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bioRxiv2026

On-slide Preparation of Caenorhabditis elegans Towards Quantitative, High-Resolution LA-ICP-TOF Mass Spectrometry Imaging

Metal homeostasis is a complex process wherein essential metals serving structural, catalytic and regulatory roles are acquired, trafficked, and exported once they are present in excess. Understanding changes in metal content and localization in heterogenous tissue types is critical to understanding fundamental physiology as well as a growing number of disease states. Laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS) imaging is a powerful technique for untargeted quantitation and mapping of metals in biological systems. While the nematode Caenorhabditis elegans (C. elegans) is a well-established model organism for fundamental biological research and metal-based diseases, there have been few reports of mass spectrometry-based imaging of C. elegans, mostly due to challenges preparing samples that maintain the native distribution of the elements. In this study, we developed an embedding, quantitation and imaging workflow that preserves C. elegans using 3D-printed uniform layer media application tools (ULMATs). Multiple embedding media were evaluated, and petrolatum, commercially known as Vaseline, stood out for its performance in preserving C. elegans for imaging applications. Worms were subjected to microscopy and LA-ICP-TOF-MS imaging where we achieved a 2-m spatial resolution by over-sampling laser shots during ablation. Quantitative elemental maps were obtained using a series of gelatin standards that were sectioned at a 40-m thickness to closely mimic the average tissue ablation depth of a Day 1 gravid adult C. elegans. Our results establish a new workflow for comprehensive elemental profiling of C. elegans using LA-ICP-TOF-MS, which holds high potential for future spatial metal biology research with C. elegans. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/698490v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@7c66c5org.highwire.dtl.DTLVardef@13f4934org.highwire.dtl.DTLVardef@1df3215org.highwire.dtl.DTLVardef@512fd2_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

An APP-centered molecular gateway integrates innate immunity and retinoic acid signaling to drive irreversible metamorphic commitment

The mechanisms by which environmental signals induce permanent developmental changes remain a fundamental biological problem. We investigated sea star metamorphosis, where microbial biofilms induce a total body plan reorganization. Using systems biology and functional assays, we identified a three-tiered signaling cascade: sensing, conversion, and execution. The immune adaptor MyD88 senses microbes, while MAPK proteins convert this signal into a retinoic acid developmental cue. An amyloid precursor protein (APP)-centered module acts as the irrevocable commitment gateway, stabilized by a positive feedback loop to ensure irreversibility. Remarkably, the genes driving this transition overlap with human pathways for Alzheimers disease and ADHD/Autism. By defining this ancient neuro-immune axis in an echinoderm adult body plan similar to the chordate head, our study establishes sea star metamorphosis as a model for understanding the evolutionary origins of human neurological disorders from the evolutionary developmental pathology perspective.

developmental biology↗

bioRxiv2026

Constraints for spatially and temporally precise learning in a neural circuit model of reinforcement learning

Reinforcement learning is a key means by which animals learn appropriate actions in a given context. A large body of work suggests that such learning depends on interactions between cortico-basal ganglia circuits and the midbrain dopaminergic system, yet the underlying circuit mechanisms and plasticity rules are not fully understood. Here we present a biologically plausible, multi-region neural circuit model of songbird vocal learning and map it onto the actor-critic framework of reinforcement learning. In this model, stochastic spiking activity in the cortico-basal ganglia pathway implements action selection and drives behavioral exploration, while the pathways driving midbrain dopaminergic signaling evaluate behavioral outcomes and support a reward prediction error based learning rule that approximates stochastic gradient ascent. The model achieves millisecond-scale precise learning that matches observed behavior. We further use the model to examine two fundamental constraints on biological reinforcement learning. First, dopaminergic reinforcement signals are temporally imprecise, which can cause interference between neurons controlling actions that occur close in time. Second, dopaminergic signals are spatially imprecise, which can cause interference between neurons controlling different aspects of behavior but receiving a common reinforcement signal. By jointly modeling the actor and critic components of the circuit, we show that fast updating of reward prediction is crucial for precise and efficient learning under both forms of interference, and the model predicts the experimentally observed timescale of reward prediction updating. These results suggest a circuit-level mechanism by which biological systems achieve reinforcement learning despite the temporal and spatial limitations of global neuromodulatory signals.

