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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2023

Cell-Free Protein Expression in Polymer Materials

While synthetic biology has advanced complex capabilities like sensing and molecule synthesis in aqueous solutions, important applications may also be pursued for biological systems in solid materials. Harsh processing conditions used to produce many synthetic materials such as plastics makes incorporation of biological functionality challenging. One technology that shows promise in circumventing these issues is cell-free protein synthesis (CFPS), where core cellular functionality is reconstituted outside the cell. CFPS enables genetic functions to be implemented without the complications of membrane transport or concerns over cellular viability or release of genetically modified organisms. Here we demonstrate that dried CFPS reactions have remarkable tolerance to heat and organic solvent exposure during the casting processes for polymer materials. We demonstrate the utility of this observation by creating plastics that have spatially patterned genetic functionality, produce antimicrobials in situ, and perform sensing reactions. The resulting materials unlock the potential to deliver DNA-programmable bio-functionality in a ubiquitous class of synthetic materials.

synthetic biology↗

bioRxiv2025

Genome modeling and design across all domains of life with Evo 2

All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation--from noncoding pathogenic mutations to clinically significant BRCA1 variants--without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

genomics↗

bioRxiv2018

Ordered arrangement of dendrites within a C. elegans sensory nerve bundle

Biological systems are organized into well-ordered structures and can evolve new patterns when perturbed. To identify principles underlying biological order, we turned to C. elegans for its simple anatomy and powerful genetics. We developed a method to quantify the arrangement of three dendrites in the main sensory nerve bundle, and found that they exhibit a stereotyped arrangement throughout larval growth. Dendrite order does not require prominent features including sensory cilia and glial junctions. In contrast, loss of the cell adhesion molecule (CAM) CDH-4/Fat-like cadherin causes dendrites to be ordered randomly, despite remaining bundled. Loss of the CAMs PTP-3/LAR or SAX-7/L1CAM causes dendrites to adopt an altered order, which becomes increasingly random as animals grow. Misexpression of SAX-7 leads to subtle but reproducible changes in dendrite order. Our results suggest that differential expression of CAMs allows dendrites to self-organize into a stereotyped arrangement that readily gives rise to new patterns when perturbed.

developmental biology↗

bioRxiv2019

Robustness of bidirectional microtubule network self-organization

Robustness of biological systems is crucial for their survival, however, for many systems its origin is an open question. Here we analyze one sub-cellular level system, the microtubule cytoskeleton. Microtubules self-organize into a network, along which cellular components are delivered to their biologically relevant locations. While individual microtubule are highly dynamic with their dynamics depends on the organism environment and genetics, network sensitivity to this dynamics would result in pathologies. Combining mathematical modelling with genetic manipulations in Drosophila, we show that the microtubule self-organization indeed does not depend on dynamics of individual microtubules, and thus is robust on the tissue level. We demonstrate the origin of this robustness via a mathematical model, suggesting this being a generic mechanism.

cell biology↗

bioRxiv2026

Automated Synthetic Cell-based Screening for Designed Proteins with Emergent Functions

Designing minimal biological systems with emergent functions such as spatiotemporal self-organization is a central goal of bottom-up synthetic biology. While computational optimization and design show promises to accelerate functional protein engineering through Design-Build-Test-Learn cycles, screening libraries for complex functions remains a major challenge. Conventional screens typically lack the spatiotemporal resolution and cell-like confinement required in bottom-up synthetic biology. Here, we present PUREdrop, an automated microfluidic platform that encapsulates and expresses protein libraries in thousands of picolitre-sized synthetic cells per construct. These droplets are sorted into a 96-well plate and analyzed by time-lapse imaging, allowing parallel quantification of expression kinetics and emergent functions. To demonstrate the platforms potential, we first screened computationally re-designed variants of the bacterial cell division protein FtsZ, identifying variants with improved bundling phenotypes and faster kinetics. We then extended our screening procedure towards general protein modulators of FtsZ and identified a combination that anchors filaments to the interface, producing a ring-like phenotype. PUREdrop bridges computational protein engineering and synthetic cell research, elevating the rational engineering of complex biological function to the next level.

