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bioRxiv2021

A curated collection of human vaccination response signatures

Recent advances in high-throughput experiments and systems biology approaches have resulted in hundreds of publications identifying "immune signatures". Unfortunately, these are often described within text, figures, or tables in a format not amenable to computational processing, thus severely hampering our ability to fully exploit this information. Here we present a data model to represent immune signatures, along with the Human Immunology Project Consortium (HIPC) Dashboard (www.hipc-dashboard.org), a web-enabled application to facilitate signature access and querying. The data model captures the biological response components (e.g., genes, proteins, cell types or metabolites) and metadata describing the context under which the signature was identified using standardized terms from established resources (e.g., HGNC, Protein Ontology, Cell Ontology). We have manually curated a collection of >600 immune signatures from >60 published studies profiling human vaccination responses for the current release. The system will aid in building a broader understanding of the human immune response to stimuli by enabling researchers to easily access and interrogate published immune signatures.

bioinformatics↗

bioRxiv2023

Mapping the Morphology of DNA on Carbon Nanotube-Based Sensors in Solution using X-ray Scattering Interferometry

Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes. However, it remains unclear how the ssDNA conformation on the SWCNT surface contributes to their ultimate functionality, as observations have been constrained to computational models or experiments under dehydrated states that differ substantially from the aqueous biological environments in which the nanosensors are applied. Herein, we demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two ssDNA oligomer lengths, conformational changes as a function of ionic strength, and the mechanism of dopamine sensing for a previously established ssDNA-SWCNT nanosensor, with corresponding ab initio modeling for visualization. We show that the shorter oligomer, (GT)6, adopts a highly ordered structure of stacked rings along the SWCNT axis, compared to the longer, less periodic (GT)15 wrapping. The presence of dopamine elicits a simultaneous axial elongation and radial constriction of the ssDNA closer to the SWCNT surface. Application of XSI to probe solution-phase morphologies of nanoparticle-based tools will yield insights into sensing mechanisms and inform future design strategies for polymer-functionalized SWCNT technologies.

biophysics↗

bioRxiv2024

PICASO: Profiling Integrative Communities of Aggregated Single-cell Omics data

Various single-cell modalities covering transcriptomics, epigenetic and spatio-temporal changes in health and disease phenotypes are used in an exploratory way to understand biological systems at single-cell resolution. However, the vast amount of such single-cell data is not systematically linked to existing biomedical data. Networks have previously been used to represent harmonized biomedical data. Integrating various resources of biomedical data in networks has recently received increasing attention. These aggregated networks can provide additional insight into the biology of complex human diseases at cell-type level, however, lack inclusion of single cell expression data. Here, we present the PICASO framework, which incorporates single-cell gene expression data as an additional layer to represent associations between cell types, disease phenotypes, drugs and genes. The PICASO network includes several standardized biomedical databases such as STRING, Uniprot, GeneOntology, Reactome, OmniPath and OpenTargets. Using multiple cell type-specific instances of the framework, each annotated and scored with their respective expression data, comparisons between disease states can be made by computing respective sub-networks and comparing the expression scores between conditions. Ultimately, these group-specific networks will allow the identification of relevant genes, processes and potentially druggable targets, as well as the comparison of different measured groups and thus the identification of group-specific communities and interactions.

bioinformatics↗

bioRxiv2025

scProtoTransformer: Scalable Reference Mapping Across Molecules, Cells and Donors

The rapid accumulation of single-cell data has made it possible to comprehensively characterize biological systems at molecular, cellular, and donor levels. However, scalable reference mapping across different resolutions remains a major challenge in current research. Here, we propose scProtoTransformer, a prototype-based Transformer architecture designed to achieve scalable reference mapping across molecular, cell, and donor levels. scProtoTransformer introduces a knowledge-guided prototype tokenizer that projects gene expression into biologically interpretable pathway prototypes, effectively reducing numerical batch effects while preserving biological semantic patterns. Furthermore, by leveraging knowledge distilled from the foundation model and a dynamic supervised fine-tuning strategy, scProtoTransformer achieves robust biological representations with reduced pre-training requirements. Benchmark experiments across molecular, cell, and donor-level reference mapping demonstrate that scProtoTransformer delivers competitive or even superior performance compared with state-of-the-art approaches, while providing interpretability through biologically prototypes. Together, these results establish scProtoTransformer as a unified framework for scalable reference mapping, laying the foundation for systematic understanding from genes to individuals.

