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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,621 records · Page 90Linked to original sources

bioRxiv2020

Harnessing the central dogma for stringent multi-level control of gene expression

Strictly controlled inducible gene expression is crucial when engineering biological systems where even tiny amounts of a protein have a large impact on function or host cell viability. In these cases, leaky protein production must be avoided at all costs, but ideally without affecting the achievable range of expression. Here, we demonstrate how the central dogma offers a simple way to effectively address this challenge. By simultaneously regulating both transcription and translation, we show how relative basal expression of an inducible system can be greatly reduced, with minimal impact on the maximum induced expression rate. Using this approach, we create several stringent expression systems displaying >1000-fold change in their output after induction in vivo and up to a 350-fold change when used in a cell-free expression system. Furthermore, we find that multi-level regulation is able to suppress transcriptional noise and creates a digital-like switch when transitioning between on and off states. This work provides foundational knowledge and a genetic toolkit of parts to create multi-level gene expression controllers for those working with toxic genes or requiring precise regulation and propagation of cellular signals. It also demonstrates the value of exploring more complex and diverse regulatory designs for synthetic biology.

synthetic biology↗

bioRxiv2020

Multiomics data collection, visualization, and utilization for guiding metabolic engineering

Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we present a set of tools that, combined, provide the ability to store, visualize and leverage these data to predict the outcome of bioengineering efforts.

synthetic biology↗

bioRxiv2022

SEN1 is responsible for molybdate transport into nodule symbiosomes for nitrogen fixation in Lotus japonicus

Symbiotic nitrogen fixation (SNF) in legume root nodules requires a steady supply of molybdenum (Mo) for synthesis of the iron-Mo cofactor for nitrogenase in bacteroids. For this nutrient to be exported by the host plant it must cross the peribacteroid membrane (PBM), however, the molybdate transporter responsible has not yet been identified. A Lotus japonicus symbiotic mutant, sen1, forms nodules that do not fix nitrogen; it has nodule defects and bacteroid degradation. The biochemical function and subcellular localization of SEN1 protein remains to be elucidated. Here, we found a new phenotype in which the SEN1 mutation resulted in increased Mo accumulation in the nodule host fractions but decreased Mo accumulation in the bacteroids at 10 days post inoculation. We identified the molybdate efflux transport activity of SEN1 via heterologous expression in yeast. SEN1 was expressed exclusively in nodules, and its expression was stable in response to varying Mo supply in nutrient solution. In situ immunostaining verified that the SEN1 protein is localized, in part, to the PBM in the rhizobium-infected cells. Taken together, these results confirmed that SEN1 is responsible for mediating molybdate efflux from the cytosol of nodule host cells to the symbiosomes for SNF. Furthermore, SEN1 mutation reduced the expression of nifD and nifK, suggesting that SEN1 may be pertinent to iron-Mo-cofactor assembly. This work fills the knowledge gap regarding how molybdate is allocated from the host plant to the bacteroids; such knowledge is critical for developing new SNF biological systems in non-legume plants. One-sentence summarySEN1 is localized partly in the peribacteroid membrane of nodule cells and mediates the molybdate exportation from the host plant cytosol to the symbiosomes for symbiotic nitrogen fixation.

plant biology↗

bioRxiv2025

Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex

Animals can solve new, complex tasks by reusing and adapting what theyve learned before. This kind of flexibility depends not just on having prior experience, but on how that experience was structured in the first place. The design of early training curriculum is especially important: poorly structured experiences can hinder abstraction and limit generalization, while carefully structured training promotes more flexible and adaptive behavior. Yet, the neural mechanisms supporting this process remain unclear. To investigate how early training shapes learning we first trained recurrent neural networks (RNNs) on variants of an odor-timing task previously used to study complex timing behavior in mice. We then tested the RNN predictions on how previous experience affects generalization using behavioral and electrophysiological recordings in mice trained on the same task using staged training sequences. RNNs and mice trained without well-structured early experience developed rigid strategies and made repeated errors. In contrast, those given more balanced early training were better able to generalize and showed similar neural activity patterns that reflected the tasks underlying temporal structure. Using dynamical systems approaches, we reveal a mechanism for this effect: networks trained with appropriately structured curricula developed distinct dynamical motifs that support the correct abstractions when complexity was increased. Networks that lacked early training or received remedial curricula developed single fixed-point solutions that failed to generalize beyond the training stimuli. Together, these findings demonstrate that it is not just the presence of prior experience, but its structure, that governs how flexible and generalizable knowledge emerges in both biological systems and computational models.