synthetic biology↗

bioRxiv2019

CreLite: An Optogenetically Controlled Cre/loxP System Using Red Light

Precise manipulation of gene expression with temporal and spatial control is essential for functional studies and the determination of cell lineage relationships in complex biological systems. The Cre-loxP system is commonly used for gene manipulation at desired times and places. However, specificity is dependent on the availability of tissue- or cell-specific regulatory elements used in combination with Cre or CreER (tamoxifen-inducible). Here we present CreLite, an optogenetically-controlled Cre system using red light in developing zebrafish embryos. Cre activity is disabled by splitting Cre and fusing the inactive halves with the Arabidopsis thaliana red light-inducible binding partners, PhyB and PIF6. In addition, PhyB-PIF6 binding requires phycocyanobilin (PCB), providing an additional layer of control. Upon exposure to red light (660 nm) illumination, the PhyB-CreC and PIF6-CreN fusion proteins come together in the presence of PCB to restore Cre activity. Red-light exposure of transgenic zebrafish embryos harboring a Cre-dependent multi-color fluorescent protein reporter (ubi:zebrabow) injected with CreLite mRNAs and PCB, resulted in Cre activity as measured by the generation of multi-spectral cell labeling in various tissues, including heart, skeletal muscle and epithelium. We show that CreLite can be used for gene manipulations in whole embryos or small groups of cells at different stages of development. CreLite provides a novel optogenetic tool for precise temporal and spatial control of gene expression in zebrafish embryos that may also be useful in cell culture, ex vivo organ culture, and other animal models for developmental biology studies.

molecular biology↗

bioRxiv2019

Co-catabolism of arginine and succinate drives symbiotic nitrogen fixation

Biological nitrogen fixation emerging from the symbiosis between bacteria and crop plants holds a significant promise to increase the sustainability of agriculture. One of the biggest hurdles for the engineering of nitrogen-fixing organisms is to identify the metabolic blueprint for symbiotic nitrogen fixation. Here, we report on the CATCH-N cycle, a novel metabolic network based on co-catabolism of plant-provided arginine and succinate to drive the energy-demanding process of symbiotic nitrogen fixation in endosymbiotic rhizobia. Using systems biology, isotope labeling studies and transposon sequencing in conjunction with biochemical characterization, we uncovered highly redundant network components of the CATCH-N cycle including transaminases that interlink the co-catabolism of arginine and succinate. The CATCH-N cycle shares aspects with plant mitochondrial arginine degradation path-way. However, it uses N2 as an additional sink for reductant and therefore delivers up to 25% higher yields of nitrogen than classical arginine catabolism -- two alanines and three ammonium ions are secreted for each input of arginine and succinate. We argue that the CATCH-N cycle has evolved as part of a specific mechanism to sustain bacterial metabolism in the microoxic and acid environment of symbiosomes. In sum, our systems-level findings provide the theoretical framework and enzymatic blueprint for the rational design of plants and plant-associated organisms with new properties for improved nitrogen fixation.\n\nSignificance StatementSymbiotic bacteria assimilate nitrogen from the air and fix it into a form that can be used by plants in a process known as biological nitrogen fixation. In agricultural systems, this process is restricted mainly to legumes, yet there is considerable interest in exploring whether similar symbioses can be developed in non-legumes including cereals and other important crop plants. Here we present systems-level findings on the minimal metabolic function set for biological nitrogen fixation that provides the theoretical framework for rational engineering of novel organisms with improved nitrogen-fixing capabilities.

plant biology↗

bioRxiv2026

An Integrated Method for Profiling Lipid-Protein Interactions Using Multifunctional Lipid Probes

Cellular lipids shape health and disease through specific protein interactions, yet lipid-protein networks remain poorly defined. Despite rapid advances in functional lipid probes, the field still lacks a practical, dedicated protocol for conducting lipid-protein interaction studies. We describe detailed methods for determining lipid interactomes from cells using multifunctionalized lipid derivatives. We provide protocols that detail 1) how to treat cells with lipid derivatives and perform photochemistry to obtain lipid-protein conjugates; 2) how to perform click chemistry with a fluorophore and observe lipid-protein conjugates by in-gel fluorescence; 3) how to perform click chemistry with azide beads and prepare lipid-protein conjugates for proteomic analysis. We provide context on important parameters for each step and include guidelines for controls, as well as suggestions for troubleshooting based on common problems encountered during the preparation of this protocol. This protocol enables mapping lipid interactomes across diverse biological systems. The entire workflow from cell treatment to complete proteomic sample preparation requires [~]15 hours over four days, depending on the type of experimental readout (in-gel fluorescence or proteomics), and the usage of pause points. Practitioners are expected to be familiar with standard biochemical techniques, such as sterile sample handling and tissue culture and gel electrophoresis. Additional skills are needed for mass spectrometric analysis, and collaboration with a proteomics core facility is recommended. The described procedures uniquely enable the identification of the protein interactors (the interactome) of select lipid species, providing for a major shift in the characterization of the biological roles of lipids in cellular systems.

biochemistry↗

bioRxiv2018

Simvastatin mediates inhibition of exosome synthesis, localization, and secretion via multicomponent interventions.