bioinformatics↗

bioRxiv2026

CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI

Enzyme cascades enable complex biochemical transformations, but their optimization is resource-intensive, requiring navigation through high-dimensional parameter spaces encompassing reaction conditions, enzyme ratios, and buffer composition. Here we introduce CascadeMAP, an autonomous microfluidic platform for closed-loop optimization of enzyme cascades, integrating high-throughput microfluidics with Bayesian optimization and multi-agent AI system. We demonstrate the platform across two cascades: (i) a glycerol detection pathway monitored by fluorescence and (ii) a 1,2,3-trichloropropane degradation pathway monitored by label-free Raman spectroscopy providing orthogonal detection modalities. Bayesian optimization identified optimal conditions three times faster than Design of Experiments. Multi-agent AI system automated hypothesis generation, processing 11 GB of experimental data, pattern recognition, and insight synthesis. Operating without human intervention for 7 days, CascadeMAP processed [~]220,000 reactions across [~]7,400 different conditions. This capability establishes a generalizable framework for the autonomous optimization of enzyme cascades and metabolic pathways and accelerates the development of biocatalytic and synthetic biological systems.

biochemistry↗

bioRxiv2018

Tuning gene expression variability and multi-gene regulation by dynamic transcription factor control

Many natural transcription factors are regulated in a pulsatile fashion, but it remains unknown whether synthetic gene expression systems can benefit from such dynamic regulation. Using a fast-acting, light-responsive transcription factor in Saccharomyces cerevisiae, we show that dynamic pulsatile signals reduce cell-to-cell variability in gene expression. We then show that by encoding such signals into a single input, expression mean and variability can be precisely and independently tuned. Further, we construct a light-responsive promoter library and demonstrate how pulsatile signaling also enables graded multi-gene regulation at fixed expression ratios, despite differences in promoter dose-response characteristics. Pulsatile regulation can thus lead to highly beneficial functional behaviors in synthetic biological systems, which previously required laborious optimization of genetic parts or complex construction of synthetic gene networks.

synthetic biology↗

bioRxiv2018

A tunable dual-input system for on-demand dynamic geneexpression regulation.

Cellular systems have evolved numerous mechanisms to finely control signalling pathway activation and properly respond to changing environmental stimuli. This is underpinned by dynamic spatiotemporal patterns of gene expression. Indeed, in addition to gene transcription and translation regulation, modulation of protein levels, dynamics and localization are also essential checkpoints that govern cell functions. The introduction of tetracycline-inducible promoters has allowed gene expression control using orthogonal small molecules, facilitating rapid and reversible manipulation to study gene function in biological systems. However, differing protein stabilities means this solely transcriptional regulation is insufficient to allow precise ON-OFF dynamics, thus hindering generation of temporal profiles of protein levels seen in vivo. We developed an improved Tet-On based system augmented with conditional destabilising elements at the post-translational level that permits simultaneous control of gene expression and protein stability. Integrating these properties to control expression of a fluorescent protein in mouse Embryonic Stem Cells (mESCs), we found that adding protein stability control allows faster response times to changes in small molecules, fully tunable and enhanced dynamic range, and vastly improved microfluidic-based in-silico feedback control of gene expression. Finally, we highlight the effectiveness of our dual-input system to finely modulate levels of signalling pathway components in stem cells.

synthetic biology↗

bioRxiv2019

Killing in response to competition stabilises synthetic microbial consortia

The scale of the biological systems we can engineer is limited by the burden that host cells can bear. Division-of-labour can spread that burden across a community of cells but competitive exclusion inevitably leads to the removal of less fit community members over time. Here, we leverage amensalism and competitive exclusion to stabilise multi-species communities by engineering a strain of Escherichia coli which secretes a toxin in response to competition. We show mathematically and experimentally that such a system can produce stable populations with a composition that is tunable by easily controllable parameters. This is the first system to use competitive exclusion to create a stable two-species consortia and the first to only require the engineering of a single strain.