neuroscience↗

bioRxiv2025

An RNA ligase shapes transcriptional profiles, neural function, and behaviour in the developing larval zebrafish

RNA ligases are essential for the repair, splicing, and editing of RNA across various biological systems. Recently, a new enzyme that catalyses 5-3 RNA ligation - RNA ligase 1 (Rlig1) - was identified in vitro. However, the in vivo biological functions of Rlig1 have remained elusive. Here, we reveal the role of Rlig1 during vertebrate development using embryonic and larval zebrafish as a model system. We found that rlig1 mRNA is maternally deposited and present ubiquitously during early embryogenesis, whereas at larval stages it localises to the brain and eyes. Interestingly, CRISPR/Cas9-generated rlig1 knockout zebrafish exhibited no overt morphological abnormalities, but showed reduced behavioural responsiveness to visual stimuli along with massively perturbed transcriptomes and widespread dysregulation of core metabolic and translational pathways. Brain-wide calcium imaging in rlig1 knockout larvae revealed decreased neuronal activity in key regions for visual processing, consistent with the observed behavioural defects. Together, our findings identify a role for Rlig1 in maintaining the integrity and function of the nervous system and uncover a new link between neuronal RNA processing, development, and sensory-motor computation.

developmental biology↗

bioRxiv2018

Panoptic vDISCO imaging reveals neuronal connectivity, remote trauma effects and meningeal vessels in intact transparent mice

Analysis of entire transparent rodent bodies could provide holistic information on biological systems in health and disease. However, it has been challenging to reliably image and quantify signal from endogenously expressed fluorescent proteins in large cleared mouse bodies due to the low signal contrast. Here, we devised a pressure driven, nanobody based whole-body immunolabeling technology to enhance the signal of fluorescent proteins by up to two orders of magnitude. This allowed us to image subcellular details in transparent mouse bodies through bones and highly autofluorescent tissues, and perform quantifications. We visualized for the first-time whole-body neuronal connectivity of an entire adult mouse and discovered that brain trauma induces degeneration of peripheral axons. We also imaged meningeal lymphatic vessels and immune cells through the intact skull and vertebra in naive animals and trauma models. Thus, our new approach can provide an unbiased holistic view of biological events affecting the nervous system and the rest of the body.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC=\"FIGDIR/small/374785_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (62K):\norg.highwire.dtl.DTLVardef@8fad72org.highwire.dtl.DTLVardef@c41ed5org.highwire.dtl.DTLVardef@1c470eaorg.highwire.dtl.DTLVardef@1d144cb_HPS_FORMAT_FIGEXP M_FIG C_FIG Note: Manuscript videos are available at Supplementary material section of BioRxiv and at the following link http://vdisco.isd-muc.de/

neuroscience↗

bioRxiv2025

Coarse-graining reveals collective predictive information in a sensory population

Biological systems perform complex computations using hundreds of individual actors, but they do so efficiently and in a way that can be read out and interpreted by other biological networks. Coarsegraining may allow for key collective features to be effectively and efficiently communicated. In the brain, early sensory systems perform prediction, which can compensate for lags in neural processing. This computation is collective, meaning it relies upon interactions between many neurons, and operates in complex, dynamic natural environments. Taking these two facets of biological complexity together, we search for maximally-predictive collective variables in large groups of retinal ganglion cells responding to dynamic natural visual scenes. To find collective variables that best capture predictive computations in the neurons, we apply a tractable, approximate implementation of the information bottleneck method to neural data. We infer a lower-dimensional representation that is maximally informative about the future neural activity. We observe scaling relationships between this mutual information estimate, neural subset size, and information decay timescale. Further, the structure of collective modes changes for predicting at short versus longer timescales.

