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Results for “Systems Biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Maintaining gene expression levels by positive feedback in burst size in the presence of infinitesimal delay

Synthesis of individual molecules in the expression of genes often occurs in bursts of multiple copies. Gene regulatory feedback can affect the frequency with which these bursts occur or their size. Whereas frequency regulation has traditionally received more attention, we focus specifically on the regulation of burst size. It turns out that there are (at least) two alternative formulations of feedback in burst size. In the first, newly produced molecules immediately partake in feedback, even within the same burst. In the second, there is no within-burst regulation due to what we call infinitesimal delay. We describe both alternatives using a minimalistic Markovian drift-jump framework combining discrete and continuous dynamics. We derive detailed analytic results and efficient simulation algorithms for positive noncooperative autoregulation (whether infinitesimally delayed or not). We show that at steady state both alternatives lead to a gamma distribution of protein level. The steady-state distribution becomes available only after a transcritical bifurcation point is passed. Interestingly, the onset of the bifurcation is postponed by the inclusion of infinitesimal delay.

systems biology↗

bioRxiv2018

Continuous State HMMs for Modeling Time Series Single Cell RNA-Seq Data

MotivationMethods for reconstructing developmental trajectories from time series single cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods, are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy.\n\nResultsWe developed a new method based on continuous state HMMs (CSHMMs) for representing and modeling time series scRNA-Seq data. We define the CSHMM model and provide efficient learning and inference algorithms which allow the method to determine both the structure of the branching process and the assignment of cells to these branches. Analyzing several developmental single cell datasets we show that the CSHMM method accurately infers branching topology and correctly and continuously assign cells to paths, improving upon prior methods proposed for this task. Analysis of genes based on the continuous cell assignment identifies known and novel markers for different cell types.\n\nAvailabilitySoftware and Supporting website: www.andrew.cmu.edu/user/chiehll/CSHMM/\n\nContactzivbj@cs.cmu.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2018

Biodynamics: A novel quasi-first principles theory on the fundamental mechanisms of cellular function/dysfunction and the pharmacological modulation thereof

Cellular function depends on heterogeneous dynamic intra-, inter-, and supramolecular structure-function relationships. However, the specific mechanisms by which cellular function is transduced from molecular systems, and by which cellular dysfunction arises from molecular dysfunction are poorly understood. We proposed previously that cellular function manifests as a molecular form of analog computing, in which specific time-dependent state transition fluxes within sets of molecular species (\"molecular differential equations\" (MDEs)) are sped and slowed in response to specific perturbations (inputs). In this work, we offer a theoretical treatment of the molecular mechanisms underlying cellular analog computing (which we refer to as \"biodynamics\"), focusing primarily on non-equilibrium (dynamic) intermolecular state transitions that serve as the principal means by which MDE systems are solved (the molecular equivalent of mathematical \"integration\"). Under these conditions, bound state occupancy is governed by kon and koff, together with the rates of binding partner buildup and decay. Achieving constant fractional occupancy over time depends on: 1) equivalence between kon and the rate of binding site buildup); 2) equivalence between koff and the rate of binding site decay; and 3) free ligand concentration relative to koff/k0n (n {middle dot} Kd, where n is the fold increase in binding partner concentration needed to achieve a given fractional occupancy). Failure to satisfy these conditions results in fractional occupancy well below that corresponding to n {middle dot} Kd. The implications of biodynamics for cellular function/dysfunction and drug discovery are discussed.

systems biology↗

bioRxiv2018

Palantir characterizes cell fate continuities in human hematopoiesis

Recent studies using single cell RNA-seq (scRNA-seq) data derived from differentiating systems have raised fundamental questions regarding the discrete vs continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells, which treats cell-fate as a probabilistic process, and leverages entropy to measure the changing nature of cell plasticity along the differentiation trajectory. Palantir generates a high resolution pseudotime ordering of cells, and assigns each cell state with its probability to differentiate into each terminal state. We apply Palantir to human bone marrow scRNA-seq data and detect key landmarks of hematopoietic differentiation. Palantirs resolution enables identification of key transcription factors driving lineage fate choices, as these TFs closely track when cells lose plasticity. We demonstrate that Palantir is generalizable to diverse tissue types and well-suited to resolve less studied differentiating systems.