synthetic biology↗

bioRxiv2024

High resolution live cell imaging to define ultrastructural and dynamic features of the halotolerant yeast Debaryomyces hansenii

Although some budding yeasts have proved tractable and intensely studied models, others are more recalcitrant. Debaryomyces hansenii, an important yeast species in food and biotechnological industries with curious physiological characteristics, has proved difficult to manipulate genetically and remains poorly defined. To remedy this, we have combined live cell fluorescent dyes with high resolution imaging techniques to define the sub-cellular features of D. hansenii, such as the mitochondria, nuclei, vacuoles and the cell wall. Using these tools, we define biological processes like the cell cycle, organelle inheritance and different membrane trafficking pathways of D. hansenii for the first time. Beyond this, reagents designed to study Saccharomyces cerevisiae proteins were used to access proteomic information about D. hansenii. Finally, we optimised the use of label free holotomography to image yeast, defining the physical parameters and visualising sub-cellular features like membranes and vacuoles. Not only does this work shed light on D. hansenii but this combinatorial approach serves as a template for how other cell biological systems, which are not amenable to standard genetic procedures, can be studied.

cell biology↗

bioRxiv2019

Mycobacterium tuberculosis hijacks an evolutionary recent IFN-IL-6-CEBP axis linked to monocyte development and disease severity in humans

Monocyte counts are increased during human tuberculosis (TB) but it has not been determined whether Mycobacterium tuberculosis (Mtb) directly regulates myeloid commitment. We demonstrated that exposure to Mtb directs primary human CD34+ cells to differentiate into monocytes/macrophages. In vitro myeloid conversion did not require type I or type II IFN signaling. In contrast, Mtb enhanced IL-6 responses by CD34+ cell cultures and IL-6R neutralization inhibited myeloid differentiation and decreased mycobacterial growth in vitro. Integrated systems biology analysis of transcriptomic, proteomic and genomic data of large data sets of healthy controls and TB patients established the existence of a myeloid IL-6/IL6R/CEBP gene module associated with disease severity. Furthermore, genetic and functional analysis revealed the IL6/IL6R/CEBP gene module has undergone recent evolutionary selection, including Neanderthal introgression and human pathogen adaptation, connected to systemic monocyte counts. These results suggest Mtb co-opts an evolutionary recent IFN-IL6-CEBP feed-forward loop, increasing myeloid differentiation linked to severe TB in humans.

immunology↗

bioRxiv2026

MINT Block: multivariate integration across independent studies to identify multi-omics signatures

Integrative multi-omics analyses can identify molecular signatures spanning multiple layers of a biological system. However, signatures derived from datasets with small sample sizes may not generalise. Combining data from multiple independent studies can increase the available sample size, but may also introduce unwanted variation arising from study-specific differences. Existing approaches typically address this variation sequentially by correcting the data before downstream model fitting, which can inadvertently distort the biological signals of interest. We introduce MINT Block, a framework that integrates multi-omics data across independent studies while simultaneously accounting for study-specific unwanted variation. MINT Block supports multivariate regression and classification analyses, enabling the identification of generalisable multi-omics signatures that capture relationships between different types of molecules and predict sample phenotypes. We evaluated MINT Block using bulk and single-cell multi-omics datasets from studies of human disease and marine biology. Across these applications, we showed that MINT Block identified stable, interpretable molecular signatures and improves prediction accuracy compared with existing sequential approaches.

bioinformatics↗

bioRxiv2025

Engineering Clostridium thermocellum for production of 2,3-butanediol from cellulose

Clostridium thermocellum is a promising host for consolidated bioprocessing due to its ability to directly ferment cellulose into fuels and chemicals. However, natural product formation in this organism is limited. Here, we report engineering C. thermocellum for the production of 2,3-butanediol (23BD), a valuable industrial chemical. We functionally expressed a thermophilic 23BD pathway in this organism resulting in a 23BD titer of 19.7 mM from cellulose, representing a metabolic yield of 24%. We used a cell-free systems biology approach to identify limiting steps in the 23BD pathway, revealing that exogenous 23BD dehydrogenase (BDH) activity was essential for production, while native acetolactate synthase (ALS) and acetolactate decarboxylase (ALDC) activities were present but limiting in the parent strain. This approach also revealed redox balance limitations. We demonstrated that this improved understanding of redox balance limitations could be used to increase 23BD titer in vivo, showing that adding acetate could be used to increase 23BD yield. This work establishes a foundation for developing C. thermocellum into a robust platform for 23BD production directly from cellulose and highlights the utility of cell-free systems for guiding metabolic engineering in non-model organisms.