neuroscience↗

bioRxiv2025

Generative design of synthetic gene circuits for functional and evolutionary properties

1AbstractsIn the past decades, a wide suite of design tools for biological systems have been developed, but using these to create biotechnologies that achieve reliable and predictable behaviour remains challenging. Modelling approaches have enabled researchers to traverse the vast search space of genetic circuits more efficiently, while machine learning has proven useful for designing parts and predicting their function or evolutionary properties. Generative algorithms have the potential to leverage these features to design entire genetic circuits from the sequence level, but have only recently begun to be applied to synthetic biological applications. Here, we show that even simple generative models like the conditional variational autoencoder (CVAE) can produce novel genetic circuits that match complex dynamic functions such as signal adaptation. Using in silico RNA simulation, we construct a dataset of RNA sequences and convert them to circuits via RNA interaction predictors, allowing us to estimate functional features alongside evolutionary stability and interpret model-learned features. Our model generates diverse distributions of circuits that match their target adaptation specification well, even when limited to small training data sets. Structures in the embedding space correspond to motifs previously identified as crucial for adaptation and reflect the design rules for adaptable circuits. Framing adaptation as a single design objective outperforms other input representations, reflecting the importance of choosing the correct data encoding for generating genetic circuits. Finally, we show that functional and evolutionary properties can be prompted simultaneously, providing a proof-of-concept for the combined design of phenotype and evotype.

synthetic biology↗

bioRxiv2024

Autonomous biogenesis of the entire protein translation machinery excluding ribosomes

Recreating the conditions for autonomous biogenesis of the protein translation machinery is fundamental to our understanding of living systems and is essential for the assembly of minimal cell models. All components of the translation machinery, including the ribosomes, translation factors and aminoacyl tRNA synthetases, are made of proteins and are therefore involved in their own synthesis, posing a unique challenge for self-biogenesis. Here, we created physicochemical conditions for autonomous biogenesis of all the translation machinery, excluding the ribosome. We surface-immobilized synthetic genes coding for all thirty components as densely packed DNA brushes forming a reaction center that localizes, concentrates and catalyzes their simultaneous synthesis. To demonstrate their activity, we first determined empirically the minimal critical concentration of each translation protein required to initiate its own self-biogenesis in bulk solution. We then assembled a minimal gene expression reaction with all translation proteins at their critical concentrations. Under these dilute conditions, reporter proteins were not synthesized unless the DNA brushes encoded all the translation proteins, thereby demonstrating their co-synthesis, functionality and engagement in their own synthesis. This scenario of a complex biochemical process that amplifies itself can be generalized and extended to impact our understanding toward the design of autonomous self-replicating biological system.

synthetic biology↗

bioRxiv2020

Dissecting phenotypic transitions in metastatic disease via photoconversion-based isolation

Cancer patients presenting with surgically resectable disease often harbor occult metastases, a potential source of relapse that is targetable only through systemic therapy. Studies of this occult fraction have been limited by a lack of tools with which to isolate discrete cells based on spatial grounds. We developed PIC-IT, photoconversion-based isolation technique allowing efficient recovery of cell clusters of any size including solitary disseminated tumor cells (DTCs), which are largely inaccessible otherwise. In a murine pancreatic cancer model, transcriptional profiling of spontaneously arising DTCs revealed phenotypic heterogeneity, functionally reduced propensity to proliferate and enrichment for inflammatory-response phenotype associated with NF-{kappa}B /AP-1 signaling. Pharmacological inhibition of NF-{kappa}B depleted DTCs but had no effect on macrometastases, suggesting DTCs are particularly dependent on this pathway. PIC-IT enables systematic investigation of the earliest stages of metastatic colonization. Moreover, this new technique can be applied to other biological systems in which isolation and characterization of spatially distinct cell populations is not currently feasible.