systems biology↗

bioRxiv2018

Mapping the human kinome in response to DNA damage

We provide a catalog for the effects of the human kinome on cell survival in response to DNA damaging agents, selected to cover all major DNA repair pathways. By treating 313 kinase-deficient cell lines with ten diverse DNA damaging agents, including seven commonly used chemotherapeutics, we were able to identify kinase specific vulnerabilities and resistances. In order to identify novel synthetic lethal interactions, we investigate the cellular response to carmustine for 25 cell lines, by establishing a phenotypic FACS assay designed to mechanistically investigate and validate gene-drug interactions. We show apoptosis, cell cycle, DNA damage and proliferation after alkylation or crosslink-induced damage for selected cell lines and rescue the cellular sensitivity of DYRK4, EPHB6, MARK3, PNCK as a proof of principle for our study. Our data suggest that some cancers with inactivated DYRK4, EPHB6, MARK3 or PNCK gene could be particularly vulnerable to treatment by alkylating chemotherapeutic agents carmustine or temozolomide.

systems biology↗

bioRxiv2018

Heat stress modifies the lactational performances and the urinary metabolomic profile related to gastrointestinal microbiota of dairy goats

The aim of the study was to identify the candidate biomarkers of heat stress (HS) in the urine of lactating dairy goats through the application of proton Nuclear Magnetic Resonance (1H NMR)-based metabolomic analysis. Dairy does (n = 16) in mid-lactation were submitted to thermal neutral (TN; indoors; 15 to 20{degrees}C; 40 to 45% humidity) or HS (climatic chamber; 37{degrees}C day, 30{degrees}C night; 40% humidity) conditions according to a crossover design (2 periods of 21 days). Thermophysiological traits and lactational performances were recorded and milk composition analyzed during each period. Urine samples were collected at day 15 of each period for 1H NMR spectroscopy analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) assessment with cross validation were used to identify the goat urinary metabolome from Human Metabolome Data Base. HS increased rectal temperature (1.2{degrees}C), respiratory rate (3.5-fold) and water intake (74%), but decreased feed intake (35%) and body weight (5%) of the lactating does. No differences were detected in milk yield, but HS decreased the milk contents of fat (9%), protein (16%) and lactose (5%). Metabolomics allowed separating TN and HS urinary clusters by PLS-DA. Most discriminating metabolites were hippurate and other phenylalanine (Phe) derivative compounds, which increased in HS vs. TN does. The greater excretion of these gut-derived toxic compounds indicated that HS induced a harmful gastrointestinal microbiota overgrowth, which should have sequestrated aromatic amino acids for their metabolism and decreased the synthesis of neurotransmitters and thyroid hormones, with negative impact on milk yield and composition. In conclusion, HS markedly changed the thermophysiological traits and lactational performances of dairy goats, which were translated into their urinary metabolomic profile through the presence of gut-derived toxic compounds. Hippurate and other Phe-derivative compounds are suggested as urinary biomarkers to detect heat stressed dairy animals in practice.

systems biology↗

bioRxiv2018

Dendritic spine geometry and spine apparatus organization govern the spatiotemporal dynamics of calcium

Dendritic spines are small subcompartments that protrude from the dendrites of neurons and are important for signaling activity and synaptic communication. These subcompartments have been characterized to have different shapes. While it is known that these shapes are associated with spine function, the specific nature of these shape-function relationships is not well understood. In this work, we systematically investigated the relationship between the shape and size of both the spine head and spine apparatus, a specialized endoplasmic reticulum compartment in the spine head, in modulating rapid calcium dynamics using mathematical modeling. We developed a spatial multi-compartment reaction-diffusion model of calcium dynamics in three dimensions with various flux sources including N-methyl-D-aspartate receptors (NMDAR), voltage sensitive calcium channels (VSCC), and different ion pumps on the plasma membrane. Using this model, we make several important predictions - first, the volume-to-surface area ratio of the spine regulates calcium dynamics, second, membrane fluxes impact calcium dynamics temporally and spatially in a nonlinear fashion, and finally the spine apparatus can act as a physical buffer for calcium by acting as a sink and rescaling the calcium concentration. These predictions set the stage for future experimental investigations of calcium dynamics in dendritic spines.

