iMeta

Here we introduce OpenEvo, a fully open-source, low-cost turbidostat platform for automated continuous culture and directed evolution experiments. Existing tools are expensive, complex, or lack open-source hardware; OpenEvo addresses this gap. OpenEvo is a complete, fully automated evolution platform with detailed, illustrated construction instructions for beginners, open-source software and firmware, and a single device priced around $300. An optional PC-based version offers enhanced functionality, including remote access, programmable evolution cycles, programmable LED stimulation, and a data visualization tool. OpenEvo can cycle through three types of media for positive, negative, and neutral selection conditions, supporting a wide range of experimental designs. We validate the use of OpenEvo by evolving H. volcanii to grow from 15% to 12% salt over ~150 cycles, ~1,000 hours. Evolved cells grew 36% faster than wild-type at 12% salt. Whole-genome sequencing of adapted cells found SNPs and large deletions. We also demonstrate positive and negative selection using the OpenEvo LEDs to drive optogenetics via a Phytochrome B-based optogenetic tool, with light as the selection stimulus during over 4000 hours of growth. OpenEvo lowers the technical and cost barriers for continuous evolution experiments, serves as a teaching tool, and is designed to grow an open community of users who share modifications.

Flavins are organic redox cofactors central to metabolism and uniquely capable of acting as extracellular electron shuttles. For life to have emerged, it must have disengaged itself from its stationary geochemical environment, a step requiring mobile redox-active components. The role of flavins at life's origin has been debated for decades, centered on their capacity for both one- and two-electron chemistry, distinguishing them from nicotinamides and iron-sulfur clusters. Here we chart the abiotic reduction of flavin mononucleotide (FMN), flavin adenine dinucleotide (FAD), and riboflavin under hydrothermal conditions (40 {degrees}C, 1 bar N2 or 5 bar H2, pH 6, 8, and 10) by nickel (Ni) and iron (Fe). Flavins show greater environmental versatility than hydride carriers such as NAD and can harvest electrons from metals that would otherwise reduce water's protons to H2. Reduction is favoured under acidic conditions, while increasing molecular charge at higher pH impedes electron transfer. Ni acts as a hydrogenation catalyst, reducing deprotonated flavins via hydride transfer, suggesting mineral composition could have influenced geochemical selection of early electron carriers. Reduced FMNH2 and FADH2 were tested as electron shuttles toward Fe3+-containing minerals, revealing that FMNH2 enables faster mineral dissolution than FADH2. We further demonstrate complete redox cycling of FMN through Ni-assisted H2 reduction and subsequent oxidation by magnetite (Fe3O4) under inert atmosphere, releasing Fe2+. This study highlights the versatility, stability and redox chemical capabilities of flavins in prebiotic context.

