Biological Systems Modeling

Validate your NAM (Wait what?) ! Everyone says it. Regulators say it. Reviewers say it. Conference speakers say it. But validate against what? That question has been unanswered for years. Until now. FDA CDER just released a draft guidance that finally spells out what a NAM must deliver to support regulatory decision-making. Not in theory. In practice. The framework has four pillars. Context of Use. Human Biological Relevance. Technical Characterization. Fit-for-Purpose. Context of Use comes first. Not the technology. Not the platform. The question. What drug development decision does your NAM support? Dose selection? Mechanistic understanding of an adverse event? Justification for dropping an animal species that adds no regulatory value? If your NAM does not answer a specific question, validation is meaningless. Then comes the part most people skip. Human biological relevance. FDA is explicit. Using human-derived cells does not automatically make your model human-relevant. You must demonstrate that the cell types, the architecture, and the functional endpoints recapitulate the physiology you claim to model. A liver-on-chip that does not express CYP450 proteins or release ALT is not a hepatotoxicity model. It is a cell culture device. Human-derived is a source. Human-relevant is a performance standard. Technical characterization follows. Predictive performance for the specific COU. Sensitivity. Specificity. Stability over time. Batch-to-batch reproducibility. Donor variability documented. For organ-on-chip platforms, FDA asks for evidence that the device itself does not interfere with the test article through leaching, absorption, or biocompatibility artifacts. This is not a checklist for grants. This is an engineering specification. Fit-for-purpose closes the loop. Three paths. Replace an animal study with equivalent or better safety data. Fill a data gap where no animal model exists. Confirm or complement traditional findings. Note what FDA did not say. They did not say NAMs must outperform animal studies across the board. They said NAMs must be reliable for a defined purpose. That is a fundamentally different standard. One more detail. A NAM does not need to be formally validated to be reviewed. A fit-for-purpose NAM, even without full validation, may adequately address a specific toxicological concern within a weight-of-evidence framework. For years, the NAM field operated under a vague mandate. Prove your method works. But no one defined what "works" means. FDA just defined it. Answer a specific question, with human-relevant biology, reproducible technical performance, and regulatory utility. The companies that understood Context of Use before this guidance will accelerate. The ones that built platforms first and looked for questions later will need to restructure. Science is not validated in the abstract. It is validated against a decision.

Molecular dynamics simulation of an entire cell By Jan A. Stevens, Fabian Grünewald, P. A. Marco van Tilburg, Melanie König, Benjamin R. Gilbert, Troy A. Brier, Zane R. Thornburg, Zaida Luthey-Schulten, Siewert J. Marrink Abstract The ultimate microscope, directed at a cell, would reveal the dynamics of all the cell’s components with atomic resolution. In contrast to their real-world counterparts, computational microscopes are currently on the brink of meeting this challenge. In this perspective, we show how an integrative approach can be employed to model an entire cell, the minimal cell, JCVI-syn3A, at full complexity. This step opens the way to interrogate the cell’s spatio-temporal evolution with molecular dynamics simulations, an approach that can be extended to other cell types in the near future. https://lnkd.in/gtnntTK6 doi: 10.3389/fchem.2023.1106495 The illustration is a snapshot of the complete Figure 2 "Whole-cell Martini model of JCVI-syn3A" from the paper and is made available under a CC BY 4.0 International license.

🧬 AI just wrote the code for living organisms and they actually work. Researchers at Arc Institute and Stanford have achieved something unprecedented: using genome language models Evo 1 and Evo 2 to generate 16 viable bacteriophage genomes from scratch the first time AI has designed complete, functional genomes that work in the real world. Think about that for a moment. Not just designing a protein. Not simulating a genome on a computer. Actually creating living viral systems with substantial evolutionary novelty that infect bacteria and replicate successfully. Here's what makes this revolutionary: In 1977, ΦX174 was the first genome ever sequenced. In 2003, it was the first genome chemically synthesized. Now in 2025, it's the template for the first AI-generated genomes. We've gone from reading DNA, to writing it, to designing it. The results? Several AI-generated phages outperformed the wild-type virus, with one variant called EVO-Φ69 being 65x more powerful than natural viruses. One even uses an evolutionarily distant DNA packaging protein that researchers wouldn't have rationally designed. The implications are staggering: → Cocktails of these generated phages rapidly overcome antibiotic-resistant bacteria → Accelerated development of phage therapies for drug-resistant infections → A blueprint for designing synthetic biological systems at genome scale → Foundation for creating useful living systems with entirely novel capabilities This isn't science fiction anymore. AI models trained on 2 million bacteriophage genomes can now propose new genetic codes—and 16 out of 302 designs actually worked MIT Technology Review. We're watching the birth of generative biology in real-time. The ability to design life at the genomic level opens possibilities we're only beginning to imagine. What do you believe the opportunities and risks are of this virus making AI? The conversation is just beginning. 📄 Study: https://lnkd.in/ddP3Fjdp #SyntheticBiology #AI #GenerativeAI #Genomics #Biotechnology #Innovation #Science