cancer biology↗

bioRxiv2020

A multiplexed, automated evolution pipeline enablesscalable discovery and characterization of biosensors

Biosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Using aptamer-coupled ribozyme libraries and a novel ribozyme regeneration method, we developed de novo rapid in vitro evolution of RNA biosensors (DRIVER) that enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identified and validated biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors were applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors were also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

bioengineering↗

bioRxiv2024

AC-BioSD : A biomolecular signal differentiator module with enhanced performance

Temporal gradient estimation is a pervasive phenomenon in natural biological systems and holds great promise for synthetic counterparts with broad-reaching applications. Here, we advance the concept of BioSD (Biomolecular Signal Differentiators) by introducing a novel biomolecular topology, termed Autocatalytic-BioSD or AC-BioSD. Its structure allows for insensitivity to input signal changes and high precision in terms of signal differentiation, even when operating far from nominal conditions. Concurrently, disruptive high-frequency signal components are effectively attenuated. In addition, the usefulness of our topology in biological regulation is highlighted via a PID (Proportional-Integral-Derivative) bio-control scheme with set point weighting and filtered derivative action in both the deterministic and stochastic domains.

synthetic biology↗

bioRxiv2024

NicheTrans: Spatial-aware Cross-omics Translation

Spatial omics technologies have revolutionized our studies on tissue architecture and cellular interactions at single-cell resolution. While spatial multi-omics approaches offer unprecedented insights into complex biological systems, their widespread adoption is hindered by technical challenges, specialized requirements, and limited accessibility. To address these limitations, we present NicheTrans, the first spatially-aware cross-omics translation method and a flexible Transformer-based multi-modal deep learning framework. Unlike existing single-cell (non-spatial) translation methods, NicheTrans uniquely incorporates both cellular microenvironment information and flexible integration of multi-modal data, such as morphology and prior knowledge. We validated NicheTrans across diverse biological cases: Parkinsons Disease (PD), Alzheimers Disease (AD), breast cancer, and lymph nodes. Our approach demonstrated superior performance compared to existing single-cell methods, highlighting the crucial role of spatial and multi-modal information in cross-omics translation. Through NicheTrans, we uncovered spatial multi-omics domains that were not detectable through single-omics analysis alone. Model interpretation revealed key molecular relationships, including gene programs associated with dopamine metabolism and amyloid {beta}-associated cell states. Additionally, using translated protein markers as spatial landmarks, we quantified the spatial organization of key glial cell subtypes in the AD brain. NicheTrans represents a powerful tool for generating comprehensive spatial multi-omics insights from more accessible single-omics measurements, making multi-omics analysis more feasible for the broader research community.

bioinformatics↗

bioRxiv2018

ComPath: An ecosystem for exploring, analyzing, and curating pathway databases

Although pathways are widely used for the analysis and representation of biological systems, their lack of clear boundaries, their dispersion across numerous databases, and the lack of interoperability impedes the evaluation of the coverage, agreements, and discrepancies between them. Here, we present ComPath, an ecosystem that supports curation of pathway mappings between databases and fosters the exploration of pathway knowledge through several novel visualizations. We have curated mappings between three of the major pathway databases and present a case study focusing on Parkinsons disease that illustrates how ComPath can generate new biological insights by identifying pathway modules, clusters, and cross-talks with these mappings. The ComPath source code and resources are available at https://github.com/ComPath and the web application can be accessed at http://compath.scai.fraunhofer.de/.