Discovery of exosomes as modulator of cellular communication has added a new dimension to our understanding of biological processes. Exosomes influence the biological systems by mediating trans-communication across tissues and cells, which has important implication for health and disease. Identification of strategies for exosome modulation may pave the way towards better understanding of exosome biology and development of novel therapeutics. In absence of well-characterized modulators of exosome biogenesis, an alternative option is to target pathways generating important exosomal components. Cholesterol represents one such essential component required for exosomal biogenesis. We initiated this study to test the hypothesis that owing to its cholesterol lowering effect, simvastatin, a HMG CoA inhibitor, might be able to alter exosome formation and secretion. Using previously established protocols for detecting secreted exosomes in biological fluids, simvastatin was tested for its effect on exosome secretion under various in-vitro and in-vivo settings. Murine model of AAI was used for further validation of our findings. Utilizing aforementioned systems, we demonstrate exosome-lowering potential of simvastatin in various in-vivo and in-vitro models, of AAI and atherosclerosis. We believe that the knowledge acquired in this study holds potential for extension to other exosome dominated pathologies and model systems.

cell biology↗

bioRxiv2024

Parallel pheromone, metabolite, and lipid analyses reveal patterns associated with early life transitions and ovary activation in honey bee (Apis mellifera) queens

Eusocial insects exist in a state of reproductive conflict in which workers forgo reproduction in favor of helping relatives, typically queens, rear offspring. The honest signal hypothesis posits that queens emit pheromonal signals that convey information about their fecundity, which workers use to make decisions around investing in direct vs. indirect fitness and queen acceptance. We evaluated this idea using liquid chromatography-tandem mass spectrometry to measure honey bee queen retinue pheromone (QRP) components in relation to queen age, laying status, and likelihood of acceptance using a protocol that enables QRP to be measured concurrently with metabolomic and lipidomic analyses. We found that older mated queens (>1 month) were more readily accepted by colonies than younger queens (10-12 d), regardless of the queens prior laying status. This is despite non-laying queens having significantly smaller ovaries at the time of introduction. Older queens produced higher levels of the QRP components 9(R)-HDA, LEA, and HVA compared to younger queens, with HVA also positively correlating with ovary mass. However, these data suggest that ovary mass is not an influential fertility metric for worker decision-making; therefore, the relationship between HVA and ovary mass is merely an honest signal of a non-influential metric. Metabolomic and lipidomic analyses showed that samples cluster strongly according to queen age and mating status, but not ovary mass. These data also reveal some of the first hints of the importance of prostanoids in queen maturation, along with many other physiological changes that occur in the first month of a queens life. Significance statementInsect pheromones have historically been evaluated using gas chromatography-mass spectrometry, a technique that is incompatible with parallel lipidomics and metabolomics inquests. Here, we repurposed an established two-phase extraction protocol and optimized a liquid chromatography-tandem mass spectrometry method to acquire pheromone, metabolite, and lipid data concurrently from a single sample. We applied this technique to interrogate the honest signal hypothesis, which relates queen pheromone profiles to reproductive quality, but the approach is broadly applicable to any question in which simultaneous determination of complex pheromone profiles and lipidomics or metabolomics data is asset. Such applications may help uncover new pheromones and reveal relationships between pheromones, hormones, and physiology in diverse biological systems.

molecular biology↗

bioRxiv2026

Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning Tasks

MotivationExtracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the automation of biomedical machine learning (ML). Existing Automated ML tools lack flexibility, while Large Language Models (LLMs) struggle to consistently deliver reproducible machine learning codebases, and existing LLM Agent-powered solutions lag behind human-engineered ML models. ResultsHere, we introduce Agentomics, an autonomous LLM-powered agentic system for end-to-end ML experimentation. Given a biomedical dataset, Agentomics implements various ML modeling strategies, and produces a ready-to-use ML model. Agentomics introduces strict validation checkpoints for standard ML development steps, allowing gradual development on top of working code with defined interfaces and validated artifacts. Further, it offers native support for biomedical foundation models that can be leveraged during experimentation. The generic nature of Agentomics allows the user to create ML solutions for a large variety of datasets and use various LLMs. We evaluate Agentomics across 20 datasets from the domains of Protein Engineering, Drug Discovery, and Regulatory Genomics. When benchmarked against other agentic systems, Agentomics outperformed them in all tested domains. When benchmarked against human expert solutions, Agentomics generated novel state-of-the-art models for 11/20 established benchmark datasets. Availability and ImplementationAgentomics is implemented in Python. Source code and documentation are freely available at: https://github.com/BioGeMT/Agentomics-ML. Contactpanagiotis.alexiou@um.edu.mt

bioinformatics↗

bioRxiv2024

Quantitative Resolving Cell Fate in the Early Embryogenesis of Caenorhabditis elegans

