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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 397 records · Page 22Linked to original sources

bioRxiv2018

Common behavioral mechanisms underlie C. elegans aggregation and swarming

In complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While such collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming--a dynamic phenotype only observed at longer timescales. Using fluorescent multi-worm tracking, we quantify aggregation behavior in terms of individual dynamics and population-level statistics. Based on our quantification, we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules that give rise to aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation. Hence, mesoscopic C. elegans uses mechanisms familiar from microscopic systems for aggregation, but implemented via more complex behaviors characteristic of macroscopic organisms.

biophysics↗

bioRxiv2018

Successful optimization of reconstruction parameters in structured illumination microscopy - a practical guide

The impact of the different reconstruction parameters in super-resolution structured illumination microscopy (SIM) onto artifacts is carefully analyzed. They comprise the Wiener filter parameter, an apodization function, zero-frequency suppression and modifications of the optical transfer function. A detailed investigation of the reconstructed image spectrum is concluded to be suitable for identifying artifacts. For this purpose, two samples, an artificial test slide and a more realistic biological system, were used to characterize the artifact classes and their correlation with the image spectra as well as the reconstruction parameters. In addition, a guideline for efficient parameter optimization is suggested and the implementation of the parameters in selected up-to-date processing packages (proprietary and open-source) is depicted.

bioinformatics↗

bioRxiv2018

Learning Gene Networks Underlying Clinical Phenotypes Using SNP Perturbations

Recent technologies are generating an abundance of genome sequence data and molecular and clinical phenotype data, providing an opportunity to understand the genetic architecture and molecular mechanisms underlying diseases. Previous approaches have largely focused on the co-localization of single-nucleotide polymorphisms (SNPs) associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus (eQTL) mapping, and thus have provided only limited capabilities for uncovering the molecular mechanisms behind the SNPs influencing clinical phenotypes. Here we aim to extract rich information on the functional role of trait-perturbing SNPs that goes far beyond this simple co-localization. We introduce a computational framework called Perturb-Net for learning the gene network that modulates the influence of SNPs on phenotypes, using SNPs as naturally occurring perturbation of a biological system. Perturb-Net uses a probabilistic graphical model to directly model both the cascade of perturbation from SNPs to the gene network to the phenotype network and the network at each layer of molecular and clinical phenotypes. Perturb-Net learns the entire model by solving a single optimization problem with an extremely fast algorithm that can analyze human genome-wide data within a few hours. In our analysis of asthma data, for a locus that was previously implicated in asthma susceptibility but for which little is known about the molecular mechanism underlying the association, Perturb-Net revealed the gene network modules that mediate the influence of the SNP on asthma phenotypes. Many genes in this network module were well supported in the literature as asthma-related.

bioinformatics↗

bioRxiv2018

KPGminer: A tool for retrieving pathway genes from KEGG pathway database

Pathway analysis is a very important aspect in computational systems biology as it serves as a crucial component in many computational pipelines. KEGG is one of the prominent databases that host pathway information associated with various organisms. In any pathway analysis pipelines, it is also important to collect and organize the pathway constituent genes for which a tool to automatically retrieve that would be a useful one to the practitioners. In this article, I present KPGminer, a tool that retrieves the constituent genes in KEGG pathways for various organisms and organizes that information suitable for many downstream pathway analysis pipelines. We exploited several KEGG web services using REST APIs, particularly GET and LIST methods to request for the information retrieval which is available for developers. Moreover, KPGminer can operate both for a particular pathway (single mode) or multiple pathways (batch mode). Next, we designed a crawler to extract necessary information from the response and generated outputs accordingly. KPGminer brings several key features including organism-specific and pathway-specific extraction of pathway genes from KEGG and always up-to-date information. Thus, we hope KPGminer can be a useful and effective tool to make downstream pathway analysis easier and faster. KPGminer is freely available for download from https://sourceforge.net/projects/kpgminer/.

bioinformatics↗

bioRxiv2018

Collective search with finite perception: transient dynamics and search efficiency