systems biology↗

bioRxiv2018

Inferring metabolite interactomes via molecular structure informed Bayesian graphical model selection with an application to coronary artery disease

IntroductionWhile the generation of reference genomes facilitates the elucidation of gene-phenome associations, reference models of the metabolome that are specific to organism, sample type (e.g. plasma, serum, urine, cell-culture), and state (including disease), remain uncommon. In studying heart disease in humans, a reference model describing the relationships between metabolites in plasma has not been determined but would have great utility as a reference for comparing acute disease states such as myocardial infarction.\n\nMaterials and MethodsWe present a methodology for deriving probabilistic models that describe the partial correlation structure of metabolite distributions (\"interactomes\") from metabolomics data. As determining partial correlation structures requires estimating p*(p-1)/2 parameters for p metabolites, the dimension of the search space for parameter values is immense. Consequently, we have developed a Bayesian methodology for the penalized estimation of model parameters in which the magnitude of penalization is drawn from probability distributions with hyperparameters linked to molecular structure similarity. In our work, structural similarity was determined as the Tanimoto coefficient of algorithmically-generated \"atom colors\" that capture the local structure around each atom within each structure. A Gibbs sampler (a Markov chain Monte Carlo technique) was implemented for simulating the posterior distribution of model parameters. We have made software for implementing this methodology publicly available via the R package BayesianGLasso.\n\nResults / ConclusionsFirst, we demonstrate robust performance of our methodology (sensitivity, specificity, and measures of accuracy) for recovering the true underlying partial correlation structure over simulated datasets (with simulated metabolite abundances and simulated known structural similarity). We then present an interactome model for stable heart disease inferred from non-targeted mass spectrometry data via this methodology. Inspection of the local graph topology about cholate reveals probabilistic interactions with other primary bile acids, secondary bile acids, and many steroid hormones sharing the same precursors.

systems biology↗

bioRxiv2018

Evaluation of a Dual Isolation Width Acquisition (DIWA) method for isobaric labelling ratio decompression

ABSRACTIsobaric labelling is a highly precise approach for protein quantification. However, due to the isolation interference problem, isobaric tagging suffers from ratio underestimation at the MS2 level. The use of narrow isolation widths is a rational approach to alleviate the interference problem; however, this approach compromises proteome coverage. We reasoned that although a very narrow isolation window will result in loss of peptide fragment ions, the reporter ion signals will be retained for a significant portion of the spectra. Based on this assumption we have designed a Dual Isolation Width Acquisition (DIWA) method, in which each precursor is first fragmented with HCD using a standard isolation width for peptide identification and preliminary quantification, followed by a second MS2 HCD scan using a much narrower isolation width for the acquisition of quantitative spectra with reduced interference. We leverage the quantification obtained by the \"narrow\" scans to build linear regression models and apply these to decompress the fold-changes measured at the \"standard\" scans. We evaluate the DIWA approach using a nested two species/gene knockout TMT-6plex experimental design and discuss the perspectives of this approach.

systems biology↗

bioRxiv2018

Predicting the future direction of cell movement with convolutional neural networks

Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell fate. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future cell fate from current cell shape, and can be used to automatically identify those morphological features that influence future cell fate.