The nematode Caenorhabditis elegans exhibits an invariant cell lineage during its development where the gene-molecular network that regulates the development is crucial for the biological process. While there are many molecular cell atlases describing the phenomena and key molecules involved in cell transformation, the underlying mechanisms from a systems biology perspective have received less attention. Based on an endogenous molecular-cellular theory that relates the molecular mechanisms to biological phenotypes, we constructed a model of core endogenous network to describe the early stages of embryonic development of the nematode. Different cell types and intermediate cell states during development from zygotes to founder cells correspond to the steady states of the network as a nonlinear stochastic dynamical system. Connections between steady states form a topological landscape that encompasses known developmental lineage trajectories. By regulating the expression of agents in the network, we quantitatively simulated the effects of the Wnt and Notch signaling pathway on cell fate transitions and predicted the possible trajectories of transdifferentiation of the AB cell across the lineage. The success of the current study may help advance our understanding of the fundamental principles of developmental biology and cell fate determination, offering an effective tool for quantitative analysis of cellular processes.

developmental biology↗

bioRxiv2022

Multi-Omic Integration by Machine Learning (MIMaL) Reveals Protein-Metabolite Connections and New Gene Functions

Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow the ability to gather information of cellular states of its components, measuring abundances of transcripts, their translation, the accumulation of proteins, lipids and metabolites. These highly complex datasets reflect the state of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through integration of these data remains challenging. Here we show that connections between omic layers can be discovered through a combination of machine learning and model interpretation. We find that model interpretation values connecting proteins to metabolites are valid experimentally and reveal also largely new connections. Further, clustering the magnitudes of protein control over all metabolites enabled prediction of gene five gene functions, each of which was validated experimentally. We accurately predicted that two uncharacterized genes in yeast modulate mitochondrial translation, YJR120W and YLD157C.We also predict and validate functions for several incompletely characterized genes, including SDH9, ISC1, and FMP52. Our work demonstrates that multi-omic analysis with machine learning (MIMaL) views multi-omic data through a new lens to reveal new insight that was not possible using existing methods.

bioinformatics↗

bioRxiv2023

Automation of Experimental Workflows for High Throughput Robotic Cultivations

Process systems engineering methods and tools have been difficult to apply in bioprocess engineering, mainly due to the high complexity of biological systems and the low reproducibility of the experiments. High throughput robotic cultivation platforms in combination with computational tools for experimental design, resource scheduling, and operation, are rapidly gaining popularity. One important contribution being the generation of data in high throughput needed to overcome this lack of data with high information content and the worrying reproducibility crisis in life sciences. In this work, directed acyclic graphs are used to represent, manage and track all experimental workflows in a robotic platform. They support data provenance and enable traceability and reproducibility of workflows in robotic facilities. The experimental workflows are automated using Apache Airflow enabling to manage all necessary steps for fed-batch cultivations, including sampling, sample transport by a mobile robot, feed additions, data collection, storage in a SQL database and model fitting. The added value of this system is demonstrated in scale-down experiments, where E. coli BL21 (DE3), producing elastin like proteins, exhibits robustness towards glucose oscillations that mimic industrial cultivation conditions.

bioengineering↗

bioRxiv2025

UNICORN: Towards Universal Cellular Expression Prediction with an Explainable Multi-Task Learning Framework

Sequence-to-function analysis is a challenging task in human genetics, especially in predicting cell-type-specific multi-omic phenotypes from biological sequences such as individualized gene expression. Here, we present UNICORN, a new method with improved prediction performances than the existing methods. UNICORN takes the embeddings from biological sequences as well as external knowledge from pre-trained foundation models as inputs and optimizes the predictor with carefully-designed loss functions. We demonstrate that UNICORN outperforms the existing methods in both gene expression prediction and multiomic phenotype prediction at the cellular level and the cell-type level, and it can also generate uncertainty scores of the predictions. Moreover, UNICORN is able to link personalized gene expression profiles with corresponding genome information. Finally, we show that UNICORN is capable of characterizing complex biological systems for different disease states or perturbations. Overall, embeddings from foundation models can facilitate the understanding of the role of biological sequences in the prediction task, and incorporating multi-omic information can enhance prediction performances.

