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bioRxiv2026

Long-read sequencing quantifies synthetic mRNA abundance, integrity and host response in vivo

Synthetic mRNA can be used to reprogram biological systems and is increasingly used in vaccines, gene therapies and other advanced therapeutics. However, measuring mRNA abundance, molecular integrity and biological effects in complex samples remains challenging. Existing assays typically quantify short transcript regions or infer delivery from lipid or protein readouts. Here we present a long-read nanopore sequencing method that directly quantifies synthetic mRNA in complex cell and tissue samples. The approach enables absolute quantification of full-length synthetic mRNA, maps degradation at nucleotide resolution and simultaneously profiles associated host transcriptional responses. Applied to lipid nanoparticle (LNP) delivered mRNA in mice, the method revealed tissue-specific delivery and degradation patterns and uncovered a critical disconnect between mRNA accumulation and protein expression across organs. This approach enables integrated measurement of mRNA fate, integrity and biological responses, and will enable mechanistic studies of RNA delivery, stability, translation and innate immune recognition.

pharmacology and toxicology↗

bioRxiv2026

A proteomic feasibility study connecting metabolic and synaptic pathway alterations in serum and extracellular vesicles to characterize treatment-resistant depression

Treatment-resistant depression (TRD) remains a major clinical challenge, yet the biological processes distinguishing TRD from non-treatment-resistant depression (nTRD) are incompletely defined. While circulating serum proteomes reflect broad systemic alterations associated with depression, extracellular vesicles (EVs) could provide a more selective representation of intercellular signaling relevant to treatment resistance. Here, we carried out a pilot study to evaluate the extent that parallel proteomic profiling of serum and serum-derived EVs could distinguish healthy controls (CON), nTRD, and TRD individuals. In this exploratory and hypothesis-generating study, serum proteomes exhibited robust global differences between depression groups and controls, largely reflecting shared systemic biology across nTRD and TRD. In contrast, EV proteomes showed limited global separation but revealed subtype-associated pathway differences. Relative to controls, nTRD EVs were enriched for immune and inflammatory pathways. By contrast, TRD EVs were characterized by enrichment of mitochondrial metabolism, oxidative phosphorylation, translational initiation, and MYC-regulated pathways, together with depletion of synaptic signalling, membrane trafficking, and cytoskeletal pathways. Comparative analysis of pathways significant in both contrasts revealed that these bioenergetic and translational signatures were selectively amplified in TRD relative to nTRD. Our exploratory analyses identified that the circulating EV cargo may reflect a treatment-resistance-specific reorganization of biological pathways not apparent in bulk serum proteomics. This study highlights parallel serum and EV proteomics as a complementary approach for molecular stratification in antidepressant resistance.

neuroscience↗

bioRxiv2026

Quantifying Assay Confidence in Barrier Organ-on-Chip Systems: A Monte Carlo Assay-Readiness Framework for Detecting Modest Permeability Shifts

Organ-on-chip (OoC) systems are increasingly used to generate human-relevant evidence in drug discovery and preclinical development, yet interpretation can be limited by the interaction of biological variability, device-to-device variability, and technical assay noise. Here, we describe the UstarFlowAI barrier-confidence framework, an in silico assay-readiness approach that quantifies whether a predefined permeability-related effect is resolvable under specified uncertainty. The proposed context of use is decision support for laminar, low-Reynolds-number airway epithelial monolayer barrier OoC assays in which device fabrication, pump performance, flow, wall shear stress, and biological fluctuation may affect permeability-related readouts. Monte Carlo simulations propagate defined manufacturing, biological, systemic, and well-level uncertainty and estimate the minimum detectable shift (MDS) under a prespecified decision rule. In the present proof-of-concept design, the reference workflow was parameterized at 9.4% technical coefficient of variation (CV), whereas the standardized workflow was parameterized at 5.0% CV, corresponding to an approximately 46.8% reduction in the modeled technical-noise component. At n = 48 replicate wells per group and a nominal total assay-noise operating point of 17% CV, a predefined 10% permeability shift exceeded the upper MDS uncertainty bound for the standardized workflow in both best- and worst-case scenarios, whereas it overlapped the 95% MDS interval for the reference workflow and therefore did not meet the prespecified PASS criterion. These findings demonstrate the behavior of the computational framework under the stated assumptions; they do not constitute experimental validation, regulatory qualification, or evidence that a 10% shift is universally meaningful across barrier OoC systems. We therefore propose a prospective wet-lab validation strategy in which model predictions are tested against airway barrier measurements generated under controlled sources of technical and biological variability. The framework is intended to complement, rather than replace, the underlying biological model by making assay uncertainty and decision thresholds explicit and auditable.