neuroscience↗

bioRxiv2017

Lysinibacillus fusiformis M5 induces increased complexity in Bacillus subtilis 168 colony biofilms via hypoxanthine

In recent years, biofilms have become a central subject of research in the fields of microbiology, medicine, agriculture, or systems biology amongst others. The sociomicrobiology of multispecies biofilms, however, is still poorly understood. Here, we report a screening system that allowed us to identify soil bacteria, which induce architectural changes in biofilm colonies when cocultured with B. subtilis. We identified the soil bacterium Lysinibacillus fusiformis M5 as inducer of wrinkle-formation in B. subtilis colonies mediated by a diffusible signaling molecule. This compound was isolated by bioassay-guided chromatographic fractionation. The elicitor was identified to be the purine hypoxanthine using mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy. We show that the induction of wrinkle formation by hypoxanthine is not dependent on signal recognition by the histidine kinases KinA, KinB, KinC, and KinD, which are generally involved in phosphorylation of the master regulator Spo0A. Likewise, we show that hypoxanthine signaling does not induce the expression of biofilm-matrix related operons epsA-O and tasA-sipW-tapA. Finally, we demonstrate that the purine permease PbuO, but not PbuG, is necessary for hypoxanthine to induce an increase in wrinkle formation of B. subtilis biofilm colonies. Our results suggest that hypoxanthine-stimulated wrinkle development is not due to a direct induction of biofilm-related gene expression, but rather caused by the excess of hypoxanthine within B. subtilis cells, which may lead to cell stress and death.\n\nIMPORTANCEBiofilms are a bacterial lifestyle with high relevance regarding diverse human activities. Biofilms can be favorable, for instance in crop protection. In nature, biofilms are commonly found as multispecies communities displaying complex social behaviors and characteristics. The study of interspecies interactions will thus lead to a better understanding and use of biofilms as they occur outside laboratory conditions. Here, we present a screening method suitable for the identification of multispecies interactions, and showcase L. fusiformis as a soil bacterium that is able to live alongside B. subtilis and modify the architecture of its biofilms.

microbiology↗

bioRxiv2022

Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

Probing epigenomic marks such as histone modifications at a single cell level in thousands of cells has been recently enabled by technologies such as scCUT&Tag. Here we developed a multimodal and optimized iteration of scCUT&Tag called nano-CT (for nano-CUT&Tag) that allows simultaneous probing of three epigenomic modalities at single-cell resolution, using nanobody-Tn5 fusion proteins. nano-CT is compatible with starting materials as low as 25 000 cells and has significantly higher resolution than scCUT&Tag, with a 16-fold increase in the number of fragments per cells. We used nano-CT to simultaneously profile chromatin accessibility, H3K27ac and H3K27me3 in a complex tissue - juvenile mouse brain. The obtained multimodal dataset allowed for discrimination of more cell types/states that scCUT&Tag, and inference of chromatin velocity between ATAC and H3K27ac in the oligodendrocyte (OL) lineage. In addition, we used nano-CT to deconvolute H3K27me3 repressive states and infer two sequential waves of H3K27me3 repression at distinct gene modules during OL lineage progression. Thus, given its high resolution, versatility, and multimodal features, nano-CT allows unique insights in epigenetic landscapes in different biological systems at single cell level.

molecular biology↗

bioRxiv2022

Cytoflow: A Python Toolbox for Flow Cytometry

Cytoflow is a free, open-source flow cytometry toolbox that enables quantitative and reproducible analyses of flow cytometry experiments. Comprised of a set of well-documented Python modules wrapped by a graphical user interface, Cytoflow allows both programmers and bench scientists to apply modern data analysis methods (including machine learning) to high-dimensional flow data sets. Modern analyses may also lead to new insight about the biological systems that are studied with this powerful technique.

cell biology↗

bioRxiv2023

New genome scale network modeling and mining workflow for detecting metabolic changes induced by exposure to chemicals

