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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

OmicsNet: Integration of Multi-Omics Data using Path Analysis in Multilayer Networks

Integrative analysis of heterogeneous omics data is essential to obtain a comprehensive overview of otherwise fragmented information and to better understand dysregulated biological pathways leading to a specific condition. One of the major challenges in systems biology is to develop computational methods for proper integration of multi-omics datasets. We propose OmicsNet that uses a multilayer network for the integration and analysis of multi-omics data of heterogeneous types. Each layer of the multilayer network represents a certain data type: input layers correspond to genotype features and nodes in the output layer correspond to phenotypes, while intermediate layers may represent genesets or biological concepts to facilitate functional interpretation of the data. OmicsNet then calculates the highest coefficient paths in multilayer network from each genomic feature to the phenotype by computing an integrated score along the paths. These paths may indicate the most plausible signalling cascade caused by perturbed genotype features leading to a particular phenotype response. With example applications, we illustrate the potential power of OmicsNet in the functional analysis, biomarker discovery and drug response prediction in personalized medicine using multi-omics data.

bioinformatics↗
bioRxiv2018

Modeling genome-wide evolution of catalytic turnover rates: Strong epistasis shaped modern enzyme kinetics

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. To address this question, we combine genome-scale metabolic modelling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. A small number of biophysically constrained enzymes suffice to induce diminishing returns epistasis that prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. In addition, multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape is smooth and causes kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data on in vitro and in vivo turnover rates, validating our modelling approach. Our analysis thus suggests that enzyme evolution can be predicted on a genome scale and reveals the mechanisms by which evolutionary forces shape modern kcats and the whole of cell metabolism.

evolutionary biology↗
bioRxiv2018

Characterizing Building Blocks of Resource Constrained Biological Networks

Identification of motifs-recurrent and statistically significant patterns-in biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over-or under-estimates), and thus, wrong biological interpretations. In this paper, we develop a novel motif counting algorithm, Partially Overlapping MOtif Counting (POMOC), that considers capacity levels for all interactions in counting motifs. Our experiments on real and synthetic networks demonstrate that motif count using the POMOC method significantly differs from the existing motif counting approaches, and our method extends to large-scale biological networks in practical time. Our results also show that our method makes it possible to characterize the impact of different stress factors on cells organization of network. In this regard, analysis of a S. cerevisiae transcriptional regulatory network using our method shows that oxidative stress is more disruptive to organization and abundance of motifs in this network than mutations of individual genes. Our analysis also suggests that by focusing on the edges that lead to variation in motif counts, our method can be used to find important genes, and to reveal subtle topological and functional differences of the biological networks under different cell states.

bioinformatics↗
bioRxiv2018

A Multimodal Adaptive Super-Resolution and Confocal Microscope

Existing optical microscopy techniques compromise between resolution, photodamage, speed of acquisition and imaging in to deep samples. This often confines a technique to a certain biological system or process. We present a versatile imaging system which can switch between imaging modalities with sub millisecond transition times to adapt to the needs of a wide range of sample types. The imaging modalities provide the minimally invasive but low-resolution epi-fluorescence though increasing invasive but higher resolution confocal and structured illumination until the highest resolution is achieved through the most intrusive, localisation microscopy. The ability of the system to overcome the limitations of conventional single mode microscopy is demonstrated by several biological investigations. The ideas presented in this work allow researchers to move away from the model of a single imaging modality to study a specific process and instead follow those processes using the most suitable method available during the lifetime of the investigation.

biophysics↗
bioRxiv2018

Enhancing biochemical resolution by hyper-dimensional imaging microscopy

Two decades of high-paced innovation have improved the spatial resolution of fluorescence microscopy to enable molecular resolution combined with the low-invasiveness and specificity characteristic of optical microscopy. However, fluorescence microscopy also enables scientists and clinicians to map and quantitate the physico-chemical properties (e.g., analyte concentration, enzymatic activities and protein-protein interactions) of biological samples. But the optimization of the biochemical resolving power in fluorescence microscopy is not as well-developed compared to its spatial resolution. Typical techniques rely on the observation of individual properties of fluorescence thus limiting the opportunities for sensing and multiplexing. Aiming to overcome existing limitations, we demonstrate a new imaging paradigm -- Hyper Dimensional Imaging Microscopy (HDIM) -- that enables the orthogonal properties of fluorescence emission (excited state lifetime, polarization and spectra) in biological samples to be quantified simultaneously and efficiently. Therefore, akin to how multi-dimensional separation in mass-spectroscopy and multi-dimensional spectra in NMR impacted proteomics and structural biology, we envisage that HDIM spectra of unprecedented dimensionality will impact the fields of systems biology and medical diagnostics by maximizing the biochemical resolving power of fluorescence microscopy.

biophysics↗
bioRxiv2017

Collateral sensitivity to β-lactam drugs in drug-resistant tuberculosis is driven by the transcriptional wiring of BlaI operon genes

