results & refining its approach until a reproducible, fully validated evidence-based discovery emerges. This is the first time we've seen AI discover new science. SPARKS is orders of magnitude more capable than frontier models & even when comparing just the writing, SPARKS still outperforms: in our benchmark evaluation, it scored 1.6× higher than o3-pro and over 2.5× higher than o3 deep research - not because it writes more, but because it writes with purpose, grounded in original, validated compositional reasoning from start to finish. We benchmarked SPARKS on several case studies, where it uncovered two previously unknown protein design rules: 1⃣ Length-dependent mechanical crossover β-sheet-rich peptides outperform α-helices—but only once chains exceed ~80 amino acids. Below that, helices dominate. No prior systematic study had exposed this crossover, leaving protein designers without a quantitative rule for sizing sheet-rich materials. This discovery resolves a long-standing ambiguity in molecular design and provides a principle to guide the structural tuning of biomaterials and protein-based nanodevices based on mechanical strength. 2⃣ A stability “frustration zone” At intermediate lengths (~50- 70 residues) with balanced α/β content, peptide stability becomes highly variable. Sparks mapped this volatile region and explained its cause: competing folding nuclei and exposed edge strands that destabilize structure. This insight pinpoints a failure regime in protein design where instability arises not from randomness, but from well-defined physical constraints, giving designers new levers to avoid brittle configurations or engineer around them. This gives engineers and biologists a roadmap for avoiding stability traps in de novo design - especially when exploring hybrid motifs. Stay tuned for more updates & examples, papers and more details.

Nine in ten drug programs that enter the clinic fail, most often when we first test for efficacy - when the mechanism did not drive the disease. The best-validated fix has been known for a decade: targets with human genetic support are 2 to 4 times more likely to succeed in the clinic. Yet only 3.6% of genetically supported targets have ever been pursued for an indication the genetics supports. The genetic window is foggy. A disease map yields dozens or hundreds of associated genes, most with muted effects, because evolution selects against variants of massive impact. Part 2 of my Deep Phenotype manifesto, out today, describes the AI we built at insitro to cut through that fog. Specialized ML models turn high-content measurements, from whole-body imaging in large human cohorts to genome-scale perturbation screens in human cells, into a library of precision phenotypes: quantitative traits that sharply raise the power of human genetics. In MASH, they surfaced over 30x more genetic associations than clinical staging. On top of this library sits a causal AI model, our Virtual Human, that integrates evidence across data modalities, disease biologies, and physical scales. The premise is that this integration gives rise to higher conviction, which in turn we believe will give rise to higher clinical success rates. To assess that hypothesis,we asked the model to predict success of historical phase 2 trials, zero-shot, with no trial outcomes in training. In its top decile of target-indication pairs, the false-positive rate was 10% in metabolic disease and 21% in cardiac, against a historical failure rate of 57% in both. We recognize, of course, that a retrospective benchmark is no guarantee of prospective success. Conviction is only half the value. Because the same phenotypes span from patient to cell, a genetic hit becomes an experiment we can run: the platform tells us in which cells a gene acts, which pathways it perturbs, whether a drug should inhibit or activate it. The assays that credential a target become the assays we optimize molecules against; the biomarkers that found the mechanism follow the drug into the clinic. The piece traces one example end to end: MASH, from the UK Biobank to a validated liver program. If this works, the payoff arrives twice. The first is economic: every medicine that reaches a patient carries the cost of the failures behind it, and nothing lowers that burden more than mechanisms that survive phase 2. The larger prize is measured in patients: the many diseases that still lack any disease-modifying therapy because we have not known which mechanisms drive them. If we can generate causally credentialed targets repeatably, disease after disease, the question changes: from whether the next program will fare better to how many diseases we can take on at once. That is the wager behind the Virtual Human: a repeatable path to medicines for the many who have none today. https://lnkd.in/gxFt-Fst