Cysteine and serine proteases act as an immune hub in the plant apoplast to provide robust extracellular immunity during microbial colonisation. Microbial pathogens counteract these immune proteases by inhibiting their activity using small secreted proteins (SSPs). Traditionally, SSPs with protease-inhibitory activity are predicted using sequence-dependent database searches. However, in recent years, fungal SSPs have been shown to exhibit protease-inhibitory functions despite lacking the inhibitor domain that is annotated through sequence similarity searches. Hence, a large number of these novel SSPs with putative protease inhibitor functions are missed during detection and filtered out during sequence similarity searches. This necessitates the development of newer approaches to predict SSPs lacking an annotated inhibitor domain. Machine learning approaches, such as protein language models, have emerged as powerful tools for predicting protein functions. To date, no machine learning models have been developed to predict the protease-inhibitory activities of SSPs lacking an annotated inhibitor domain. Here, we introduce a protease inhibitor prediction pipeline, PINPOINT (Protease INhibitor PredictiOn at plant-pathogen INTerface). The PINPOINT pipeline combines fine-tuned protein language model classifiers, a structure-aware autoencoder, and effector prediction into a multi-level framework for identifying SSPs with predicted protease inhibitor functions. PINPOINT predicts protease inhibitors using SSPs sequences and monomeric structures with pre-computed structures obtained from the AlphaFold Protein Structure Database or predicted using the ESMFold public API. We successfully validated the PINPOINT platform using SSPs from the plant fungal pathogen Macrophomina phaseolina. Notably, the PINPOINT platform robustly predicted several of these SSPs as protease inhibitors including Sequence-unrelated but structurally similar (SUSS) effectors. We further validated the inhibitory potential of these predicted M. phaseolina SSPs using AlphaFold Multimer (AFM) screening against candidate apoplastic soybean cysteine and serine proteases. Additionally, this platform can be used as a pre-filtering step in AFM screening approaches to reduce the number of candidates for discovering novel SSPs with protease inhibitor function for cross-kingdom plant-microbe interaction studies. The PINPOINT platform will accelerate the prediction of novel SSPs including SUSS effectors with protease inhibitor functions in proteomes of any organisms. We made the PINPOINT pipeline accessible to the research community as a web-based notebook environment for interactive computing in Google Colab, available at https://github.com/iitj-mpg-lab/PINPOINT

Brain organoids offer an invaluable model system for studying human brain development and disease. However, the establishment of high-fidelity brain organoids with multiple cell lineages including vasculature and immune cells remains a huge challenge. Here, we present a new strategy to generate human cerebral organoids with vasculature and microglia-like cells using genetic code expansion technology (GCE-T) via site-specific protein engineering. The strategy integrates orthogonal genetic translation machinery in hPSCs via PiggyBac transposon system, enabling temporally control of ETV2 expression and endothelial differentiation in hPSC-derived cerebral organoids. The vascularized human cerebral organoids (vhCOs) exhibit coordinated development of multiple cell lineages and blood-brain barrier (BBB) features. Moreover, vhCOs form perfusable vascular network after transplanted in the immune-deficient mice. Single-nucleus RNA sequencing reveals enhanced neurovascular interactions, multi-brain-regional identities, diverse neuronal subtypes and specialized endothelial subclusters in vhCOs, closely resembling human fetal brain. Strikingly, we identify enriched microglia-like cells comprising three distinct subtypes in vhCOs, which contribute to microglia-vascular interactions and synergistically modulate vascular development. Upon Zika virus (ZIKV) infection, vhCOs show neurovascular dysfunction and impaired microglia development, offering new insights into viral-induced neurodevelopmental disorders. This study offers a unique platform for producing more valuable brain organoids with vasculature and immune components, opening a new avenue to advance organoid research and applications.

Motivation: Chimeric metagenome-assembled genomes (MAGs) that pool DNA from multiple organisms contaminate downstream analyses. Marker-gene tools such as CheckM2 miss low-level chimerism, and DNA foundation models have been proposed as a sequence-composition alternative, but whether large autoregressive models (Evo2, 7B parameters) outperform smaller contrastive models (DNABERT-S, 117M) has not been rigorously tested.

Single-molecule (SM) imaging (SMI)-based approaches have the powerful ability to capture receptor interactions, which are necessary for cell signaling, in their native live-cell environment. Yet, due to substoichiometric labeling, SMI generally provides only partial information on these interactions. We developed Deep-FISIK, which utilizes graph neural networks and multi-head attention for message-passing, to predict from SMI data the kinetics of homotypic interactions of the full receptor system. The input to Deep-FISIK are the SM detections in SMI experiments, without the need for explicit tracking. Thus, Deep-FISIK is compatible with labeling a higher fraction of receptors in the SMI experiments, increasing the prediction accuracy of the interaction kinetics parameters. The performance of Deep-FISIK is robust in the presence of a variety of deviations from the training data, indicating the applicability of Deep-FISIK to many receptor systems and SMI experiments.