systems biology↗

bioRxiv2018

Resistant potato starch fuels beneficial host-microbe interactions in the gut

Interactions between diet, the microbiota, and the host set the ecological conditions in the gut and have broad implications for health. Prebiotics are dietary compounds that may shift these conditions towards health by promoting the growth of beneficial microbes. Pigs fed a diet amended with 5% resistant potato starch (RPS) exhibited alterations associated with gut health relative to swine fed an unamended diet (CON). RPS intake increased abundances of anaerobic Clostridia in feces and several tissues, as well as intestinal concentrations of butyrate. Functional gene amplicons suggested bacteria similar to Anaerostipes hadrus were stimulated by RPS intake. The CON treatment exhibited increased abundances of several genera of Proteobacteria (which utilize respiratory metabolisms) in several location. RPS intake increased the abundance of regulatory T cells in the cecum, but not periphery, and cecal immune status alterations were indicative of enhanced mucosal defenses. A network analysis of host and microbial changes in the cecum revealed that regulatory T cells positively correlated with butyrate concentration, luminal IgA concentration, expression of IL-6 and DEF1B, and several mucosa-associated bacterial taxa. Thus, the administration of RPS modulated the microbiota and host response, improved cecal barrier function, promoted immunological tolerance, and reduced the niche for bacterial respiration.\n\nImportanceThe gut microbiota is central to host health. Many disease states and disorders appear to arise from interactions between the gut microbial community and host immune system. As a result, methods and interventions to support the growth and activity of beneficial gut microbes are being actively pursued. Feeding the gut microbiota with dietary compounds, known as prebiotics, is one of the most promising ways to support gut health. Here we describe a successful prebiotic intervention in weaning swine, a relevant translational model for human health. This work unites microbial and immunological data and demonstrates one way a prebiotic intervention can play out for the benefit of the host.

systems biology↗

bioRxiv2018

Hierarchical Complexity of the Adult Human Structural Connectome

The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology.

systems biology↗

bioRxiv2018

Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data

The recent explosion of metagenomic sequencing data opens the door towards the modeling of microbial ecosystems in unprecedented detail. In particular, co-occurrence based prediction of ecological interactions could strongly benefit from this development. However, current methods fall short on several fronts: univariate tools do not distinguish between direct and indirect interactions, resulting in excessive false positives, while approaches with better resolution are so far computationally highly limited. Furthermore, confounding variables typical for cross-study data sets are rarely addressed. We present FlashWeave, a new approach based on a flexible Probabilistic Graphical Models framework to infer highly resolved direct microbial interactions from massive heterogeneous microbial abundance data sets with seamless integration of metadata. On a variety of benchmarks, FlashWeave outperforms state-of-the-art methods by several orders of magnitude in terms of speed while generally providing increased accuracy. We apply FlashWeave to a cross-study data set of 69 818 publicly available human gut samples, resulting in one of the largest and most diverse models of microbial interactions in the human gut to date.

systems biology↗

bioRxiv2018

Complex genetic and epigenetic regulation deviates gene expression from a global transcriptional program

Environmental or genetic perturbations lead to gene expression changes. While most analyses of these changes emphasize the presence of qualitative differences on just a few genes, we now know that changes are widespread. This large-scale variation has been linked to the exclusive influence of a global transcriptional program determined by the new physiological state of the cell. However, given the sophistication of eukaryotic regulation, we expect to have a complex architecture of specific control affecting this program. Here, we examine this architecture. Using data of Saccharomyces cerevisiae expression in different nutrient conditions, we first propose a five-sector genome partition, which integrates earlier models of resource allocation, as a framework to examine the deviations from the global control. In this scheme, we recognize invariant genes, whose regulation is dominated by physiology, specific genes, which substantially depart from it, and two additional classes that contain the frequently assumed growth-dependent genes. Whereas the invariant class shows a considerable absence of specific regulation, the rest is enriched by regulation at the level of transcription factors (TFs) and epigenetic modulators. We nevertheless find markedly different strategies in how these classes deviate. On the one hand, there are TFs that act in a unique way between partition constituents, and on the other, the action of chromatin modifiers is significantly diverse. The balance between regulatory strategies ultimately modulates the action of the general transcription machinery and therefore limits the possibility of establishing a unifying program of expression change at a genomic scale.

systems biology↗

bioRxiv2018

Integrative analyses of multi-tissue Hi-C and eQTL data demonstrate close spatial proximity between eQTLs and their target genes