bioengineering↗

bioRxiv2025

A modular lentiviral system for multiplexed gene perturbation and functional analysis reveals interdependence of hormone receptors in breast cancer growth in vivo

Precise and flexible control of gene expression is essential for dissecting gene function in complex biological systems. Although recent developments in genetic engineering and CRISPR/Cas9 technology have expanded tools for gene activation, suppression and editing, their application in physiologically relevant models remains challenging, time consuming, and expensive. Here, we present a modular, doxycycline-inducible vector system that integrates gene overexpression, shRNA-mediated knockdown, and CRISPR/Cas9-mediated regulation within a single, lentivirus-compatible system. The modular design allows rapid exchange of selection markers, epitope tags, and reporters via Gateway cloning, providing broad adaptability across experimental settings. In addition to standard fluorescent and luminescent reporters, the system includes advanced sensors, such as Fucci cell cycle reporters, to enable monitoring of cellular processes. By combining fluorescence barcoding with combinatorial genetic perturbations, the platform supports multiplexed analysis of gene function and genetic interactions through phenotypic characterization by fluorescence imaging or flow cytometry. We demonstrate its utility in vivo with breast cancer intraductal xenografts, which suggest that ER+ breast cancer cells (MCF7) rely on androgen (AR), estrogen (ER) and progesterone receptors (PR) for in vivo growth. This versatile gene perturbation system provides tight temporal control, streamlined implementation, and high-content phenotyping capacity facilitating efficient in vitro and in vivo studies while reducing the use of animals in in vivo validation experiments. It thus expands the experimental repertoire for dynamic, multigene interrogation in complex systems.

molecular biology↗

bioRxiv2026

KBase Research Agent: Automated Multi-Agent Workflow Construction for Reproducible Genome Analysis

Constructing multi-step bioinformatics workflows, from read quality control through genome assembly to functional annotation, requires expertise in both biology and computational tool selection, creating a bottleneck for scalable and reproducible analysis. We present the KBase Research Agent, a multi-agent system for automating such workflows within the DOE Systems Biology Knowledgebase (KBase). Given a set of sequencing reads and a research objective, the agent constructs an analysis plan grounded in KBase documentation and a Knowledge Graph (KG) of the KBase application catalog, then selects, parameterizes, validates and executes appropriate KBase applications to carry out the workflow. The resulting analysis is preserved as a reproducible KBase Narrative. We evaluate the systems planning and execution quality against ground truth constructed from reference workflows derived from peer-reviewed Microbiology Resource Announcements. We further apply the agent to 100 previously unanalyzed bacterial isolate genomes from the JGI IMG/M database, where it autonomously performed read quality control, genome assembly, taxonomic classification with GTDB-Tk, and downstream analysis producing annotated genomes, reproducible Narratives, and draft manuscripts without human intervention. Across these experiments, the KBase Research Agent demonstrates the feasibility of domain-grounded, end-to-end scientific workflow automation in a production bioinformatics platform.

bioinformatics↗

bioRxiv2023

Non-asymptotic transients away from steady states determine cellular responsiveness to dynamic spatial-temporal signals