Cancer cells rewire metabolism to support proliferation. Intriguingly, divergent metabolic choices are made to attain this common goal. Identifying the unique metabolic requirements for a specific cell has profound implications for cancer biology and precision medicine. Genome-scale metabolic models (GEMs) have emerged as powerful tools to systematically characterize, understand, and predict metabolism of cells and tissues. Despite being comprehensive, the current GEMs remain limited in their predictive power. Here, we present a new GEM of human cells, in silico Human Metabolic Essentiality (iHME), that significantly improves the prediction of metabolic dependencies at a reduced computational cost. Wse rationally downsized, curated, and corrected previous models to remove unsupported metabolic redundancies, which led to a slim model containing 4,377 reactions, 3,241 metabolites, and 1,825 genes. When used to reconstruct metabolic networks of 1,103 cancer cell lines, iHME recalled on average 84.6% of experimental essential genes, which is two-fold increase over previous models. Cholesterol biosynthesis was revealed to be the most reliably predicted pathway with alternative dependencies. Finally, we applied the model to reconstruct individualized networks and predict essential gene profiles for 8,384 patient tumor samples. Glucose transporter SLC2A1 (GLUT1) was identified as a context-specific dependency for head and neck cancers and ovarian cancer. Likewise, CDP-diacylglycerol synthase CDS2 was identified for skin cancer. Overall, iHME is a new genome-scale model for prediction of metabolic dependency at higher accuracy and computational efficiency.

Methanogenic archaea, such as Methanothermobacter marburgensis, represent a powerful biological platform for carbon capture and valorization, directly converting carbon dioxide (CO2) and molecular hydrogen (H2) into proteinogenic amino acids (AAs). In this study, we present a controlled and scalable strategy for tailoring AA production (biosynthesis and secretion) in continuous gas fermentation. By applying various Design of Experiments (DOE) techniques, we systematically identified and optimized key process parameters governing AA biosynthesis and shaping a targeted AA secretion profile. A hybrid modeling framework combining experimental data with scale-independent parameters derived from computational fluid dynamics (CFD) enabled robust performance prediction across bioreactor scales. This model-driven approach successfully translated the process from 120 mL glass bottles via 2 L to 150 L reactors, corresponding to a reaction-volume scale-up factor of 2000. These findings set the foundation for a robust and predictive platform for sustainable AA production, positioning archaea as a high-potential alternative in industrial biotechnology.

Influenza A viruses H3N2 and H10N5 represent, respectively, a persistently dominant seasonal pathogen and a newly documented zoonotic threat with the latter strain variants responsible for the first confirmed human fatality in January 2024, yet no vaccine platform currently addresses co-protection against both subtypes within a unified immunogen. We report here the immunoinformatics based vaccine design and multi-layered computational validation of a 419-amino-acid multi-epitope subunit vaccine construct targeting conserved hemagglutinin (HA) and neuraminidase (NA) antigens identified through multiple sequence alignment of the avian H10N5 (A/swine/Hubei/10/2008) and H3N2 human reference strain sequences to identify viral agents undergoing mammalian adaptations. Linear B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were predicted using ABCpred, BCEpred, BepiPred 2.0, NetMHCpan 2.1, and NetMHCpan 4.0, then filtered through VaxiJen 3.0, AllerTOP v2.1, and ToxinPred to retain only antigenic, non-allergenic, non-toxic candidates. The final construct, incorporating an avian {beta}-defensin N-terminal adjuvant with GPGPG, AAY, and EAAAK linkers, exhibited a molecular weight of 43.9 kDa, instability index of 31.15, and SOLPro solubility probability of 0.763. Tertiary structure modeling via I-TASSER and GalaxyRefine achieved 84.4% Ramachandran-favored residues. Molecular docking against TLR3 and TLR7 yielded binding free energies of -16.1 and -16.8 kcal/mol with picomolar dissociation constants. Molecular dynamics simulations confirmed complex stability over extended trajectories. Furthermore, codon optimization produced a Codon Adaptation Index of 1.0 for E. coli K12 expression. In silico immune simulation demonstrated robust activation of humoral and cellular immunity including elevated IgG1, IgM, IFN-{gamma}, IL-2, rapid NK cell expansion, and broad B-cell clonal diversity. These findings establish a computationally validated candidate capable of providing protection against influenza in multiple host organisms, warranting experimental advancement.