BackgroundThe evolution and spread of antimicrobial resistance is a major global public health threat. In some cases the evolution of resistance to one antimicrobial seemingly results in enhanced sensitivity to another (known as collateral sensitivity). This largely underexplored phenomenon represents a fascinating evolutionary paradigm that opens new therapeutic possibilities for patients infected with pathogens unresponsive to classical treatments. Intrinsic resistance to {beta}-lactams in Mycobacterium tuberculosis (Mtb, the causative agent of tuberculosis) has traditionally curtailed the use of these low-cost and easy-to-administer drugs for tuberculosis treatment. Recently, {beta}-lactam sensitivity has been reported in strains resistant to classical tuberculosis drug therapy, leading to a resurgence of interest in using {beta}-lactams in the clinic. Unfortunately though, there remains a limited understanding of the mechanisms driving {beta}-lactam sensitivity.\n\nMethodsWe used a novel combination of systems biology and computational approaches to characterize the molecular underpinnings of {beta}-lactam sensitivity in Mtb. We performed differential gene expression and coexpression analyses of genes previously associated with {beta}-lactam sensitivity and genes associated with resistance to classical tuberculosis drugs. Protein-protein interaction and gene regulatory network analyses were used to validate regulatory interactions between these genes, and random walks through the networks identified key mediators of these interactions. Further validation was obtained using functional in silico knockout of gene pairs.\n\nResultsOur results reveal up regulation of the key regulatory inhibitor of {beta}-lactamase production, blal, following treatment with classical drugs. Co-expression and network analyses showed direct co-regulation between genes associated with {beta}-lactam sensitivity and those associated with resistance to classical tuberculosis treatment. blal and its downstream genes (sigC and atpH) were found to be key mediators of these interactions.\n\nConclusionsOur results support the hypothesis that Mtb {beta}-lactam sensitivity is a collateral consequence of the evolution of resistance to classical tuberculosis drugs, mediated through changes to transcriptional regulation. These findings support continued exploration of {beta}-lactams for the treatment of tuberculosis, particularly for patients infected with strains resistant to classical therapies that are otherwise difficult to treat. Importantly, this work also highlights the potential of systems-level and network biology approaches to improve our understanding of collateral drug sensitivity.

microbiology↗
bioRxiv2018

Pou5f3, SoxB1 and Nanog remodel chromatin on High Nucleosome Affinity Regions at Zygotic Genome Activation

The zebrafish embryo is mostly transcriptionally quiescent during the first 10 cell cycles, until the main wave of Zygotic Genome Activation (ZGA) occurs, accompanied by fast chromatin remodeling. At ZGA, homologs of mammalian stem cell transcription factors (TFs) Pou5f3, Nanog and Sox19b bind to thousands of developmental enhancers to initiate transcription. So far, how these TFs influence chromatin dynamics at ZGA has remained unresolved. To address this question, we analyzed nucleosome positions in wild-type and Maternal-Zygotic (MZ) mutants for pou5f3 and nanog by MNase-seq. We show that Nanog, Sox19b and Pou5f3 bind to the High Nucleosome Affinity Regions (HNARs). HNARs are spanning over 600 bp, featuring high in vivo and predicted in vitro nucleosome occupancy and high predicted propeller twist DNA shape value. We suggest a two-step nucleosome destabilization-depletion model, where the same intrinsic DNA properties of HNAR promote both high nucleosome occupancy and differential binding of TFs. In the first step, already prior to ZGA, Pou5f3 and Nanog destabilize nucleosomes on HNAR centers genome-wide. In the second step, post-ZGA, Nanog, Pou5f3 and SoxB1 maintain open chromatin state on the subset of HNARs, acting synergistically. Nanog binds to the HNAR center, while the Pou5f3 stabilizes the flanks. The HNAR model will provide a useful tool for genome regulatory studies in the variety of biological systems.

developmental biology↗
bioRxiv2016

A computational method for the investigation of multistable systems and its application to genetic switches

Genetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology. Here we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour. Our results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks.

synthetic biology↗
bioRxiv2018

Three-dimensional cell geometry controls excitable membrane signaling in Dictyotelium cells

Phosphatidylinositol (3,4,5)-trisphosphate (PtdInsP3) is known to propagate as waves on the plasma membrane and is related to the membrane protrusive activities in Dictyostelium and mammalian cells. While there have been a few attempts to study the three-dimensional dynamics of these processes, most studies have focused on the dynamics extracted from single focal planes. However, the relation between the dynamics and three-dimensional cell shape remains elusive, due to the lack of signaling information about the unobserved part of the membrane. Here we show that PtdInsP3 wave dynamics are directly regulated by the three-dimensional geometry - size and shape - of the plasma membrane. By introducing an analysis method that extracts the three-dimensional spatiotemporal activities on the entire cell membrane, we show that PtdInsP3 waves self-regulate their dynamics within the confined membrane area. This leads to changes in speed, orientation and pattern evolution, following the underlying excitability of the signal transduction system. Our findings emphasize the role of the plasma membrane topology in reaction-diffusion driven biological systems and indicate its importance in other mammalian systems.