Majority of the theory on cell polarization and the understanding of cellular sensing and responsiveness to localized chemical cues has been based on the idea that non-polarized and polarized cell states can be represented by stable asymptotic switching between them. The existing model classes that describe the dynamics of signaling networks underlying polarization are formulated within the framework of autonomous systems. However these models do not simultaneously capture both, robust maintenance of polarized state longer than the signal duration, and retained responsiveness to signals with complex spatial-temporal distribution. Based on recent experimental evidence for criticality organization of biochemical networks, we challenge the current concepts and demonstrate that non-asymptotic signaling dynamics arising at criticality uniquely ensures optimal responsiveness to changing chemoattractant fields. We provide a framework to characterize non-asymptotic dynamics of systems state trajectories through a non-autonomous treatment of the system, further emphasizing the importance of (long) transient dynamics, as well as the necessity to change the mathematical formalism when describing biological systems that operate in changing environments.

biophysics↗

bioRxiv2024

A random mutagenesis screen enriched for missense mutations in bacterial effector proteins.

To remodel their hosts and escape immune defenses, many pathogens rely on large arsenals of proteins (effectors) that are delivered to the host cell using dedicated translocation machinery. Effectors hold significant insight into the biology of both the pathogens that encode for them and the host pathways that they manipulate. One of the most powerful systems biology tools for studying effectors is the model organism, Saccharomyces cerevisiae. For many pathogens, the heterologous expression of effectors in yeast is growth inhibitory at a frequency much higher than housekeeping genes, an observation ascribed to targeting conserved eukaryotic proteins. Abrogation of yeast growth inhibition has been used to identify bacterial suppressors of effector activity, host targets, and functional residues and domains within effector proteins. We present here a yeast-based method for enriching for informative, in-frame, missense mutations in a pool of random effector mutants. We benchmark this approach against three effectors from Legionella pneumophila, an intracellular bacterial pathogen that injects a staggering >330 effectors into the host cell. For each protein, we show how in silico protein modeling (AlphaFold2) and missense- directed mutagenesis can be combined to reveal important structural features within effectors. We identify known active site residues within the metalloprotease RavK, highly conserved residues in SdbB, and previously unidentified functional motifs within the C-terminal domain of SdbA. We show that this domain has structural similarity with glycosyltransferases and exhibits in vitro activity consistent with this predicted function.

microbiology↗

bioRxiv2018

Long read sequencing reveals poxvirus evolution through rapid homogenization of gene arrays

Large DNA viruses rapidly evolve to defeat host defenses. Poxvirus adaptation can involve combinations of recombination-driven gene copy number variation and beneficial single nucleotide variants (SNVs) at the same locus, yet how these distinct mechanisms of genetic diversification might simultaneously facilitate adaptation to immune blocks is unknown. We performed experimental evolution with a vaccinia virus population harboring a SNV in a gene actively undergoing copy number amplification. Comparisons of virus genomes using the Oxford Nanopore Technologies sequencing platform allowed us to phase SNVs within large gene copy arrays for the first time, and uncovered a mechanism of adaptive SNV homogenization reminiscent of gene conversion, which is actively driven by selection. Our work reveals a new mechanism for the fluid gain of beneficial mutations in genetic regions undergoing active recombination in viruses, and illustrates the value of long read sequencing technologies for investigating complex genome dynamics in diverse biological systems.

evolutionary biology↗

bioRxiv2019

Design and assembly of DNA molecules using multi-objective optimisation

Rapid engineering of biological systems is currently hindered by limited integration of manufacturing constraints into the design process, ultimately limiting the yield of many synthetic biology workflows.\n\nHere we tackle DNA engineering as a multi-objective optimization problem aiming at finding the best tradeoff between design requirements and manufacturing constraints. We developed a new open-source algorithm for DNA engineering, called Multi-Objective Optimisation algorithm for DNA Design and Assembly (MOODA), available as a Python package and web application at http://mooda.stracquadaniolab.org.\n\nExperimental results show that our method provides near optimal constructs and scales linearly with design complexity, effectively paving the way to rational engineering of DNA molecules from genes to genomes.