BackgroundThe growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. ResultsTo address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated conditions-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes (PHH) transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone) or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. ConclusionThe proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. All code is freely available as well as data to reproduce results.

bioinformatics↗

bioRxiv2024

Linking Molecular Mechanisms to their Evolutionary Consequences: a primer

A major obstacle to predictive understanding of evolution stems from the complexity of biological systems, which prevents detailed characterization of key evolutionary properties. Here, we highlight some of the major sources of complexity that arise when relating molecular mechanisms to their evolutionary consequences and ask whether accounting for every mechanistic detail is important to accurately predict evolutionary outcomes. To do this, we developed a mechanistic model of a bacterial promoter regulated by two proteins, allowing us to connect any promoter genotype to six phenotypes that capture the dynamics of gene expression following an environmental switch. Accounting for the mechanisms that govern how this system works enabled us to provide an in-depth picture of how regulated bacterial promoters might evolve. More importantly, we used the model to explore which factors that contribute to the complexity of this system are essential for understanding its evolution, and which can be simplified without information loss. We found that several key evolutionary properties - the distribution of phenotypic and fitness effects of mutations, the evolutionary trajectories during selection for regulation - can be accurately captured without accounting for all, or even most, parameters of the system. Our findings point to the need for a mechanistic approach to studying evolution, as it enables tackling biological complexity and in doing so improves the ability to predict evolutionary outcomes.

evolutionary biology↗

bioRxiv2024

CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants

Genomic selection (GS) leverages genome-wide markers and phenotypes to predict breeding values, with its effectiveness largely dependent on the accuracy of genomic prediction (GP) models. However, GP methods often struggle to capture inter-individual variability and are limited by the curse of dimensionality, where the number of SNPs far exceeds the sample size. To address these challenges, we present CLCNet (Contrastive Learning and Chromosome-aware Network), a novel deep learning framework that integrates contrastive learning and chromosome-aware feature modeling. CLCNet comprises two key components: (i) a contrastive learning module that enhances the models ability to capture fine-grained, genotype-dependent phenotypic differences among individuals, and (ii) a chromosome-aware module that captures structured feature selection at both chromosome and genome levels, thereby distilling the most informative SNPs. We evaluated CLCNet across four crop species, covering ten agronomically important traits, and compared it with a diverse set of classical linear, machine learning, and deep learning models. CLCNet achieved superior prediction performance, with statistically significant improvements in Pearson correlation coefficient (PCC), ranging from 0.34% to 12.19% over baseline, together with reduced mean squared error (MSE). Performance gains were more pronounced for traits with moderate linkage disequilibrium (LD; r2 = 0.21-0.36) and high heritability (h2 > 0.66), such as those in maize, rapeseed, and soybean. For cotton traits characterized by high LD (r2 = 0.74) and lower heritability (h2 < 0.50), CLCNet maintained robust performance without degradation. Overall, these results demonstrate that CLCNet is an effective framework for improving genomic prediction accuracy and holds strong potential for practical applications in plant breeding. Short abstractCLCNet is a novel deep learning framework for genomic prediction that integrates contrastive learning with chromosome-aware feature selection. By jointly modeling inter-individual genotype-phenotype variation and chromosomal genomic structure, CLCNet improves prediction accuracy under high-dimensional, low-sample-size conditions. Across four crop species and ten agronomic traits, CLCNet consistently outperformed classical statistical, machine learning, and existing deep learning models. The framework also identified biologically relevant SNPs and candidate genes, demonstrating its potential for practical applications in genomic selection and computational plant breeding. Key pointsO_LIWe propose CLCNet, a multi-task deep learning framework that integrates contrastive learning with chromosome-aware feature selection for genomic prediction, under high-dimensional, low-sample-size conditions. C_LIO_LIThe chromosome-aware module explicitly exploits genomic structural information to select representative and informative SNPs across chromosomes. C_LIO_LIContrastive learning improves model robustness by stabilizing representation learning and reducing the influence of random effects across samples. C_LIO_LIBy complementing GWAS analyses, CLCNet provides additional insights into genotype-phenotype relationships with potential relevance for gene discovery. C_LI Biographical NoteJiangwei Huang is a PhD candidate at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research focuses on genomic prediction, deep learning, and computational plant breeding. Zhihan Yang is a PhD candidate at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. Her research interests include genomic prediction and bioinformatics. Rongcheng Han is an associate professor at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research focuses on bioinformatics and plant phenomics. Yuqiang Jiang is a professor at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research interests include plant genomics, plant phenomics and genetic improvement. Organization descriptionThe Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, is a leading research institute focusing on genetics, genomics, molecular breeding, bioinformatics, and systems biology in plants and animals.