Motile organisms often use finite spatial perception of their surroundings to navigate and search their habitats. Yet standard models of search are usually based on purely local sensory information. To model how a finite perceptual horizon affects ecological search, we propose a framework for optimal navigation that combines concepts from random walks and optimal control theory. We show that, while local strategies are optimal on asymptotically long and short search times, finite perception yields faster convergence and increased search efficiency over transient time scales relevant in biological systems. The benefit of the finite horizon can be maintained by the searchers tuning their response sensitivity to the length scale of the stimulant in the environment, and is enhanced when the agents interact as a result of increased consensus within subpopulations. Our framework sheds light on the role of spatial perception and transients in search movement and collective sensing of the environment.

biophysics↗

bioRxiv2018

Eco-evolutionary processes underlying early warning signals of population declines

O_LIEnvironmental change can impact the stability of populations and can cause rapid declines in abundance. Abundance-based warning signals have been proposed to predict such declines, but these have been shown to have limited success, leading to the development of warning signals based on the distribution of fitness-related traits such as body size. C_LIO_LIThe dynamics of such traits in response to external environmental perturbations are controlled by a range of underlying factors such as reproductive rate, genetic variation, and plasticity. However, it remains unknown how such ecological and evolutionary factors affect the stability landscape of populations and the detectability of abundance and trait-based warning signals of population decline. C_LIO_LIHere, we apply a trait-based demographic approach and investigate both trait and population dynamics in response to gradual changes in the environment. We explore a range of ecological and evolutionary constraints under which the stability of a population may be affected. C_LIO_LIWe show both analytically and with model-based simulations that strength of abundance-based early warning signals is significantly affected by ecological and evolutionary factors. C_LIO_LIFinally, we show that a unified approach, combining trait- and abundance-based information, significantly improves our ability to predict population declines. Our study suggests that the inclusion of trait dynamic information alongside generic warning signals should provide more accurate forecasts of the future state of biological systems. C_LI

ecology↗

bioRxiv2018

Community structure of phototrophic co-cultures from extreme environments

Cyanobacteria are found in most illuminated environments and are key players in global carbon and nitrogen cycling. Although significant efforts have been made to advance our understanding of this important phylum, still little is known about how members of the cyanobacteria affect and respond to changes in complex biological systems. This lack of knowledge is in part due to our dependence on pure cultures when determining the metabolism and function of a microorganism. In the work presented here we took advantage of the Culture Collection of Microorganisms from Extreme Environments (CCMEE), a collection of more than 1,000 publicly available photosynthetic co-cultures now maintained at the Pacific Northwest National Laboratory. To highlight some of their scientific potential, we selected 26 of these photosynthetic co-cultures from the CCMEE for 16S rRNA gene sequencing. We assessed if samples readily available from the CCMEE could be used to generate new insights into the role of microbial communities in global and local carbon and nitrogen cycling. Results from this work support the existing notion that culture depositories in general hold the potential to advance fundamental and applied research. If collections of co-cultures can be used to infer roles of the individual organisms remains to be seen and requires further investigation.

microbiology↗

bioRxiv2018

Measuring the importance of vertices in the weighted human disease network

Many human genetic disorders and diseases are known to be related to each other through frequently observed co-occurrences. Studying the correlations among multiple diseases provides an important avenue to better understand the common genetic background of diseases and to help develop new drugs that can treat multiple diseases. Meanwhile, network science has seen increasing applications on modeling complex biological systems, and can be a powerful tool to elucidate the correlations of multiple human diseases. In this article, known disease-gene associations were represented using a weighted bipartite network. We extracted a weighted human diseases network from such a bipartite network to show the correlations of diseases. Subsequently, we proposed a new centrality measurement for the weighted human disease network in order to quantify the importance of diseases. Using our centrality measurement to quantify the importance of vertices in the weighted human disease network, we were able to find a set of most central diseases. By investigating the 30 top diseases and their most correlated neighbors in the network, we identified disease linkages including known disease pairs and novel findings. Our research helps better understand the common genetic origin of human diseases and suggests top diseases that likely induce other related diseases.