Our DINO (dynamics-informed dataset to overcome the limitations of static molecular data in AI-driven drug discovery) proposal is public! Static molecular structures are useful, but they miss the dynamics that underlie real molecular function. Most AI models to date are trained on static data, causing them to suffer from state bias, miss key motions, and generate biologically implausible candidates. As a result, generated candidates often lack the biological plausibility needed for molecules to be a drug and require costly experimental validation. Can this be improved by embedding molecular dynamics directly into AI models? Victor Greiff, Rahmad Akbar, and I propose DINO, a dynamics-informed dataset designed to bridge static structural data and real molecular behavior in AI-driven drug discovery. By integrating experimental and synthetic molecular dynamics across proteins, antibodies, nucleic acids, small molecules, and complexes, DINO aims to "embed molecular motion into AI-driven design" and capture "biophysically realistic dynamics and functional behavior". Data obtained in this way can enable the learning of conformational ensembles, binding energetics, and functional kinetics in the next generation of AI models. By grounding molecular design in thermodynamic principles, DINO can enable AI systems to "move beyond static assumptions and generate biochemically plausible candidates with higher therapeutic potential", supporting mechanistic, uncertainty-aware modeling and more biologically realistic therapeutic design in silico. Read the proposal here: https://lnkd.in/e2tdz3gJ

What if climate change is already reshaping your brain, your metabolism, and your mental health… simultaneously? This newly published Viewpoint in The Lancet Group Planetary Health proposes a powerful systems-based framework to understand how climate and environmental stressors affect human health across multiple interconnected organ systems. * Figure 2 maps how climate-related stressors (e.g., heat, pollution, food and water insecurity, psychosocial stress) simultaneously impact gut integrity, kidney function, and brain health, reinforcing the idea of parallel, not isolated, pathways of disease. A key conceptual contribution of this work is positioning barrier integrity (gut and BBB) as a central interface through which environmental stress translates into systemic vulnerability, linking inflammation, microbiome alterations, and neurophysiological outcomes. From an Eco-Affective Health (EWAH Lab) perspective, this framework strongly aligns with a core principle: environmental change is not only a set of external exposures, but a deeply embodied, multisystem process that integrates physiology, affect, and lived experience. Importantly, the paper also highlights that vulnerability is not uniform, it emerges from the interaction between environmental exposures, biological susceptibility, and social conditions across the life course. This reinforces a critical implication: future research and policy must move beyond siloed models and adopt integrative, systems-level approaches that account for biological, environmental, and psychosocial dimensions simultaneously. Article link: https://lnkd.in/duz7EDdg Also, follow our work at ewahlab.com #EWAHLab #EcoAffectiveHealth #PlanetaryHealth #ClimateHealth #SystemsBiology #EnvironmentalHealth #GutBrainAxis #Neuroscience #GlobalHealth #SustainabilityScience

GENERATIVE BIOLOGY AI just wrote genetic instructions that cells actually followed – a breakthrough that turns biology into a programming language. For the first time ever, researchers at the Center for Genomic Regulation created AI-generated DNA sequences that successfully controlled gene expression in healthy mammalian cells. Think of it as writing software, but for living organisms. Why this matters: → The AI can design custom 250-letter DNA fragments with specific instructions like "activate this gene in stem cells becoming red blood cells but not platelets" → These synthetic enhancers worked EXACTLY as predicted when tested in mouse blood cells → Unlike previous efforts focused on cancer cells, this team worked with healthy cells, uncovering subtle mechanisms that shape our immune system → The researchers built a library of 64,000+ synthetic enhancers tested across seven stages of blood cell development Most fascinating was discovering "negative synergy" - where two factors that individually activate genes can completely shut them down when combined. This unlocks precision we never had before. The implications are enormous for gene therapy. Instead of being limited to DNA sequences evolution produced, we can now design ultra-selective gene switches customized to specific cells and tissues - potentially making treatments more effective with fewer side effects. Full paper: https://lnkd.in/en3bGZP9 Follow-up with @EricTopol's post about curing rare diseases with the existing genomic technology stack https://lnkd.in/eGCYMjGJ