Systematic reviews in computational biology require screening large heterogeneous bibliographic sets, especially when topics span computational methods, cancer genomics and statistical modelling. This paper presents a reproducible semantic triage pipeline that combines SPECTER scientific-document embeddings, research-question similarity, proposal-summary similarity and domain keyword coverage to rank candidate studies for systematic review screening. The pipeline was evaluated on 2,231 Covidence records, including 120 final included studies (prevalence = 5.38%), against keyword-only, TF-IDF, BM25, MiniLM, PubMedBERT and SPECTER-only baselines. SPECTER-hybrid achieved the highest average precision (AP = 0.546), recovered 50% of included studies after screening 4.48% of records, and produced an 11.16-fold enrichment over prevalence. Ablation analysis showed that semantic-keyword combinations consistently outperformed single-signal variants. These findings suggest that citation-informed hybrid ranking can support literature triage while retaining human reviewers as final decision-makers.

Enzymatic depolymerization of polyethylene terephthalate (PET) has advanced rapidly, alongside a growing volume of publicly available metagenomic data from microbial communities under sustained selective pressure from plastic exposure. Reasoning that such environments may harbor underexplored polyester-active enzymes, we developed a targeted mining workflow that screens exclusively plastic-associated datasets through multi-step bioinformatic filtering--integrating catalytic-motif screening, disulfide-topology validation, structural-similarity scoring, and phylogenetic profiling--to recover high-confidence PETase candidates. Applied to 271 plastic-associated metagenomes, the pipeline yielded 21 non-redundant candidates, several of which combine the Type I catalytic motif (GHSMGGGG) with Type II-like extended loops and secondary disulfide bonds. Two candidates were experimentally confirmed as PET hydrolases; the more active, PET-KR1, is a thermostable enzyme (Tm = 66.5 {degrees}C) that depolymerizes PET across a broad temperature range, with markedly higher productivity on powdered than on film substrate. PET-KR1 achieved optimal depolymerization at 50 {degrees}C, yet at 60-65 {degrees}C, where total yields declined, the product pool was more strongly enriched in the terminal monomer TPA, suggesting that thermostability and substrate accessibility are the primary targets for further engineering. Molecular dynamics simulations revealed a conserved hydrophobic binding network around the catalytic serine, consistent with established PETase substrate-recognition modes, and rational disulfide engineering raised the melting temperature by 3.5 {degrees}C, confirming amenability to further optimization. Overall, PET-KR1 expands the scaffold space available for PETase engineering, while the discovery workflow, built entirely on publicly available tools and open-access data, provides a reproducible strategy for metagenomic mining of novel PET-degrading enzymes toward biocatalytic PET recycling.

Scientific coding agents are difficult to benchmark because many research tasks require executable work yet produce ambiguous or hard-to-verify outputs. Because benchmark construction requires substantial time and resources, automation offers a path to accelerating methods evaluation. We introduce an interactive framework for constructing scientific-agent benchmarks from peer-reviewed papers and diagnosing agent behavior through trace inspection. We apply it as a case study in spatial transcriptomics alignment, constructing 40 tasks from SABench in which agents submit coordinate tables aligning pairs of two-dimensional tissue slices. Across 120 runs and three configurations, we compare a basic prompt, a package-aware prompt, and a full prompt with a prebuilt virtual environment. In this setting, richer package and environment context increased tool exploration but reduced the mean alignment score relative to the basic prompt (0.36 vs. 0.43; 95% CI, [-0.11,-0.03]). Trace inspection showed that added scaffolding often induced unnecessary transformations, fragile package-first workflows, and infrastructure failures. These results illustrate how specialized tooling can alter agent behavior and why scientific-agent benchmarks should evaluate agent traces and the workflows that produce them in addition to the final outputs.