synthetic biology↗

bioRxiv2018

A Multi-layer, Self-aligning Hydrogel Micro-molding Process Offering a Fabrication Route to Perfusable 3D In-Vitro Microvasculature

The in-vitro fabrication of hierarchical biological systems such as human vasculature, which are made up of two or more cell types with intricate co-culture architectures, is by far one of the most complicated challenges that tissue engineers have faced. Here, we introduce a versatile method to create multi-layered, cell-laden hydrogel microstructures with coaxial geometries and heterogeneous mechanical and biological properties. The technique can be used to build in-vitro vascular networks that are fully embedded in hydrogels of physiologically realistic mechanical stiffness. Our technique produces free-standing 3D structures, eliminating rigid polymeric surfaces from the vicinity of cells and allowing layers of multiple cell types to be defined with tailored extracellular matrix (ECM) composition and stiffness, and in direct contact with each other. We demonstrate co-axial geometries with diameters ranging from 200-2000 m and layer thicknesses as small as 50-200 {micro}m in agarose- collagen (AC) composite hydrogels. Coaxial geometries with such fine feature sizes are beyond the capabilities of most bioprinting techniques. A potential application of such a structure is to simulate vascular networks in the brain with endothelial cells surrounded by multiple layers of pericytes and other glial cells. For this purpose, the composition and mechanical properties of the composite AC hydrogels have been optimized for cell viability and biological performance of endothelial and glial cell types in both 2D and 3D culture modes. Multi-layered vascular constructs with an endothelial layer surrounded by layers of glial cells have been fabricated. This prototype in-vitro model resembles vascular geometries and opens the way for complex multi-luminal blood vessels to be fabricated.

bioengineering↗

bioRxiv2026

Real-Time GPU-Accelerated Digital Heart Twin: Integrating Bidirectional Interactions Between Living Optogenetic Monolayers and Computational Simulations

Reentrant arrhythmias are life-threatening cardiac events that are difficult to study due to limited experimental control over the complex circuit dynamics. We aimed to develop a real-time digital heart twin that allows in real-time dynamic manipulation of reentrant pathways in vitro using physiologically relevant simulations. We designed a closed-feedback loop system that couples a cultured cardiac monolayer with a two-dimensional computational simulation of cardiac tissue. The simulation, based on GPU-accelerated models (e.g., cellular automata), predicts wave propagation in real-time using the Abubu.js library. Optical mapping captures monolayer activation patterns, and simulation outputs are converted into light-based stimulation via optogenetics, using LEDs and microcontrollers to depolarize cardiac tissue. Our platform is capable of accurately detecting and responding to electrical waves in real-time, enabling interactive control of reentrant circuits. The system replaces traditional fixed-delay stimulation protocols with computationally guided interventions, better mimicking physiological conduction dynamics. This digital twin provides a novel and responsive method to study reentrant arrhythmias. Its integration of optical stimulation, real-time modeling, and tissue feedback enables the construction of user-defined reentry pathways under dynamic control. By merging computational and biological systems, this work introduces a versatile experimental framework for investigating arrhythmias. The platform may inform future control and anti-arrhythmic strategies and pave the way for personalized cardiac electrophysiology studies.

bioengineering↗

bioRxiv2022

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering the full trajectory compared to few time samples. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetics changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

evolutionary biology↗

bioRxiv2023

Comparative Restriction Enzyme Analysis of Methylation (CREAM) Reveals Methylome Variability Within a Clonal In Vitro Cannabis Population