synthetic biology↗

bioRxiv2026

Kinemomics: spatiotemporal morphodynamic mapping of ventricular kinematic subpopulations in organotypic fetal heart slices

Recent advancements in omics technologies have deepened our understanding of complex biological systems and the interplay among gene expression, mechanical cues, and cellular metabolism. However, capturing the spatiotemporal dynamics of live cellular behavior within intact tissue contexts remains a major challenge. Here, we introduce Kinemomics, a framework for spatiotemporal mapping of morphogenetic kinematic states in living tissue. Using a novel fetal cardiac organotypic slice culture system that preserves both beating and morphogenesis, Kinemomics integrates live fluorescent labeling, timelapse imaging, and endpoint immunofluorescence to generate morphodynamic profiles across full-thickness developing myocardium. We validate Kinemomics under controlled Yap-1 signaling perturbations and demonstrate its ability not only to intrinsically decode region- and treatment-specific phenotypes, but also to resolve their spatiotemporal evolution during development and malformation. Kinemomics further reveals localized emergent developmental and malforming fronts that drive system-level behavior, establishing a generalizable platform for investigating computational morphodynamics and cross-scale interactions in complex spatiotemporal systems.

developmental biology↗

bioRxiv2026

Towards interoperable modeling of toehold-mediated strand exchange circuits across DNA nanotechnology and engineering biology

Originally developed for DNA nanotechnology, toehold-mediated strand exchange (TMSE) circuits are gaining traction in synthetic biology due to their high programmability, seamless integration with biological components, and robust operation across diverse environments and cell types. However, while forward-engineering in synthetic biology has benefited from automated genetic circuit modeling pipelines, there is currently a lack of accessible, automated tools for the mechanistic modeling of TMSE circuits integrated with these systems, hindering the development of new biotechnologies. The TMSE-BioCRNpyler Library allows TMSE molecules to be transcribed RNAs or fixed-concentration nucleic acids while leveraging existing BioCRNpyler features, such as upstream transcription regulation and downstream gene regulation. We demonstrate this librarys applicability by modeling published applications of TMSE circuits spanning a wide range of applications, including simple in vitro reactions, cell-free biosensors, and in vivo microbial and mammalian systems. Additionally, we validated that models compiled using the TMSE-BioCRNpyler Library produced results with < 0.2 % relative error compared to multiple models of TMSE previously developed in the literature. Finally, to streamline interoperability with existing models, we developed txt2biocrnpyler. This accompanying tool converts chemical reaction networks from the literature into a BioCRNpyler-ready source script and a Systems Biology Markup Language XML file -- a standard data format for sharing and simulating biological models. The TMSE-BioCRNpyler Library serves as a powerful new resource for the rational, automated design of molecular information processing systems.

bioengineering↗

bioRxiv2023

Principles and best practices of optimizing a micromirror-based multicolor TIRF microscopy system

TIRF (Total Internal Reflection Fluorescence) microscopy is a powerful tool for measuring the intra- and intermolecular dynamics of fluorescently-labeled single molecules. As TIRF measurements move to more complex biological systems with more fluorescent probes, the multi-band-pass dichroic that separates excitation from emission becomes limiting for the microscopes detection efficiency. To avoid this, multicolor colocalization-based experiments can employ "micromirror" (mm)TIRF, which replaces the dichroic with two 45{degrees}-angled rod mirrors that control the TIR excitation beam(s). Whereas a dichroic spectrally separates excitation and emission wavelengths, the micromirrors act to spatially separate the excitation beams from the collected emission photons within the objective lens itself. Comprehensive control of the TIR beam in mmTIRF can yield excellent signal to noise, and hence data quality, but at the price of increased optical complexity. Here, we introduce the theory behind these additional optical components and provide practical advice from our experience on the best way to set up, align, optimize, and maintain a mmTIRF instrument. We also demonstrate the practical effects of small misalignments to illustrate both the optimized signal quality and the degree of accuracy required to achieve it. We hope that this guide increases the accessibility of this type of instrumentation and helps researchers use it to produce data of the highest quality possible.