Synthetic biology is - quite literally - our future. A goundbreaking new biological foundation model Evo2 achieves state-of-the-art prediction of genetic variation impacts and generates coherent genome sequences, spanning all domains of life. A diverse team from leading research institutions including Arc Institute Stanford University NVIDIA University of California, Berkeley trained the model on 9.3 trillion DNA base pairs and has fully shared all code, parameters, and data. A few highlights from the paper (link in comments) 🔬 Zero-shot prediction achieves state-of-the-art accuracy in genetic variant interpretation. Evo 2 can predict the functional consequences of genetic mutations across all domains of life without specialized training. It surpasses existing models in assessing the pathogenicity of both coding and noncoding variants, including BRCA1 cancer-linked mutations. This generalist capability suggests Evo 2 could revolutionize genetic disease research, reducing reliance on expensive, manually curated datasets. 🛠 Genome-scale generation paves the way for synthetic life design. Evo 2 can generate full-length genome sequences with realistic structure and function, including mitochondrial genomes, bacterial chromosomes, and yeast DNA. Unlike prior models, Evo 2 ensures natural sequence coherence, improving synthetic biology applications like engineered microbes or artificial organelles. This sets the stage for programmable biology at an unprecedented scale. 🧬 Unprecedented long-context understanding revolutionizes genomic analysis. Evo 2 operates with a context window of up to 1 million nucleotides—far beyond the capabilities of previous models—allowing it to analyze genomic features across vast distances. This ability enables it to accurately identify regulatory elements, exon-intron boundaries, and structural components critical for understanding genome function. Its long-context recall is a major breakthrough for interpreting complex biological sequences. 🎛 Inference-time search enables controllable epigenomic design. Evo 2’s generative abilities extend beyond raw DNA sequence to epigenomic features, allowing researchers to design sequences with specific chromatin accessibility patterns. This approach successfully encoded Morse code messages into synthetic epigenomes, demonstrating a new method for controlling gene regulation via AI. This could lead to breakthroughs in gene therapy and epigenetic engineering. 🔮 Future potential: Toward AI-driven biological design and virtual cell modeling. Evo 2 represents a major leap toward AI-powered genomic engineering. Future iterations could integrate additional biological layers—such as transcriptomics and proteomics—to create virtual cell models that simulate complex cellular behaviors. This could revolutionize drug discovery, genetic therapy, and even synthetic life creation.

#TissueSpecific atlas of human #ProteinAssociations is providing insights on how proteins interact across different tissues in the human body. By analyzing protein coabundance across 7,811 samples from 11 human tissues, researchers found that over 25% of protein interactions are tissue-specific—often shaped by specialized structures like #synaptic components. This atlas is already proving valuable for prioritizing candidate #diseasegenes and identifying novel #drugtargets, especially in complex #brain disorders such as #schizophrenia and #OCD. Findings were validated through orthogonal experiments (e.g., #cofractionation, #pulldown assays) and #AlphaFold2-based structural predictions. A similar effort focused on #inflammatory and #autoimmune diseases could add significant value, opening new avenues in #precisionmedicine and #therapeuticdevelopment. #TheScienceCircuit #TranslationalResearch #Proteomics #MultiOmics https://lnkd.in/ediBUBmc

🔥When Cancer Outsmarts Treatment, Science Must Outthink Cancer🔥 A recent Science Advances paper caught my attention, not just as a physician, but as someone who has spent decades watching cancer evolve faster than our therapies. The study reveals a critical mechanism in EGFR-mutant non-small cell lung cancer (NSCLC): 🧬 cancer cells don’t just mutate to resist drugs , they actively protect those mutations. 🔬 The key insight: Mutant EGFR proteins are stabilised by a newly identified P2Y2–integrin axis, driven by high extracellular ATP. This “protective shield” prevents EGFR degradation, allowing cancer cells to survive and thrive despite EGFR-TKI therapy. In simple terms: 👉 The cancer cell builds a biochemical bunker around its most dangerous mutation. ⚛️ What’s compelling is that when researchers disrupted this axis via P2Y2, FAK, or ATP-related pathways, especially in combination with TKIs , drug resistance weakened and tumour growth slowed. 👨⚕️ My perspective as a doctor: For years, we’ve focused almost exclusively on blocking the signal. This research reminds us that stability, trafficking, and cellular context matter just as much. 💎 Cancer is not static. It adapts, shields itself, and rewires survival pathways. ⚛️ Future oncology will not be: • One drug • One target • One pathway 👉 It will be systems-based, combination-driven, and biology-respecting. This is how we move from: ❌ chasing resistance ➡️ anticipating it And ultimately, this is how precision medicine becomes truly precise. 👉 Science like this gives hope not hype for patients facing resistant disease. Aspire. Inspire. Achieve.

Exciting work leveraging advanced molecular dynamics (MD) simulations to understand structural ensembles of ternary complexes for PROTAC design. In this study, we developed a physics-based protocol using a non-Markovian dynamic model with the Integrative Generalized Master equation (IGME) to predict non-canonical, metastable protein-protein interaction interfaces between oncogenic KRAS and the von Hippel-Lindau (VHL) E3 ligase. Leveraging ~1.5 ms of all-atom MD, we identified new encounter complex conformations that could open the door to novel PROTAC designs with improved efficacy. By targeting metastable states, our approach allows for more precise linker design to stabilize the ternary complex between the protein of interest (POI) and E3 ligase, enhancing degradation. Encouragingly, one of our predicted interfaces closely matches an experimentally validated PROTAC with high degradation potency. We believe this method offers a novel way to predict metastable protein-protein interfaces that can be used to streamline rational PROTAC design. Great work by Yunrui QIU with leadership from Xuhui Huang at University of Wisconsin-Madison. Read the paper here (open access in JACS Au): https://lnkd.in/eAT3xn_C #PROTACs #TargetedProteinDegradation #ComputationalBiology #DrugDiscovery #CancerResearch #MolecularDynamics