genomics↗

bioRxiv2018

csuWGCNA: a combination of signed and unsigned WGCNA to capture negative correlations

Network analysis helps us to understand how genes jointly affect biological functions. Weighted Gene Co-expression Network Analysis (WGCNA) is a frequently used method to build gene co-expression networks. WGCNA may be calculated with signed or unsigned correlations, with both methods having strengths and weaknesses, but both methods fail to capture weak and moderate negative correlations, which may be important in gene regulation. Combining the advantages and removing the disadvantages of both methods in one analysis would be desirable. In this study, we present a combination of signed and unsigned WGCNA (csuWGCNA), which combines the signed and unsigned methods and improves the detection of negative correlations. We applied csuWGCNA in 14 simulated datasets, six ground truth datasets and two large human brain datasets. Multiple metrics were used to evaluate csuWGCNA at gene pair and gene module levels. We found that csuWGCNA provides robust module detection and captures more negative correlations than the other methods, and is especially useful for non-coding RNA such as microRNA (miRNA) and long non-coding RNA (lncRNA). csuWGCNA enables detection of more informative modules with biological functions than signed or unsigned WGCNA, which enables discovery of novel gene regulation and helps interpretations in systems biology.

bioinformatics↗

bioRxiv2023

High-throughput, microscopy-based screening, and quantification of genetic elements

Synthetic biology relies on the screening and quantification of genetic components to assemble sophisticated gene circuits with specific functions. Microscopy is powerful tool for characterizing complex cellular phenotypes with increasing spatial and temporal resolution to library screening of genetic elements. Microscopy-based assays are powerful tools for characterizing cellular phenotypes with spatial and temporal resolution, and can be applied to large-scale samples for library screening of genetic elements. However, strategies for high-throughput microscopy experiments remain limited. Here, we present a high-throughput, microscopy-based platform that can simultaneously complete the preparation of an 8x12-well agarose pads plate, allowing for the screening of 96 independent strains or experimental conditions in a single experiment. Using this platform, we screened a library of natural intrinsic promoters from Pseudomonas aeruginosa and identified a small subset of robust promoters that drives stable levels of gene expression under varying growth conditions. Additionally, the platform allowed for single-cell measurement of genetic elements over time, enabling the identification of complex and dynamic phenotypes to map genotype in high-throughput. We expected that the platform could be employed to accelerate the identification and characterization of genetic elements in various biological systems, as well as to understand the relationship between cellular phenotypes and internal states, including genotypes and gene expression programs. Impact statementThe high-throughput microscopy-based platform, presented in this study, enables efficient screening of 96 independent strains or experimental conditions in a single experiment, facilitating the rapid identification of genetic elements with desirable features, thereby advancing synthetic biology. The robust promoters identified through this platform, which provide predictable and consistent control over gene expression under varying growth conditions, can be utilized as reliable tools to regulate gene expression in various biological applications, including synthetic biology, metabolic engineering, and gene therapy, where consistent system performance is required.

molecular biology↗

bioRxiv2017

Watching the clock for 25 years in FlyClockbase: Variability in circadian clocks of Drosophila melanogaster as uncovered by biological model curation