🚨 I'm excited to share our latest review, “New approach methodologies for drug discovery,” published in Cell by Cell Press and selected as a Featured Article. For decades, drug discovery has relied heavily on animal models. Yet, with persistently high clinical failure rates, a fundamental question remains: How predictive are animal models of human biology, and are there better alternatives? In this review, we highlight a paradigm shift toward human-centric New Approach Methodologies (NAMs), driven by rapid advances in both policy and technology. On the regulatory front, we discuss major transitions led by agencies such as the FDA (FDA Modernization Acts 1.0 → 2.0 → 3.0) and The National Institutes of Health (stem cell guidelines and the establishment of national organoid initiatives). 🔬 On the technology side, we frame NAMs evolution across three domains: ·        “New” - foundational 2D stem cell–based systems ·        “Newer” - advanced 3D organoid-based models ·        “Newest” - future-facing in silico and AI-driven platforms Across these domains, we highlight emerging therapeutic candidates, cutting-edge models, and translational and clinical applications. We also examine key biological, technical, and regulatory bottlenecks that need to be addressed to enable robust translational adoption, and discuss ongoing clinical efforts and societal considerations for responsible implementation. 🌍 Looking forward. If the past 30 years of drug discovery were shaped by animal models, the next 30 years, animal models will likely transition from a central to a supporting role, following the 3Rs principle: refinement, reduction, and ultimately replacement. Instead, the field will likely be defined by human-centric NAMs, powered by multiscale platforms, multi-omics data, and AI-enabled pipelines. This transformation is not only scientific, but also societal, aligning drug development more closely with human biology while reducing cost, inefficiency, and ethical burden. 👏 Congratulations to an outstanding team: Wenqiang (Eric) Liu, Paul Pang, Catherine Wu and Danilo Tagle from Stanford University School of Medicine, Stanford Cardiovascular Institute, Stanford Department of Medicine, Greenstone Biosciences, National Center for Advancing Translational Sciences (NCATS), The National Institutes of Health. 📄 Please check the full paper here: https://lnkd.in/estaR2cq #NAMs #DrugDiscovery #StemCell #Organoids #AI #PrecisionMedicine #TranslationalScience

Real-time capture of stomatal dynamics 🔬🌿 🔎 Stomatal conductance serves as the critical physiological bottleneck that defines the trade-off between photosynthetic carbon assimilation and transpiration water loss. 🌱 High-throughput phenotyping has been limited by a technological dichotomy: the inability to simultaneously observe guard cell morphology and measure gas exchange kinetics in real-time. 🔬 A novel ”Stomata In-Sight" platform, developed by the researchers from the University of Illinois Urbana-Champaign, resolves this by integrating live, non-destructive confocal microscopy directly with high-precision leaf gas exchange sensors. 🔃 This convergence allows researchers to correlate 3D guard cell turgor dynamics with instantaneous and fluxes under strictly controlled environmental parameters. 💧 By manipulating variables such as vapor pressure deficit (VPD) and light intensity, the system reveals how stomatal density and aperture size functionally drive water use efficiency (WUE). 🌾 A better evaluation of stomatal kinetics provides breeders with the specific phenotypic data needed to select for genetic traits that optimize crop performance under drought stress. Video: time-lapse movie of maize stomata movement (Crawford et al.2025;DOI:10.1093/plphys/kiaf600). #microcsopy #science

If I had to give one tip to biotech startups, it would be to use Design of Experiments (DOE). It helps you save time and get more reliable results. I first heard about DOE during my Master’s in Industrial Biotechnology. It was introduced as a way to speed up experimental design. At the time, I was still convinced that optimizing a process meant changing one variable at a time. Temperature, then pH, then nutrients. I had the chance to apply DOE in my first job. That’s when I saw the real difference. The sequential approach was slow, often misleading, and blind to how variables actually interact. With DOE, I could: -Test multiple factors at once -Detect hidden interactions -Build predictive models without running every single experiment. That changes everything, especially in fermentation, where parameters are tightly interconnected. I’ll give you a concrete example. A team was optimizing enzyme production using 3 variables: temperature, nutrient concentration, and agitation speed. Sequential method: 27 experiments.DOE method: 9 well-designed tests. Not only did they save time, but they also discovered a key insight: agitation speed strongly influenced nutrient availability. That single piece of information drove faster, smarter decisions. Obviously, when I founded Cultiply, I made sure DOE would be part of our DNA. It allows us (and our clients) to reduce uncertainty and make solid technical choices from the start.