Marine macroalgae are valuable sources of bioactive compounds. In this study, we thus investigated the chemical composition and biological activity of an extract from the green seaweed Ulva lacinulata, composed of small bioactive compounds. Comprehensive compositional analyses and high-resolution mass spectrometry revealed its diverse molecular profile composed in particular of peptides/amino acid derivatives, saccharides, low-chain fatty diacids, oxylipins and minerals. Its anti-inflammatory activity was assessed after 6 h pre-treatment in LPS-stimulated cultured RAW 264.7 macrophages, showing that it significantly and dose-dependently reduced the expression and/or secretion of pro-inflammatory cytokines such as TNF-alpha; and IL-6, and targeted the NF-kB signaling cascade. It modulated the SIRT1-AMPK signaling axis and increased the LC3-II/LC3-I ratio, supporting the activation of a controlled autophagic response. This work highlighted the potential of this marine-derived extract as a safe and effective functional ingredient for the development of functional food and/or dietary supplements targeting chronic low-grade inflammation.

The iron (Fe) nitrogenase drives bacterial methane (CH4) formation by converting carbon dioxide (CO2) to CH4 in a single enzymatic step. Enhancing the initial CH4 formation activity of Fe-nitrogenase and expanding the product spectrum to hydrocarbon chains could lead to a route for sustainable feedstock chemicals. Here, we performed the first directed evolution campaign on the Fe-nitrogenase aimed at optimizing the hydrocarbon production. We achieved an ~8-fold increase in CH4 formation by Fe-nitrogenase expressing Rhodobacter capsulatus cultures in three rounds of site-saturation mutagenesis. The best performing mutant (F362ManfD, Y85FanfD, T360SanfD) extends the in vivo product spectrum of the nitrogenase to ethane (C2H6) and exhibits 6-fold higher rates for CO production in vitro, whereas the formation of the undesirable byproduct formate was abolished. Electron microscopy-based structural analysis identified a methionine and water potentially stabilizing the transition state and fine-tuning the CO2 reduction mechanism and activity.

Single-cell RNA sequencing now routinely produces detailed maps of cell types and states, but interpreting a finished project remains harder than it should be. Once the analysis is done, the results are usually handed over as static reports, figure panels and supplementary tables. A biologist who later wants to revisit an annotation, recompute a cell-type proportion or check whether a pathway is specific to one group typically has to return to a bioinformatician rather than explore the data directly. We developed scSpark to close this gap. The platform takes the completed outputs of a single-cell project: cell annotations, embeddings, differential-expression tables, trajectories, cell-cell communication networks and enrichment result and serves them through a web browser as an interactive workspace. Heavy computation stays upstream: scSpark indexes the precomputed objects under a single project structure and exposes them through six modules for cell annotation, differential analysis, trajectory exploration, cell-cell communication, functional interpretation and AI-assisted result interrogation. Every action in these modules, from a query to a label change, an export or an AI-generated summary, is linked to a specific project version, data object, parameter set and output file, so that any conclusion can be traced back to the evidence behind it. We illustrate the platform by reworking a published periodontitis dataset through this interface. scSpark does not replace upstream pipelines or expert judgement; it is a layer that makes their results easier to inspect, revise and reuse, and that turns a single-cell project from a one-off report into an interpretation others can follow and check.