bioinformatics↗

bioRxiv2018

Fast, volumetric live-cell imaging using high-resolution light-field microscopy

Visualizing diverse anatomical and functional traits that span many spatial scales with high spatio-temporal resolution provides insights into the fundamentals of living organisms. Light-field microscopy (LFM) has recently emerged as a scanning-free, scalable method that allows for high-speed, volumetric functional brain imaging. Given those promising applications at the tissue level, at its other extreme, this highly-scalable approach holds great potential for observing structures and dynamics in single-cell specimens. However, the challenge remains for current LFM to achieve subcellular level, near-diffraction-limited 3D spatial resolution. Here, we report high-resolution LFM (HR-LFM) for live-cell imaging with a resolution of 300-700 nm in all three dimensions, an imaging depth of several micrometers, and a volume acquisition time of milliseconds. We demonstrate the technique by imaging various cellular dynamics and structures and tracking single particles. The method may advance LFM as a particularly useful tool for understanding biological systems at multiple spatio-temporal levels.

bioengineering↗

bioRxiv2026

Sequential Molecular Interactions Shape Aβ42 Aggregation, Propagation, and Toxicity

Protein aggregation is a context-dependent process in which the molecular environment can influence the properties of the resulting assemblies. In biological systems, these interactions can occur sequentially, as aggregates formed in one cellular or tissue context may encounter different molecular partners and act as seeds in subsequent aggregation events. Here, we used sequential seeding as a controlled experimental model of this temporal and contextual complexity to investigate how prion-like sequences from the gut microbiome modulate amyloid-{beta} aggregation across successive aggregation cycles. Combining kinetic, biophysical, conformational, and toxicity analyses, we show that early interactions with exogenous peptides modify the properties of first-generation A{beta}40- and A{beta}42-derived seeds, resulting in propagated A{beta}42 assemblies with distinct molecular and functional properties. These findings support an Interaction History model in which exogenous sequences bias the emergence of aggregate populations whose properties and subsequent propagation depend on the molecular contexts experienced during earlier aggregation events. Overall, our results present A{beta} aggregation as a history-dependent process and suggest that single-step assays may fail to capture aggregate diversity that emerges across successive aggregation cycles.

biochemistry↗

bioRxiv2018

Identification of biological mechanisms by semantic classifier systems

The interpretability of a classification model is one of its most essential characteristics. It allows for the generation of new hypotheses on the molecular background of a disease. However, it is questionable if more complex molecular regulations can be reconstructed from such limited sets of data. To bridge the gap between complexity and interpretability, we replace the de novo reconstruction of these processes by a hybrid classification approach partially based on existing domain knowledge. Using semantic building blocks that reflect real biological processes these models were able to construct hypotheses on the underlying genetic configuration of the analysed phenotypes. As in the building process, also these hypotheses are composed of high-level biology-based terms. The semantic information we utilise from gene ontology is a vocabulary which comprises the essential processes or components of a biological system. The constructed semantic multi-classifier system consists of expert base classifiers which each select the most suitable term for characterising their assigned problems. Our experiments conducted on datasets of three distinct research fields revealed terms with well-known associations to the analysed context. Furthermore, some of the chosen terms do not seem to be obviously related to the issue and thus lead to new, hypotheses to pursue.\n\nAuthor summaryData mining strategies are designed for an unbiased de novo analysis of large sample collections and aim at the detection of frequent patterns or relationships. Later on, the gained information can be used to characterise diagnostically relevant classes and for providing hints to the underlying mechanisms which may cause a specific phenotype or disease. However, the practical use of data mining techniques can be restricted by the available resources and might not correctly reconstruct complex relationships such as signalling pathways.\n\nTo counteract this, we devised a semantic approach to the issue: a multi-classifier system which incorporates existing biological knowledge and returns interpretable models based on these high-level semantic terms. As a novel feature, these models also allow for qualitative analysis and hypothesis generation on the molecular processes and their relationships leading to different phenotypes or diseases.