biophysics↗

bioRxiv2026

FLASH-P: Turning decades of biology into accurate causal networks with AI agents

Mechanistic networks that encode causal regulatory logic can predict the effects of genetic and environmental perturbations but constructing them is a bottleneck in systems biology because the relevant knowledge lies scattered across thousands of resources, untapped for both building and validating such networks. Here we present FLASH-P, a multi-agent framework that autonomously curates this literature into perturbable, signed-directed network models for any trait-species combination in under an hour without much computational power. Twelve FLASH-P networks across seven species predicted the directional outcome of 1,088 published perturbations with a mean accuracy of 90%. This accuracy was driven by the regulatory topology FLASH-P constructs, which is why it outperformed knowledge-graph derived networks. Its merging agent combined six networks into one that preserved single-trait accuracy and recovered pleiotropic effects, and consolidated independent runs of one trait into a comprehensive, high-accuracy network. FLASH-P networks enable applications that require trait models.

Plant Biology↗

bioRxiv2025

A Minimal Genetic Circuit for Cellular Anticipation

Living systems have evolved cognitive complexity to reduce environmental uncertainty, enabling them to predict and prepare for future conditions. Anticipation, distinct from simple prediction, involves active adaptation before an event occurs and is a key feature of both neural and aneural biological agents. Building on the moving average convergence-divergence principle from financial trend analysis, we propose an implementation of anticipation through synthetic biology by designing and evaluating experimentally testable minimal genetic circuits capable of anticipating environmental trends. Through deterministic and stochastic analyses, we demonstrate that these motifs achieve robust anticipatory responses under a wide range of conditions. Our findings suggest that simple genetic circuits could be naturally exploited by cells to prepare for future events, providing a foundation for engineering predictive biological systems.

synthetic biology↗

bioRxiv2020

Refined measurement of SecA-driven protein transport reveals indirect coupling to ATP turnover

The universally conserved Sec system is the primary method cells utilise to transport proteins across membranes. Until recently, measuring the activity - a prerequisite for understanding how biological systems works - has been limited to discontinuous protein transport assays with poor time resolution, or used as reporters large, non-natural tags that interfere with the process. The development of an assay based on a split super-bright luciferase (NanoLuc) changed this. Here, we exploit this technology to unpick the steps that constitute post-translational transport in bacteria. Under the conditions deployed, transport of the model pre-protein substrate proSpy occurs at 200 amino acids per minute with the data best fit by a series of large, [~]30 amino acid, steps each coupled to many (100s) ATP hydrolysis events. Prior to that, there is no evidence for a distinct, rate-limiting initiation event. Kinetic modelling suggests that SecA-driven transport activity is facilitated by the substrate (polypeptide) concentration gradient - in keeping with classical membrane transporters. Furthermore, the features we describe are consistent with a non-deterministic motor mechanism, such as a Brownian ratchet.

biochemistry↗

bioRxiv2021

Integrative genome-scale metabolic modeling reveals versatile metabolic strategies for methane utilization in Methylomicrobium album BG8