Below is a selection of widely used books in computational structural biology, covering both foundational introductions and advanced topics that bridge physics, simulation, and statistical modeling of biomolecular systems. It is by no means an exhaustive or complete list, but it provides a solid conceptual and practical foundation for anyone entering or advancing in structural bioinformatics. - Introduction to Protein Structure (Carl Ivar Branden, John Tooze) - A classic introduction explaining how proteins fold, twist, and function - Molecular Modelling: Principles and Applications (Andrew Leach) - A clear explanation of the theory and practice behind molecular modeling and simulation - Structural Bioinformatics (Philip Bourne, Helge Weissig) - An overview of computational methods for studying biological structures, from databases to modeling - Computational Structural Biology: Methods and Applications (Torsten Schwede, Manuel Peitsch) - A collection of computational techniques and real-world examples in structural biology - Molecular Modelling and Simulation: An Interdisciplinary Guide (Tamar Schlick) - An in-depth guide to the physics and algorithms underlying biomolecular simulations. - Protein Structure Prediction: Methods and Protocols (David Webster) - A hands-on manual covering modern computational approaches to protein modeling - Biophysical Chemistry (Charles Cantor, Paul Schimmel) - A foundational text on the physical forces governing biomolecular structure and function - Understanding Molecular Simulation: From Algorithms to Applications (Daan Frenkel, Berend Smit) - A rigorous introduction to molecular simulation methods, algorithms, and their practical use - Statistical Physics of Biomolecules: An Introduction (Daniel Zuckerman) - A concise and intuitive treatment of statistical mechanics applied to biomolecular systems - The Art of Molecular Dynamics Simulation (Dennis Rapaport) - A comprehensive guide to molecular dynamics algorithms and their physical foundations - Protein Actions: Principles and Modeling (Ivet Bahar, Robert Jernigan, Ken Dill) - A modern synthesis linking statistical mechanics and protein dynamics - Computational Protein Design (Ilan Samish) - Focuses on techniques for designing proteins which is a key intermediate step between structure prediction and structural engineering Though the field is evolving at an enormous speed and new tools (especially AI-based ones) appear every week, it is worth building a strong understanding of the principles behind the field and its techniques, upon which all novel tools and approaches can later be added.

🧵 You’re not behind in bioinformatics. The field just evolves faster than anyone can follow. 1/ It’s not you. Bioinformatics evolves too fast. And not just from new algorithms— But from how fast technology itself changes. 2/ In 2012, I started learning bioinformatics. Back then, ChIP-seq was the new thing. It was replacing ChIP-on-chip. The field moved. 3/ Then came ChIP-exo. Then ATAC-seq. Then CUT&RUN. Then CUT&Tag. Each assay needs a slightly different way to analyze. No single pipeline fits all. 4/ Bulk RNA-seq was king. We ran DESeq and edgeR like gospel. Now? Single-cell RNA-seq is everywhere. And spatial is coming for it all. 5/ Now we have spatial transcriptomics, spatial proteomics. Data isn’t just gene x sample anymore. It’s gene x cell x location x time. 6/ Short reads used to dominate. Illumina ruled the land. But now PacBio and Nanopore are catching up. Long-read is real. And it’s here. 7/ Want to assemble a genome? You’ll want long reads. But they can be noisy. So you pair them with short reads for polishing. It’s a dance. 8/ There’s no way to be an expert in every assay. ATAC-seq, CUT&RUN, Hi-C, WGS, WES, CITE-seq, spatial, single-nucleus, proteomics... Impossible. 9/ And the tools? They multiply like bacteria. New preprints weekly. New pipelines monthly. New file formats constantly. 10/ So how do you survive? You focus on what doesn’t change: Curiosity Core programming skills Statistical reasoning Biological sanity checks 11/ You learn the foundations. Not the latest wrapper. You understand how PCA, normalization, clustering work—not just which package runs them. 12/ You don’t need to know every assay. But you need to know how to ask: “What’s the structure of this data?” “What’s noise vs signal?” 13/ And most of all— You find people who’ve done it before. Senior bioinformaticians. Wet lab biologists. You learn from them. 14/ You watch them look at an IGV track and say, “That’s an artifact.” Or scan a QC report and know what to ignore. That’s gold. 15/ Key takeaways: You’re not falling behind The field moves fast, but foundations last Learn concepts, not just commands Ask. Watch. Learn. Bioinformatics isn’t static. It’s surfing waves of innovation. Wipeouts happen. But when you ride one right— It’s magic. I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics https://lnkd.in/erw83Svn