systems biology↗

bioRxiv2017

IntelliEppi: Intelligent reaction monitoring and holistic data management system for the molecular biology lab

Daily alterations of routines and protocols create high, yet so far unmet demands for intelligent reaction monitoring, quality control and data management in molecular biology laboratories. To meet such needs, the \"internet of things\" is implemented here. We propose an approach which combines direct tracking of lab tubes, reactions and racks with a comprehensive data management system. Reagent tubes in this system are tagged with 2D data matrices or imprinted RFID-chips using a unique identification number. For each tube, individual content and all relevant information based on conducted experimental procedures are stored in an experimental data management system. This information is managed automatically but allow scientists to engage and interfere via user-friendly graphical interface. Tagged tubes are used in connection with a detectable RFID-tagged rack. We show that reaction protocols, HTS storage and complex reactions are easily planned and controlled.

bioinformatics↗

bioRxiv2016

MultiCellDS: a standard and a community for sharing multicellular data

Cell biology is increasingly focused on cellular heterogeneity and multicellular systems. To make the fullest use of experimental, clinical, and computational efforts, we need standardized data formats, community-curated \"public data libraries\", and tools to combine and analyze shared data. To address these needs, our multidisciplinary community created MultiCellDS (MultiCellular Data Standard): an extensible standard, a library of digital cell lines and tissue snapshots, and support software. With the help of experimentalists, clinicians, modelers, and data and library scientists, we can grow this seed into a community-owned ecosystem of shared data and tools, to the benefit of basic science, engineering, and human health.

systems biology↗

bioRxiv2018

Growth adaptation of gnd and sdhCB Escherichia coli deletion strains diverges from a similar initial perturbation of the transcriptome

Adaptive laboratory evolution (ALE) has emerged as a new approach with which to pursue fundamental biological inquiries and, in particular, new insights into the systemic function of a gene product. Two E. coli knockout strains were constructed: one that blocked the Pentose Phosphate Pathway (gnd KO) and one that decoupled the TCA cycle from electron transport (sdhCDAB KO). Despite major perturbations in central metabolism, minimal growth rate changes were found in the two knockout strains. More surprisingly, many similarities were found in their initial transcriptomic states that could be traced to similarly perturbed metabolites despite the differences in the network location of the gene perturbations and concomitant rerouting of pathway fluxes around these perturbations. However, following ALE, distinct metabolomic and transcriptomic states were realized. These included divergent flux and gene expression profiles in the gnd and sdhCDAB KOs to overcome imbalances in NADPH production and nitrogen/sulfur assimilation, respectively, that were not obvious limitations of growth in the unevolved knockouts. Therefore, this work demonstrates that ALE provides a productive approach to reveal novel insights of gene function at a systems level that cannot be found by observing the fresh knockout alone.

systems biology↗

bioRxiv2018

Dynamic Modeling and Stochastic Simulation of Metabolic Networks

Increased technological methods have enabled the investigation of biology at nanoscale levels. Nevertheless, such systems necessitate the use of computational methods to comprehend the complex interactions occurring. Traditionally, dynamics of metabolic systems are described by ordinary differential equations producing a deterministic result which neglects the intrinsic heterogeneity of biological systems. More recently, stochastic modeling approaches have gained popularity with the capacity to provide more realistic outcomes. Yet, solving stochastic algorithms tend to be computationally intensive processes. Employing the queueing theory, an approach commonly used to evaluate telecommunication networks, reduces the computational power required to generate simulated results, while simultaneously reducing expansion of errors inherent to classical deterministic approaches. Herein, we present the application of queueing theory to efficiently simulate stochastic metabolic networks. For the current model, we utilize glycolysis to demonstrate the power of the proposed modeling methods, and we describe simulation and pharmacological inhibition in glycolysis to further exemplify modeling capabilities.\n\nAuthor SummaryComputational biology is increasingly used to understand biological occurances and complex dynamics. Biological modeling, in general, aims to represent a biological system with computational approaches, as realistically and accurate as current methods allow. Metabolomics and metabolic systems have emerged as an important aspect of cellular biology, allowing a more sentive view for understanding the complex interactions occurring intracellularly as a result of normal or perturbed (or diseased) states. To understand metabolic changes, many researchers have commonly used Ordianary Differential Equations to produce in silico models of the in vitro system of interest. While these have been beneficial to date, continuing to advance computational methods of analyzing such systems is of interest. Stochastic models that include randomness have been known to produce more reaslistic results, yet the difficulty and intesive time component urges additional methods and techniques to be developed. In the present research, we propose using queueing networks as a technique to model complex metabolic systems, doing such with a model of glycolysis, a core metabolic pathway.