Predicting enzymatic reaction mechanisms is critical for understanding enzyme function and for designing and dis-covering new enzymes. Current computational predictors rely on deterministic, rule-based dictionaries, which per-form well on in-distribution tasks but fail to generalize to out-of-distribution (OOD) chemistry. To address this limita-tion, we present EZSolver, a template-free, generative framework for polar enzymatic mechanism prediction. Powered by a flow matching predictor (EZFlow) and navigated by an evaluator-guided bidirectional beam search, EZSolver learns the chemistry of electron redistribution instead of memorizing rigid templates. Evaluated across diverse en-zyme classes, EZSolver achieves a 60.0% accuracy and an 84.6% chemical plausibility rate for full mechanism predic-tion of unseen polar enzymatic reactions. While rule-based models collapse without predefined templates, EZSolver successfully extrapolates chemical knowledge to infer uncatalogued pathways, as demonstrated during rigorous OOD benchmarking. By illuminating enzymatic chemical mechanisms, EZSolver helps pave the way for automated predic-tion of enzyme function and discovery and design of novel biocatalysts for sustainable chemistry.

Due to its ability to establish a pool of undetectable and latently infected cells that can initiate viral production through random reactivation, a cure to human immunodeficiency virus (HIV) infections has remained elusive. Many approaches have been proposed, including the "shock and kill" method where latency reversing agents (LRAs) are administered to reactivate latently infected cells out of latency and remove them through immune targeting and clearance, and the "block and lock" method where latency promoting agents (LPAs) are administered to inhibit reactivation and potentially induce a "deep latency" state where infected cells can no longer reactivate. Previous large scale drug screen studies have demonstrated a correlation between a compound's capability to modulate the fluctuations (or "noise") in HIV gene expression and its potential to modulate HIV latency. However, measurements of gene expression noise are labor- and cost-intensive. To circumvent these drawbacks, we trained a variational autoencoder (VAE) on a previously published large scale time-lapse fluorescence microscopy dataset, and performed an in silico screening of ~175,000 compounds for HIV latency modulators. Out of the top 113 predicted modulators that were experimentally tested, 16 latency reversing agent (LRA) synergizers and 2 latency promoting agents (LPAs) were confirmed, yielding an overall experimental hit rate of 15.9%. Our work demonstrates that in silico drug screening modalities, guided by existing large-scale experimental datasets, can yield high experimental hit rates, reducing costs incurred from labor-intensive wet lab-focused methodologies.

Molecular information is vital for imaging technology. Optical imaging acquires molecular specificity almost exclusively via labeling strategy, which is fundamentally constrained by limited multiplexing capacity, high running costs, and experimental complexity. Conversely, label-free optical imaging offers substantial technical simplicity but is believed to have little true molecular specificity. Contrary to common belief, here we introduce super-multiplex optical imaging without labeling. By systematically studying paired vibrational spectroscopic imaging and mass spectrometry imaging, we discovered a surprisingly strong (more than 0.9) correlation between their latent space representations, supported by both experiments and theory. This insight prompts us to build supervised learning models to successfully predict spatial distribution of 100 molecular species directly from label-free vibrational images across diverse tissue systems. We developed this technology, named Prediction through Learning with AdvaNced Chemical Kaleidoscope (PLANCK), and demonstrated it with both infrared-based vibrational imaging of organ-scale tissues and Raman-based vibrational imaging of live tissues. Powered by AI, PLANCK decodes the exquisitely rich but otherwise hidden vibrational information into a surprisingly large number of ([≥]100) specific molecular species, providing a cost-effective and scalable solution for basic research and translation, including applications in live imaging.