Biological model curation provides new insights by integrating biological knowledge-fragments, assessing their uncertainty, and analyzing the reliability of potential interpretations. Here we integrate published results about circadian clocks in Drosophila melanogaster while exploring economies of scale in biological model curation. Clocks govern rhythms of gene-expression that impact fitness, health, cancer, memory, mental functions, and more. Human clock insights have been repeatedly pioneered in flies. Flies simplify investigating complex gene regulatory networks, which express proteins cyclically using environmentally entrained interlocking feedback loops that act as clocks. Simulations could simplify research further. We found that very few computational models test their quality directly against experimentally observed time series scattered in the literature. We designed FlyClockbase for integrating such scattered data to enable robust efficient access for biologists and modelers. To this end we have been defining data structures that simplify the construction and maintenance of Versioned Biological Information Resources (VBIRs) that prioritize simplicity, openness, and therefore maintainability. We aim to simplify the preservation of more raw data and relevant annotations from experiments in order to multiply the long-term value of wet-lab datasets for modelers interested in meta-analyses, parameter estimates, and hypothesis testing. Currently FlyClockbase contains over 400 wildtype time series of core circadian components systematically curated from 86 studies published between 1990 and 2015. Using FlyClockbase, we show that PERIOD protein amount peak time variance unexpectedly exceeds that of TIMELESS. We hypothesize that PERIODs exceedingly more complex phosphorylation rules are responsible. Variances of daily event times are easily confounded by errors. We improved result reliability by a human error analysis of our data handling; this revealed significance-degrading outliers, possibly violating a presumed absence of wildtype heterogeneity or lab evolution. Separate analyses revealed elevated stochasticity in PCR-based peak time variances; yet our reported core difference in peak time variances appears robust. Our study demonstrates how biological model curation enhances the understanding of circadian clocks. It also highlights diverse broader challenges that are likely to become recurrent themes if models in molecular systems biology aim to integrate all relevant knowledge. We developed a trans-disciplinary workflow, which demonstrates the importance of developing compilers for VBIRs with a more biology-friendly logic that is likely to greatly simplify biological model curation. Curation-limited grand challenges, including personalizing medicine, critically depend on such progress if they are indeed to integrate all relevant knowledge.\n\nGeneral Article SummaryCircadian clocks impact health and fitness by controlling daily rhythms of gene-expression through complex gene-regulatory networks. Deciphering how they work requires experimentally tracking changes in amounts of clock components. We designed FlyClockbase to simplify data-access for biologists and modelers, curating over 400 time series observed in wildtype fruit flies from 25 years of clock research. Substantial biological model curation was essential for identifying differences in peak time variance of the clock-proteins PERIOD and TIMELESS, which probably stem from differences in phosphorylation-network complexity.\n\nWe repeatedly encountered systemic limitations of contemporary data analysis strategies in our work on circadian clocks. Thus, we used it as an opportunity for composing a panoramic view of the broader challenges in biological model curation, which are likely to increase as biologists aim to integrate all existing expertise in order to address diverse grand challenges. We developed and tested a trans-disciplinary research workflow, which enables biologists and compiler-architects to define biology-friendly compilers for efficiently constructing and maintaining Versioned Biological Information Resources (VBIRs). We report insights gleaned from our practical clock research that are essential for defining a VBIRs infrastructure, which improves the efficiency of biological model curation to the point where it can be democratized.\n\nStatement of data availabilityStabilizing Versioned Variant of this file: QQv1r4_2017m07d14_Lion\n\nBefore final publication FlyClockbase will be at https://github.com/FlyClockbase For review purposes FlyClockbase QQv1r4 will be provided as a zip-archive in the uploaded Supplemental Material; it is also available upon request from L. Loewe.\n\nAbbreviationsTable 1: Molecular core clock components\n\nTable 2: Concepts for organizing FlyClockbase\n\nSupplemental MaterialAppendix: Supplemental Text and Tables (32 pages included in this file, QQv1v4)\n\nSupplemental Statistical Analysis (87 pages not included in this file, QQv1v4)\n\nR-Script zip file (>12K lines not included in this file, QQv1v4)\n\nFlyClockbase zip file (available upon request, QQv1v4)