bioinformatics↗

bioRxiv2026

A Modular In-Incubator Microscope for Longitudinal Live Cell Microscopy

Longitudinal live cell imaging is valuable for characterizing dynamic morphological and phenotypic changes in biological systems. However, conventional approaches rely on manual microscope operation, which is labor-intensive, limits imaging frequency, and disrupts the cellular environment. These constraints reduce scalability, increase experimental variability, and restrict both the duration and temporal resolution of continuous imaging. Although automated imaging platforms partially address these limitations, existing solutions are often constrained by the cost, footprint, and inflexibility of in-incubator microscopes or stage-top incubators. Here, we present an automated in-incubator epifluorescence microscope designed for long-term operation. The system features a modular architecture with optional multi-fluorescence imaging, automated plate scanning, configurable light sources, and compatibility with multiple plate formats, including integration with fluidic automation devices. By positioning the light sources and control electronics outside the incubator, the platform improves thermal stability and long-term operational reliability. This approach enables continuous, high-frequency imaging over extended durations, providing a source of rich data for quantifying time-dependent tissue phenotypes, morphological remodeling, and transient biological processes.

bioengineering↗

bioRxiv2026

An Open-Source Magnetofluorescence Imaging Platform forPlate-Scale Screening of Magnetic Field Effects in LiveBacteria

Magnetic field effects (MFEs) in biological systems are typically small and experimentally challenging to measure reproducibly across large sample populations. Existing approaches to measure such effects often rely on low-throughput microscopy or custom-built magnetic stimulation systems that provide limited control over magnetic field geometry, synchronization, or experimental automation. Here, we present an open-source magnetofluorescence imaging platform designed for bacterial plate-scale screening of MFEs in live colonies. The instrument integrates a programmable three-axis vector electromagnet, synchronized fluorescence excitation and imaging, and integrated acquisition software with per-frame metadata logging on a hardware-synchronized data acquisition card. An extensive calibration procedure enables accurate generation of arbitrary magnetic field vectors, while synchronized triggering ensures deterministic alignment between field application, illumination, and image acquisition. The system images an entire 100 mm Petri dish in a single acquisition. Typical experiments monitor hundreds of bacterial colonies simultaneously over multi-hour acquisition sequences. Control software, calibration routines, mechanical design files, and acquisition workflows are provided openly to facilitate replication. Instrument performance is demonstrated through detection of magnetic field-dependent fluorescence changes in E. coli expressing the engineered magnetosensitive fluorescent protein MagLOV2. This instrument provides a flexible and scalable platform for high-throughput magnetobiology, synthetic biology, and quantum biology experiments.

bioengineering↗

bioRxiv2020

Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules

The use of biological networks such as protein-protein interaction and transcriptional regulatory networks is becoming an integral part of biological research in the genomics era. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new "communities" of genes that carry out key cellular processes. Changes in community structure can be detected by maximizing a modularity-based score, but because biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. Here, we introduce Constrained Random Alteration of Network Edges (CRANE), a computational method that samples networks with fixed node strengths to identify a null distribution and assess the robustness of observed changes in network structure. In contrast with other approaches, such as consensus clustering or established network generative models, CRANE produces more biologically realistic results and performs better in simulations. When applied to breast and ovarian cancer networks, CRANE improves the recovery of cancer-relevant GO terms while reducing the signal from non-specific housekeeping processes. CRANE is a general tool that can be applied in tandem with a variety of stochastic community detection methods to evaluate the veracity of their results.

bioinformatics↗

bioRxiv2021

Dual Dean entrainment with volume ratio modulation for efficient co-encapsulation: Extreme single-cell indexing

The future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular system. Current approaches are limited by the Poisson statistic, requiring dilute cell suspensions and associated losses in throughput. In this contribution, we apply Dean entrainment to both cell and bead inputs, defining different volume packets to effect efficient co-encapsulation. Volume ratio scaling was explored to identify optimal conditions. This enabled the co-encapsulation of single cells with reporter beads at rates of ~1 million cells/hour, while increasing assay signal-to-noise with cell multiplet rates of ~2.5% and capturing ~70% of cells. The method, called Pirouette-seq, extends our capacity to investigate biological systems. TOC AbstractPirouette-seq involves cell and reporter bead inertial ordering for efficient co-encapsulation, achieving a throughput of 1 million cells/hour, a 2.5% multiplet rate and a 70% cell capture efficiency.