Gene regulation is important for cells and tissues to function. At the genomic level, it has been studied from two aspects, the identification of expression quantitative trait loci (eQTLs) and identification of long-range chromatin interactions. It is important to understand their relationship, such as whether eQTLs regulate their target genes through physical chromatin interaction. Although previous studies have suggested enrichment of eQTLs in regions with a high chromatin interaction frequency, it is unclear whether this relationship is consistent across different tissues and cell lines and whether there would be any tissue-specific patterns. Here, we performed integrative analyses of eQTL and high-throughput chromatin conformation capture (Hi-C) data from 11 human primary tissue types and 2 human cell lines. We found that chromatin interaction frequency is positively correlated with the number of genes having eQTLs, and eQTLs and their target genes are more likely to fall in the same topologically associating domains than that expected from randomly generated control datasets. These results are consistent across all tissues and cell lines we evaluated. Moreover, in dorsolateral prefrontal cortex, spleen, hippocampus, pancreas and aorta, tissue-specific eQTLs are enriched in tissue-specific frequently interacting regions. These results reveal a more detailed picture of the complicated relationship between different mechanisms of gene regulation.\n\nAuthor summaryWhole-genome gene regulation has been studied in tissues and cell lines from multiple perspectives, including identification of expression quantitative trait loci (eQTLs) and identification of long-range chromatin interactions. These two complementary approaches focus on different aspects of gene regulation, one being statistical across individuals while the other being physical within a sample. Integrating results from these two approaches will help us understand their relationships, such as whether eQTLs regulate their target genes through physical chromatin interaction. We performed comprehensive analyses using data from multiple human tissues and cell lines, and showed that chromatin interaction frequency is positively associated with eQTL results in all evaluated tissues and cell lines. The observed relationships also displayed tissue-specific pattern in some tissues. Our results revealed a more detailed picture of the complicated relationship between the different mechanisms of gene regulation.

systems biology↗

bioRxiv2018

Quantifying biosynthetic network robustness across the human oral microbiome

Metabolic interactions, such as cross-feeding, play a prominent role in microbial communitystructure. For example, they may underlie the ubiquity of uncultivated microorganisms. We investigated this phenomenon in the human oral microbiome, by analyzing microbial metabolic networks derived from sequenced genomes. Specifically, we devised a probabilistic biosynthetic network robustness metric that describes the chance that an organism could produce a given metabolite, and used it to assemble a comprehensive atlas of biosynthetic capabilities for 88 metabolites across 456 human oral microbiome strains. A cluster of organisms characterized by reduced biosynthetic capabilities stood out within this atlas. This cluster included several uncultivated taxa and three recently co-cultured Saccharibacteria (TM7) phylum species. Comparison across strains also allowed us to systematically identify specific putative metabolic interdependences between organisms. Our method, which provides a new way of converting annotated genomes into metabolic predictions, is easily extendible to other microbial communities and metabolic products.

systems biology↗

bioRxiv2018

Identification of candidate genes underlying nodulation-specific phenotypes in Medicago truncatula through integration of genome-wide association studies and co-expression networks

Genome-wide association studies (GWAS) have proven to be a valuable approach for identifying genetic intervals associated with phenotypic variation in Medicago truncatula. These intervals can vary in size, depending on the historical local recombination near each significant interval. Typically, significant intervals span numerous gene models, limiting the ability to resolve high-confidence candidate genes underlying the trait of interest. Additional genomic data, including gene co-expression networks, can be combined with the genetic mapping information to successfully identify candidate genes. Co-expression network analysis provides information about the functional relationships of each gene through its similarity of expression patterns to other well-defined clusters of genes. In this study, we integrated data from GWAS and co-expression networks to pinpoint candidate genes that may be associated with nodule-related phenotypes in Medicago truncatula. We further investigated a subset of these genes and confirmed that several had existing evidence linking them nodulation, including MEDTR2G101090 (PEN3-like), a previously validated gene associated with nodule number.

systems biology↗

bioRxiv2018

Stochasticity in multi-phosphorylation and quasi steady state approximation in stochastic simulation

Quantitative and qualitative nature of chemical noise propagation in a network of chemical reactions depend crucially on the topology of reaction networks. Multisite reversible phosphorylation-dephosphorylation of target proteins is one such recurrently found topology in various cellular networks regulating key functions in living cells. Here we analytically calculated the stochasticity in multistep reversible chemical reactions by determining variance of phosphorylated species at the steady state using linear noise approximation. We investigated the dependence of variance on the rate parameters in the reaction chain and the number of phosphorylation sites on the species. Assuming a quasi steady state approximation on the multistep reactions, originating from the disparity in time scales in the network, we propose a simulation scheme for coupled chemical reactions to improve the computational efficiency of stochastic simulation of the network. We performed case studies on signal transduction cascade and positive feedback loop with bistability to show the accuracy and efficiency of the method.

systems biology↗