bioengineering↗

bioRxiv2017

Understanding the dynamics of scaffold-mediated signaling

Many signaling networks involve scaffold proteins that bind multiple kinases in kinase cascades. While scaffolds play a fundamental role in regulating signaling, few hypotheses regarding their function have been rigorously examined. Here, we used dynamical models of scaffold signaling to investigate the impact scaffolds have on network behavior. We considered two paradigms of scaffold assembly: as either the nucleation point for assembly of discrete multi-subunit proteins (the machine paradigm) or a platform upon which kinases independently associate (the ensemble paradigm). We found that several well-accepted hypotheses regarding the role of scaffolds in regulating signal response either do not hold or depend critically on the assembly paradigm employed. In addition to providing novel insights into the function of scaffold proteins, our work suggests experiments that could distinguish between assembly paradigms. Our findings should also inform attempts to target scaffold proteins for therapeutic intervention and the design of scaffolds for synthetic biology.

systems biology↗

bioRxiv2018

Optimized cross-linking mass spectrometry for in situ interaction proteomics

Recent development of mass spectrometer cleavable protein cross-linkers and algorithms for their spectral identification now permits large-scale cross-linking mass spectrometry (XL-MS). Here, we optimized the use of cleavable disuccinimidyl sulfoxide (DSSO) cross-linker for labeling native protein complexes in live human cells. We applied a generalized linear mixture model to calibrate cross-link peptide-spectra matching (CSM) scores to control the sensitivity and specificity of large-scale XL-MS. Using specific CSM score thresholds to control the false discovery rate, we found that higher-energy collisional dissociation (HCD) and electron transfer dissociation (ETD) can both be effective for large-scale XL-MS protein interaction mapping. We found that the density and coverage of protein-protein interaction maps can be significantly improved through the use of multiple proteases. In addition, the use of sample-specific search databases can be used to improve the specificity of cross-linked peptide spectral matching. Application of this approach to human chromatin labeled in live cells recapitulated known and revealed new protein interactions of nucleosomes and other chromatin-associated complexes in situ. This optimized approach for mapping native protein interactions should be useful for a wide range of biological problems.

systems biology↗

bioRxiv2018

BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language

The rapid accumulation of knowledge in the field of systems and networks biology during recent years requires complex, but user-friendly and accessible web applications that allow from visualization to complex algorithmic analysis. While several web applications exist with various focuses on creation, revision, curation, storage, integration, collaboration, exploration, visualization, and analysis, many of these services remain disjoint and have yet to be packaged into a cohesive environment.\n\nHere, we present BEL Commons; an integrative knowledge discovery environment for networks encoded in the Biological Expression Language (BEL). Users can upload files in BEL to be parsed, validated, compiled, and stored with fine-granular permissions. After, users can summarize, explore, and optionally shared their networks with the scientific community. We have implemented a query builder wizard to help users find the relevant portions of increasingly large and complex networks and a visualization interface that allows them to explore their resulting networks. Finally, we have included a dedicated analytical service for performing data-driven analysis of knowledge networks to support hypothesis generation.\n\nThis web application can be freely accessed at https://bel-commons.scai.fraunhofer.de.

systems biology↗