biophysics↗
bioRxiv2018

GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics

The regulation of transcription factor activity dynamically changes across cellular conditions and disease subtypes. The identification of biological modulators contributing to context-specific gene regulation is one of the challenging tasks in systems biology, which is necessary to understand and control cellular responses across different genetic backgrounds and environmental conditions. Previous approaches for identifying biological modulators from gene expression data were restricted to the capturing of a particular type of a three-way dependency among a regulator, its target gene, and a modulator; these methods cannot describe the complex regulation structure, such as when multiple regulators, their target genes, and modulators are functionally related. Here, we propose a statistical method for identifying biological modulators by capturing multivariate local dependencies, based on energy statistics, which is a class of statistics based on distances. Subsequently, our method assigns a measure of statistical significance to each candidate modulator through a permutation test. We compared our approach with that of a leading competitor for identifying modulators, and illustrated its performance through both simulations and real data analysis. Our method, entitled genome-wide identification of modulators using local energy statistical test (GIMLET), is implemented with R ([≥] 3.2.2) and is available from github (https://github.com/tshimam/GIMLET).

bioinformatics↗
bioRxiv2017

Multiplexed sgRNA Expression Allows Versatile Single Non-repetitive DNA Labeling and Endogenous Gene Regulation

The CRISPR/Cas9 system has made significant contribution to genome editing, gene regulation and chromatin studies in recent years. High-throughput and systematic investigations into the multiplexed biological systems and disease conditions require simultaneous expression and coordinated functioning of multiple sgRNAs. However, current co-transfection based sgRNA co-expression systems remain poorly efficient and virus-based transfection approaches are relatively costly and labor intensive. Here we established a vector-independent method allowing multiple sgRNA expression cassettes to be assembled in series into a single plasmid. This synthetic biology-based strategy excels in its efficiency, controllability and scalability. Taking the flexibility advantage of this all-in-one sgRNA expressing system, we further explored its applications in single non-repetitive genomic locus imaging as well as coordinated gene regulation in live cells. With its strong potency, our method will greatly facilitate the understandings in genome structure, function and dynamics, and will contribute to the systemic investigations into complex physiological and pathological conditions.

synthetic biology↗
bioRxiv2017

Dissection of MAPK signaling specificity through protein engineering in a developmental context

Mitogen-activated protein kinases (MAPK) signaling affects many processes, some of which have different outcomes in the same cell. In Arabidopsis, activation of a MAPK cascade consisting of YODA, MKK4/5 and MPK3/6 inhibits early stages of stomatal developmental, but this ability is lost at the latest stage when guard mother cells (GMCs) transition to guard cells (GCs). Rather than downregulating cascade components, stomatal precursors must have a mechanism to prevent late stage inhibition because the same MKKs and MPKs mediate other physiological responses. Here, we artificially activated the MAPK cascade using MKK7, another MKK that can modulate stomatal development, and found that inhibition of stomatal development is still possible in GMCs. This suggests that MKK4/5, but not MKK7, are specifically prevented from inhibiting stomatal development. To identify regions of MKKs responsible for cell-type specific regulation, we used a domain swap approach with MKK7 and a battery of in vitro and in vivo kinase assays. We found that N-terminal regions of MKK5 and MKK7 establish specific signal-to-output connections like they do in other organisms, but they do so in combination with previously undescribed modules in the C-terminus. One of these modules encodes the GMC-specific regulation of MKK5, that when swapped with MKK7s, allows MKK5 to mediate robust inhibition of late stomatal development. Because MKK structure is conserved across species, the identification of new MKK specificity modules and signaling rules furthers our understanding of how eukaryotes create specificity in complex biological systems.

plant biology↗
bioRxiv2018

Tradeoffs and cultural diversity

Culture is humanitys prime adaptation. Which cultural traits contribute to adaptive value at the level of cultural groups, and how they do so, is, however, unclear and debated. Here, we apply an approach from systems biology, known as Pareto task inference (ParTI), to bring a fresh perspective to these questions. ParTI considers systems that need to perform multiple tasks. No system can be optimal at all tasks at once, leading to a fundamental tradeoff. These tradeoffs result in specific patterns in multivariate trait data: data falls inside polygons in trait space, whose vertices are trait combinations that are adaptive for each of the tasks. ParTI can infer the number of adaptive tasks and their nature without need for a-priori assumptions, based on the vertices of these polygons. Here, we applied ParTI to two datasets of cultural traits, on Austronesian cultures and modern hunter-gatherers, adjusting for phylogeny and spatial diffusion effects. We find evidence that these independent datasets show a tradeoff between the same three tasks. We identify the tasks as resource defense, resource competition, and mobility/exchange. Specific combinations of cultural traits are adaptive for each of these tasks. This approach can be widely applied to any large multivariate dataset to study trade-offs in cultural evolution.

evolutionary biology↗
bioRxiv2017

mixOmics: an R package for ‘omics feature selection and multiple data integration

The advent of high throughput technologies has led to a wealth of publicly available omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a molecular signature) to explain or predict biological conditions, but mainly for a single type of omics. In addition, commonly used methods are univariate and consider each biological feature independently.\n\nWe introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a system biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of omics data available from the package.

bioinformatics↗
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↗
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↗
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↗
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↗