Methylomicrobium album BG8 is an aerobic methanotrophic bacterium that can mitigate environmental methane emission, and is a promising microbial cell factory for the conversion of methane to value-added chemicals. However, the lack of a genome-scale metabolic model (GEM) of M. album BG8 has hindered the development of systems biology and metabolic engineering of this methanotroph. To fill this gap, a high-quality GEM was constructed to facilitate a system-level understanding on the biochemistry of M. album BG8. Next, experimental time-series growth and exometabolomics data were integrated into the model to generate context-specific GEMs. Flux balance analysis (FBA) constrained with experimental data derived from varying levels of methane, oxygen, and biomass were used to model the metabolism of M. album BG8 and investigate the metabolic states that promote the production of biomass and the excretion of carbon dioxide, formate, and acetate. The experimental and modeling results indicated that the system-level metabolic functions of M. album BG8 require a ratio > 1:1 between the oxygen and methane specific uptake rates for optimal growth. Integrative modeling revealed that at a high ratio of oxygen-to-methane uptake flux, carbon dioxide and formate were the preferred excreted compounds; at lower ratios, however, acetate accounted for a larger fraction of the total excreted flux. The results of this study reveal a trade-off between biomass production and organic compound excretion and provide evidence that this trade-off is linked to the ratio between the oxygen and methane specific uptake rates.

microbiology↗

bioRxiv2021

Interactive, visual simulation of a spatio-temporal model of gas exchange in the human alveolus

In interdisciplinary fields such as systems biology, close collaboration between experimentalists and theorists is crucial for the success of a project. Theoretical modeling in physiology usually describes complex systems with many interdependencies. On one hand, these models have to be grounded on experimental data. On the other hand, experimenters must be able to penetrate the model in its dependencies in order to correctly interpret the results in the physiological context. When theorists and experimenters collaborate, communicating results and ideas is sometimes challenging. We promote interactive, visual simulations as an engaging way to communicate theoretical models in physiology and to thereby advance our understanding of the process of interest. We defined a new spatio-temporal model for gas exchange in the human alveolus and implemented it in an interactive simulation software named Alvin. In Alvin, the course of the simulation can be traced in a three-dimensional rendering of an alveolus and dynamic plots. The user can interact by configuring essential model parameters. Alvin allows to run and compare multiple simulation instances simultaneously. The mathematical model was developed with the aim of visualization and the simulation software was engineered based on a requirements analysis. Our work resulted in an integrative gas exchange model and an interactive application that exceed the current standards. We exemplified the use of Alvin for research by identifying unknown dependencies in published experimental data. Employing a detailed questionnaire, we showed the benefits of Alvin for education. We postulate that interactive, visual simulation of theoretical models, as we have implemented with Alvin on respiratory processes in the alveolus, can be of great help for communication between specialists and thereby advancing research.

bioinformatics↗

bioRxiv2022

Programmable eukaryotic protein expression with RNA sensors

The diversity of cell types and states can be scalably measured and defined by expressed RNA transcripts. However, approaches to programmably sense and respond to the presence of specific RNAs within living biological systems with high sensitivity are lacking. RNA sensors that gate expression of reporter or cargo genes would have diverse applications for basic biology, diagnostics and therapeutics by enabling cell-state specific control of transgene expression. Here, we engineer a novel programmable RNA-sensing technology, Reprogrammable ADAR Sensors (RADARS), which leverages RNA editing by adenosine deaminases acting on RNA (ADAR) to gate translation of a protein payload on the presence of endogenous RNA transcripts. In mammalian cells, we engineer RADARS with diverse payloads, including luciferase and fluorescent proteins, with up to 164-fold activation and quantitative detection in the presence of target RNAs. We show RADARS are functional either expressed from DNA or as synthetic mRNA. Importantly, RADARS can function with endogenous cellular ADAR. We apply RADARS to multiple contexts, including RNA-sensing induced cell death via caspases, cell type identification, and in vivo control of synthetic mRNA translation, demonstrating RADARS as a tool with significant potential for gene and cell therapy, synthetic biology, and biomedical research. One Sentence SummaryA new technology utilizing ADAR mediated RNA-editing enables robust reprogrammable protein expression based on target RNA transcripts in mammalian cells, leading to broad applications in basic science research, cell engineering, and gene therapy.

genomics↗