❓ ONT, Illumina & MGI – What’s the Difference? 🔬 Next-Generation Sequencing (NGS) allows scientists to read genetic code by sequencing millions (or billions) of DNA fragments in parallel. Let’s explore some key platforms: 1️⃣ Illumina 1) Sample & Library Preparation: DNA/RNA is purified, fragmented, and ligated with adapters containing cluster recognition sites (bind to specific spots on the flow cell), index sequences (identify the sample), and primer binding sites. NEBNext UltraExpress® FS DNA Library Prep Kit https://lnkd.in/dMjcZphg is widely used for high-quality library preparation. 2) Cluster Generation: The flow cell has oligonucleotides complementary to the adapters, allowing fragments to bind. A PCR-like process (bridge amplification) forms clusters. Multiple copies of the strand ensure that the fluorescent signal during sequencing will be strong enough. 3) Sequencing: Fluorescently labeled nucleotides (G,C,A,T) with terminators bind one at a time to all single strands in the cluster (at any given moment, only one type of nucleotide binds, emitting a specific color). A camera records fluorescence to identify nucleotides. Terminator groups are cleaved to allow the next cycle. 4) Reverse Strand Sequencing: Index sequences are read, the reverse strand is synthesized and sequencing is repeated. 5) Data Analysis: Low-quality reads are filtered, and sequences are aligned. 2️⃣ MGI 1) Sample & Library Preparation: DNA is fragmented, ligated with adapters, and circularized into ssCirDNA. NEBNext® FS DNA Library Prep Kit for MGI® https://lnkd.in/dQMtgbNd provides a reliable solution for generating high-complexity libraries with optimized workflow. 2) DNB Generation by Rolling Circle Amplification: ssCirDNA acts as a template for continuous amplification, forming dense DNA Nanoballs (DNBs) with multiple copies of the sequence. 3) Loading DNBs: DNBs bind to specific spots on the flow cell. 4) Sequencing: Fluorescently labeled nucleotides with terminators bind one at a time to all sequences in the DNBs simultaneously. A camera records fluorescence to identify each nucleotide. Terminators are cleaved to allow the next cycle. 3️⃣ Oxford Nanopore Technologies 1) Sample & Library Preparation: DNA/RNA is extracted, purified, and ligated with motor protein adapters. 2) Loading the Flow Cell: The library is added to a flow cell containing thousands of nanopores. 3) Sequencing: The motor protein unzips the DNA, guiding it through the nanopore one base at a time. Each nucleotide disrupts the ionic current in a unique way, producing a signal used to determine the sequence. 4) Base Calling & Data Analysis: Signals are converted into nucleotide sequences, followed by read alignment and error correction. #NGS #Sequencing #Genomics #Bioinformatics #Illumina #Nanopore #MGI #Biotech

🧠 Breaking: The Entire Mouse Brain Mapped 🐁 Researchers at Duke University have revolutionized MRI technology, achieving the sharpest images of a mouse brain with a powerful 9.4 Tesla magnet and advanced gradient coils. These high-resolution images reveal microscopic brain details, providing new insights into neurodegenerative diseases like Alzheimer's. This breakthrough enhances our understanding of brain connectivity and disease progression, with the potential to improve human brain research and treatments. By combining MRI with light-sheet microscopy, the team has created high-definition brain diagrams, paving the way for better insights into age-related brain changes and conditions. Key Points: 🔷 Duke University researchers achieved the sharpest MRI images of a mouse brain using a 9.4 Tesla magnet. 🔬 The resolution allows visualizing microscopic brain details (5-micron voxels), offering insights into brain connectivity and diseases. ⌛ The research, led by G. Allan Johnson, Ph.D., is the result of nearly 40 years of work and combines MRI with light-sheet microscopy. #MRI #Neurodegeneration #Alzheimers

💡 DNA Extraction 🧬 It is the process of isolating DNA from cells or tissues to study its structure, sequence, or function. This process involves breaking open cells, separating DNA from other cellular components, and purifying it for analysis. DNA extraction is more than just a lab technique; it's a process rooted in biochemistry and molecular biology principles. Steps of DNA Extraction: • Step 1: Cell Lysis (Breaking the Cells) Cells are surrounded by membranes made of lipids and proteins. Detergents or enzymes (like Proteinase K) disrupt the membranes, releasing DNA and other cell contents. • Step 2: Removal of Proteins and Contaminants Once the cell is lysed, the DNA is mixed with other molecules like proteins, RNA, and lipids. Chemicals like phenol, salts, or specialized buffers precipitate or degrade unwanted materials. • Step 3: DNA Purification The DNA is washed to remove residual contaminants. Purification steps often use alcohol washes or binding DNA to silica membranes. • Step 4: DNA Precipitation DNA is soluble in water but not in alcohol. Adding ethanol or isopropanol causes the DNA to clump together and become visible as a white precipitate. ü Methods Phenol-Chloroform Extraction: This classic method uses organic solvents to separate DNA from proteins and other cellular components. Silica Adsorption: This method utilizes silica to bind DNA, allowing for the removal of other cellular components. Magnetic Bead Extraction: This method uses magnetic beads to bind to DNA, allowing for easy separation from other cellular material. // Applications of DNA Extraction Forensic Science: Identifying individuals through DNA. Genetic Research: Analyzing genes and mutations. Medical Diagnostics: Detecting diseases and genetic disorders. #DNAExtraction #MolecularBiology #Labscience #GeneticsTheory #Biochemistry #Biotechnology