Cyanobacteria manage photosynthetic and environmental stresses through transcriptional programs controlled by regulators also affecting carbon flux, growth states, and metabolic output that bioproduction seeks to optimize. This regulatory architecture and its most influential nodes remain incompletely characterized. We hypothesized two influential regulator layers: a conserved core responding to common stresses, and species-specific regulators mediating strain-level niche adaptations. Mapping both layers underpins understanding genome[->]regulatory-network[->]phenotype flow, enabling global transcription machinery engineering for reliable bioproduction. To test our hypothesis, we constructed conserved-core and species-specific gene regulatory networks (GRNs) for three cyanobacteria, Synechococcus elongatus PCC 7942, Synechocystis sp. PCC 6803 and Picosynechococcus sp. PCC 7002, integrating a manually curated multi-pipeline regulator inventory with 1,098 harmonized transcriptome states for the 1,362-gene tri-homolog core genome. We quantified each regulator influence using local (degree, k-core), global (betweenness, closeness), and community-aware (eigenvector) centrality measures, and an Integrated Centrality score aggregating influence across complementary topological measures. High-influence regulators are predicted to exert broad metabolic effects when manipulated, making them priority candidates for single-target engineering interventions that modulate multiple genes and reprogram complex phenotypes. Across the three cyanobacteria, the two GRN layers proved topologically distinct: the conserved core concentrated influence in stress-related hubs (11 of its top 15 by Integrated Centrality were stress-related), while species-specific networks spread influence across functionally diverse regulators. Stress-coupled enrichment also held per individual centrality measure: regulators ranking top in both the core and species-specific GRNs by the same measure were mostly stress-related (15 of 19 instances), including the multi-stress regulators RpaB, Rre1, and BolA, the heat-shock HrcA, and the nitrogen NtcA. In species-specific GRNs, stress-related regulators remained the leading category alongside circadian, carbon-metabolism, morphology, and housekeeping regulators, including PlmA, Pex, TetR, and SrrB in PCC 7942; KaiC3, Sycrp1, Rre28, and Bhl in PCC 6803; and Zur, Sycrp1, and NarL in PCC 7002. High-influence putative regulators included the iron-stress AraC-family paralogs IutR1-IutR3, OmpR-family paralogs OmpR1-OmpR2, and chromosome- or plasmid-encoded Xre-family, AraC, and HypP. Stress regulation emerges as a recurring high-influence axis across these networks. The conserved core identifies universal regulatory programs, and species-specific layers reveal strain-level innovations for cross-strain transfer to support engineering of robust bioproduction. ImportanceCyanobacteria are studied as platforms for sustainable, carbon-recycling production of fuels and chemicals from sunlight, water, and atmospheric carbon dioxide. Their reliable deployment in industrial settings is limited by environmental stresses that depress photosynthetic efficiency and product yields. The same regulatory proteins that govern stress responses also control how cells partition carbon, switch growth states, and direct metabolic output, making them natural levers for engineering robust production strains. Yet systematic, cross-species maps of these regulators have been missing. We present the first comparative regulatory map spanning three biotechnologically important model cyanobacteria, Synechococcus elongatus PCC 7942, Synechocystis sp. PCC 6803, and Picosynechococcus sp. PCC 7002, and identify the conserved regulators most influential across all three. The resulting catalog prioritizes candidate targets for experimental validation, and the supporting datasets and analytical framework are released for reuse to support efforts to engineer cyanobacterial strains for reliable industrial bioproduction.

Lipid Nanoparticles (LNPs) are widely used as delivery systems for nucleic acid therapeutics, where transfection efficiency is determined by both the identities of constituent lipid components and their composition ratios. While prior studies have focused on learning molecular representations for individual components, modeling how multiple components and their ratios jointly influence LNP performance remains underexplored. In this work, we propose STRATA, a framework that models molecule interaction between LNP components, which is known to contribute to LNP transfection efficiency. Our approach is built on two complementary views: (1) a ratio-centric view that captures interaction patterns induced by composition ratios through a transformer with a Ratio-induced Positional Embedding, and (2) a molecule-centric view that incorporates interaction-induced effects into structure-based molecule embeddings. By jointly training and aligning these views, our model integrates molecular structure and composition ratio within a unified framework that captures interaction-driven effects. Experiments demonstrate that our method improves prediction accuracy and generalization to unseen molecules and ratios, highlighting the effectiveness of our approach. Implementation code will be available after acceptance.