The primary focus of medicinal cannabis research is to ensure the stability of cannabis lines for consistent administration of chemically consistent products to patients. In recent years, tissue culture has emerged as a valuable technique for genetic preservation and rapid production of cannabis clones. However, there is concern that the physical and chemical conditions of the growing media can induce somaclonal variation, potentially impacting the viability and uniformity of clones. To address this concern, we developed Comparative Restriction Enzyme Analysis of Methylation (CREAM), a novel method to assess DNA methylation patterns and used it to assess a population of 78 cannabis clones maintained in tissue culture. Through bioinformatics analysis of the methylome, we successfully detected 2,272 polymorphic methylated regions among the clones. Remarkably, our results demonstrated that DNA methylation patterns were preserved across subcultures within the clonal population, allowing us to distinguish between two subsets of clonal lines used in this study. These findings significantly contribute to our understanding of the epigenetic variability within clonal lines in medicinal cannabis produced through tissue culture techniques. This knowledge is crucial for understanding the effects of tissue culture on DNA methylation and ensuring the consistency and reliability of medicinal cannabis products with therapeutic properties. Additionally, the CREAM method is a fast and affordable technology to get a first glimpse at methylation in a biological system. It offers a valuable tool for studying epigenetic variation in other plant species, thereby facilitating broader applications in plant biotechnology and crop improvement.

plant biology↗

bioRxiv2024

The CRISPRaTOR: a biomolecular circuit for Automatic Gene Regulation in Mammalian Cells with CRISPR technology.

We introduce the CRISPRaTOR, a biomolecular circuit for precise control of gene expression in mammalian cells. The CRISPRaTOR leverages the stochiometric interaction between the artificial transcription factor VPR-dCas9, and the anti-CRISPR protein AcrIIA4, enhanced with synthetic coiled-coil domains to boost their interaction, to maintain the expression of a reporter protein constant across diverse experimental conditions, including fluctuations in protein degradation rates and plasmid concentrations, by automatically adjusting its mRNA level. This capability, known as Robust Perfect Adaptation (RPA), is crucial for the stable functioning of biological systems and has wide-ranging implications for biotechnological applications. The CRISPRaTOR belongs to a class of biomolecular circuits named antithetic integral controllers, and it can be easily adapted to regulate any endogenous transcription factor thanks to the versatility of CRISPR-Cas system. Finally, we show that RPA holds also in cells genomically integrated with the CRISPRaTOR, thus paving the way for practical applications in biotechnology that require stable cell lines.

synthetic biology↗

bioRxiv2026

Bifurcation Analysis of Cancer-Immunity Cycle

Despite major advances in cancer immunotherapy, many patients still experience immune escape and disease progression. Understanding the dynamical mechanisms governing the transition between tumor control and immune escape is therefore essential for improving therapeutic strategies. Bifurcation theory and stability analysis provide a mathematical framework for explaining how gradual parameter changes can produce sudden qualitative transitions in biological systems. In the context of tumor-immune interactions, such transitions may correspond to critical thresholds separating immune-limited tumor control from immune-escape behavior. In this study, we investigate a discrete-time model of the cancer-immunity cycle applying the local stability analysis to the system equilibria based on the trace and determinant of the Jacobian matrices. The model incorporates immune suppression through a parameter representing tumor-mediated immune evasion, including mechanisms related to immune checkpoint pathways such as PD-1/PD-L1 signaling. The system exhibits a saddle-node bifurcation associated with the appearance and disappearance of nontrivial equilibria, under weak immune conditions. In addition, under strong immune conditions, the model demonstrates a distinct stability transition in which a stable spiral equilibrium (spiral sink) loses stability and becomes an unstable spiral (spiral source), resulting in the loss of stable tumor-control dynamics before equilibrium disappearance occurs. Additional mathematical analysis and numerical investigations indicate that the system does not generate stable non-equilibrium attractors such as limit cycles over the parameter ranges considered. Consequently, the stable equilibrium remains the only stable attractor in the model, emphasizing the importance of maintaining equilibrium stability for effective immune-mediated tumor suppression. Also, further parameter analyses reveal that both equilibrium existence and stability are highly sensitive near bifurcation boundaries, reflecting the delicate balance between tumor proliferation and immune activation. Overall, this work provides a mathematical framework for distinguishing equilibrium existence from effective tumor control in discrete tumor-immune systems. Beyond the specific model considered, the results highlight the importance of stability analysis in understanding immune escape and may contribute to future approaches in adaptive and personalized immunotherapy modeling.

cancer biology↗