genetics↗

bioRxiv2023

Mechanism of branching morphogenesis inspired by diatom silica formation

The silica-based cell walls of diatoms are prime examples of genetically controlled, species-specific mineral architectures. The physical principles underlying morphogenesis of their hierarchically structured silica patterns are not understood, yet such insight could indicate novel routes towards synthesizing functional inorganic materials. Recent advances in imaging nascent diatom silica allow rationalizing possible mechanisms of their pattern formation. Here, we combine theory and experiments on the model diatom Thalassiosira pseudonana to put forward a minimal model of branched rib patterns - a fundamental feature of the silica cell wall. We quantitatively recapitulate the time-course of rib pattern morphogenesis by accounting for silica biochemistry with autocatalytic formation of diffusible silica precursors followed by conversion into solid silica. We propose that silica deposition releases an inhibitor that slows down up-stream precursor conversion, thereby implementing a self-replicating reaction-diffusion system featuring a non-classical Turing mechanism. The proposed mechanism highlights the role of geometrical cues for guided self-organization, rationalizing the instructive role for the single initial pattern seed known as primary silicification site. The mechanism of branching morphogenesis that we characterize here is possibly generic and may apply also in other biological systems. Significance statementThe formation of minerals by living organisms is a widespread biological phenomenon occurring throughout the evolutionary tree-of-life. The silica-based cell walls of diatom microalgae are impressive examples featuring intricate architectures and outstanding materials properties that still defy their reconstitution in vitro. Here, we developed a minimal mathematical model that explains the formation of branched patterns of silica ribs, providing unprecedented understanding of basic physico-chemical processes capable of guiding silica morphogenesis in diatoms. The generic mechanism of branching morphogenesis identified here could provide recipes for bottom-up synthesis of mineral-nanowire networks for technological applications. Moreover, similar mechanisms may apply in the biological morphogenesis of other branched structures, like corals, bacterial colonies, tracheal networks, fungal plexuses, or the vascular system.

biophysics↗

bioRxiv2020

Scalable Surrogate Deconvolution for Identification of Partially-Observable Systems and Brain Modeling

For many biophysical systems, direct measurement of all state-variables, in - vivo is not-feasible. Thus, a key challenge in biological modeling and signal processing is to reconstruct the activity and structure of interesting biological systems from indirect measurements. These measurements are often generated by approximately linear time-invariant (LTI) dynamical interactions with the hidden system and may therefore be described as a convolution of hidden state-variables with an unknown kernel. In the current work, we present an approach termed surrogate deconvolution, to directly identify such coupled systems (i.e. parameterize models). Surrogate deconvolution reframes certain nonlinear partially-observable identification problems, which are common in neuroscience/biology, as analytical objectives that are compatible with almost any user-chosen optimization procedure. We show that the proposed technique is highly scalable, low in computational complexity, and performs competitively with the current gold-standard in partially-observable system estimation: the joint Kalman Filters (Unscented and Extended). We show the benefits of surrogate deconvolution for model identification when applied to simulations of the Local Field Potential and blood oxygen level dependent (BOLD) signal. Lastly, we demonstrate the empirical stability of Hemodynamic Response Function (HRF) kernel estimates for Mesoscale Individualized NeuroDynamic (MINDy) models of individual human brains. The recovered HRF parameters demonstrate reliable individual variation as well as a stereotyped spatial distribution, on average. These results demonstrate that surrogate deconvolution promises to enhance brain-modeling approaches by simultaneously and rapidly fitting large-scale models of brain networks and the physiological processes which generate neuroscientific measurements (e.g. hemodynamics for BOLD fMRI).

neuroscience↗

bioRxiv2021

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two applications showing how one can deconvolute the predictions made by RPath as well as predict novel targets. Finally, we have made the source code and data publicly available at https://github.com/enveda/RPath.

bioinformatics↗