cell biology↗

bioRxiv2022

Designed allosteric protein logic

Regulation of the activity of proteins enables control of complex cellular processes. Allosteric regulation has been introduced individually into few natural proteins. Here, we present a generally applicable regulation of diverse proteins called INSRTR (inserted peptide structure regulator), based on inserting a short unstructured peptide into a solvent-accessible loop that retains protein function. Function of the target protein can be inactivated by the addition of a peptide that forms a rigid coiled-coil dimer. This platform enables the construction of ON/OFF protein switches, their regulation by small molecules, and Boolean logic functions with a rapid response in mammalian cells. INSRTR can be used to regulate a wide range of proteins, as demonstrated on ten members of protein families with diverse biological activities including enzymes, signaling mediators, DNA binders/transcriptional regulators, fluorescent protein, and antibodies regulating chimeric antigen receptor. INSRTR platform presents an extraordinary potential for regulating biological systems and applications. One sentence summaryAuthors have designed a widely applicable system to activate or inactivate function of diverse proteins or form Boolean logic gates based on formation of a coiled-coil dimer within protein loops and demonstrated its implementation on a range of 10 diverse proteins.

synthetic biology↗

bioRxiv2023

Rationally designed protein bandpass filters for controlling cellular signaling with chemical inputs

Biological mechanisms that rely on signal integration and processing are fundamental for cell function. These types of capabilities are analogous to those found in electronic circuits where individual components perform operations on input signals. In electronics, bandpass filters are crucial components to narrow frequencies within a specified range and reject frequencies outside of that range. However, no generalizable protein-based components are currently available to mimic such processes in engineered biological systems, representing an unmet need in controllable modules. Here, we propose a rational design approach to create protein-based chemically responsive bandpass filters (CBP) which pass chemical concentrations within a range and reject concentrations outside of that range, showing an OFF-ON-OFF regulatory pattern. The CBPs were designed using structure-based approaches where we created a heterodimeric construct which the assembly is triggered by low concentration of a small-molecule, and this interaction is inhibited at high concentrations of the drug, effectively creating a bandpass filter. The CBPs have a multidomain architecture where we used known drug-receptors, a computationally designed protein binder and small-molecule inhibitors. Owing to the modularity of the system, each domain of the CBPs can be rationally fine-tuned to optimize its performance, including bandwidth, maximum response, cutoff concentration and fold changes. These CBPs were used to regulate cell surface receptor signaling pathways showing the capability to control cellular activities in engineered cells.

synthetic biology↗

bioRxiv2025

Blender tissue cartography: an intuitive tool for the analysis of dynamic 3D microscopy data

Volumetric microscopy can image complex 3D tissues, but 3D image data remains difficult to visualize and quantify. Many biological systems are organized as thin, curved sheets (for example, epithelia). Tissue cartography extracts and cartographically projects these curved surfaces from volumetric images. This converts 3D into 2D image data, greatly facilitating visualization, analysis, and computational processing. Existing tools, however, demand advanced coding expertise and are limited to simple tissue geometries. Here, we present blender issue cartography (btc), an interactive add-on for the 3D editor Blender that makes tissue cartography user-friendly by a graphical interface, and handles complex biological shapes using powerful computer graphics algorithms. An accompanying Python library supports faithful 3D measurements in 2D cartographic projections and custom analysis pipelines. Time-lapse data can be batch-processed by algorithmically aligning all time points to a single key frame. We demonstrate btc on diverse and complex tissue shapes from Drosophila, stem-cell organoids, Arabidopsis, and zebrafish. btc enables quantitative cartographic analysis of complex 3D tissues, broadening access to methods previously restricted to specialists, while leveraging tools from computer graphics to unlock new capabilities.

bioinformatics↗