bioinformatics↗

bioRxiv2025

Area-Based Selection of Binding Interfaces for Structural Prediction of Protein-Protein Complexes

Protein-protein interaction is a fundamental process in all biological systems, and the structural information of a protein-protein complex may provide important mechanistic details and insights into the biological processes involved. Elucidation of the rules underlying the interface specificity in protein-protein interactions is of great value for the correct prediction of the structures of a protein-protein complex. In the present study, we have developed the area-based methods for selecting near-native interfaces for protein-protein interactions. The quantitative relationship between different areas in the predicted structure of protein-protein complex and the predicted accuracy was explored using linear and nonlinear models. The predicted accuracy is characterized using the root mean square deviation (L_RMSD) of ligands. The performances of the newly-developed area-based models for selecting near-native interfaces for protein-protein binding interactions based on the partners structures at unbound or bound states are better than (or at least comparable to) those of the existing, more sophisticated method(s). The success rates of some models are above 90% (some are close to 100%), which indicates the importance and effectiveness of the area-based interface selection. The area-based methods presented in this work may shed lights on the final resolution of the interface selection problem in the field of protein-protein complex structure prediction and also on the rules of interface specificity for protein-protein interactions from a geometric area perspective. The principles developed this work also shed lights on understanding the protein-protein binding mechanisms from an area perspective. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/680849v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@fc8db3org.highwire.dtl.DTLVardef@bb0f62org.highwire.dtl.DTLVardef@ef1b84org.highwire.dtl.DTLVardef@1f79d8b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2018

LION/web: a web-based ontology enrichment tool for lipidomic data analysis

A major challenge for lipidomic analyses is the handling of the large amounts of data and the translation of results to interpret the involvement of lipids in biological systems. We built a new lipid ontology (LION) that associates over 50,000 lipid species to biophysical, chemical and cell biological features. By making use of enrichment algorithms, we used LION to develop a web-based interface (LION/web, www.lipidontology.com) that allows identification of lipid-associated terms in lipidomes. LION/web was validated by analyzing a lipidomic dataset derived from well-characterized sub-cellular fractions of RAW 264.7 macrophages. Comparison of isolated plasma membranes with the microsomal fraction showed a significant enrichment of relevant LION-terms including plasma membrane, headgroup with negative charge, glycerophosphoserines, above average bilayer thickness, and below average lateral diffusion. A second validation was performed by analyzing the membrane fluidity of CHO cells incubated with arachidonic acid. An increase in membrane fluidity was observed both experimentally by using pyrene decanoic acid and by using LION/web, showing significant enrichment of terms associated with high membrane fluidity ('above average, 'very high and 'high lateral diffusion, and 'below average transition temperature). The results demonstrate the functionality of LION/web, which is freely accessible in a platform-independent way.

biochemistry↗

bioRxiv2019

Face familiarity detection with complex synapses

Synaptic plasticity is a complex phenomenon involving multiple biochemical processes that operate on different timescales. We recently showed that this complexity can greatly increase the memory capacity of neural networks when the variables that characterize the synaptic dynamics have limited precision, as in biological systems. These types of complex synapses have been tested mostly on simple memory retrieval problems involving random and uncorrelated patterns. Here we turn to a real-world problem, face familiarity detection, and we show that also in this case it is possible to take advantage of synaptic complexity to store in memory a large number of faces that can be recognized at a later time. In particular, we show that the familiarity memory capacity of a system with complex synapses grows almost linearly with the number of the synapses and quadratically with the number of neurons. Complex synapses are superior to simple ones, which are characterized by a single variable, even when the total number of dynamical variables is matched. We further show that complex and simple synapses have distinct signatures that are testable in proposed experiments. Our results indicate that a memory system with complex synapses can be used in real-world tasks such as face familiarity detection. SignificanceThe complexity of biological synapses is probably important for enabling us to remember the past for a long time and rapidly store new memories. The advantage of complex synapses in terms of memory capacity is significant when the variables that characterize the synaptic dynamics have limited precision. This advantage has been estimated under the simplifying assumption that the memories to be stored are random and uncorrelated. Here we show that synaptic complexity is important also in a more challenging and realistic face familiarity detection task. We built a simple neural circuit that can report whether a face has been previously seen or not. This circuit incorporates complex synapses that operate on multiple timescales. The memory performance of this circuit is significantly higher than in the case in which synapses are simple, indicating that the complexity of biological synapses can be important also in real-world memory tasks.

neuroscience↗