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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

fastBMA: Scalable Network Inference and Transitive Reduction

BACKGROUNDInferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a novel and computationally efficient method for eliminating redundant indirect edges in the network.\n\nFINDINGSWe evaluated the performance of fastBMA on synthetic data and experimental genome-wide yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory efficient, parallel and distributed application that scales to human genome wide expression data. A 10,000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster.\n\nCONCLUSIONSfastBMA is a significant improvement over its predecessor ScanBMA. It is orders of magnitude faster and more accurate than other fast network inference methods such as LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable timeframe. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/).

systems biology↗

bioRxiv2017

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

MotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.\n\nResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.\n\nAvailabilityR scripts available at https://github.com/jijiadong/JDINAC\n\nContactlxie@iscb.org\n\nSupplementary informationSupplementary data are available at bioRxiv online.

systems biology↗

bioRxiv2017

Deciphering the dynamical origin of mixed population during neural stem cell developmental lineage commitment

Neural stem cells (NSC's) often give rise to mixed population of cells during differentiation. However, the dynamical origin of these mixed states is poorly understood. In this article, our mathematical modeling study demonstrates that the bone morphogenetic protein 2 (BMP2) driven differential differentiation dynamics of NSC's in central and peripheral nervous systems essentially function through two distinct bi-stable switches that are mutually interconnected. Stochastic simulations of the model reveal that the mixed population originates due to the existence of these bistable switching regulations while the maintenance of such mixed states depends on the level of stochastic fluctuations of the system. Importantly, the model predicts that by individually altering the expression level of key regulatory proteins, the NSC's can be converted entirely to a preferred phenotype for BMP2 doses that earlier resulted into mixed population. Our findings show that efficient neuronal regeneration can be achieved by systematically maneuvering the differentiation dynamics.\n\nOne-sentence summaryUnraveling the differential dynamical origin, maintenance and escape route of mixed population in the midst of developmental fate commitment in central and peripheral nervous systems

systems biology↗

bioRxiv2017

Limits of noise for autoregulated gene expression

Gene expression is influenced by extrinsic noise (involving a fluctuating environment of cellular processes) and intrinsic noise (referring to fluctuations within a cell under constant environment). We study the standard model of gene expression including an (in-)active gene, mRNA and protein. Gene expression is regulated in the sense that the protein feeds back and either represses (negative feedback) or enhances (positive feedback) its production at the stage of transcription. While it is well-known that negative (positive) feedback reduces (increases) intrinsic noise, we give a precise result on the resulting fluctuations in protein numbers. The technique we use is an extension of the Langevin approximation and is an application of a central limit theorem under stochastic averaging for Markov jump processes (Kang, Kurtz and Popovic, 2014). We find that (under our scaling and in equilibrium), negative feedback leads to a reduction in the Fano factor of at most 2, while the noise under positive feedback is potentially unbounded. The fit with simulations is very good and improves on known approximations.

systems biology↗

bioRxiv2017

Large-scale rewiring in a yeast hybrid

The merging of genomes in inter-specific hybrids can result in novel phenotypes, including increased growth rate and biomass yield, a phenomenon known as heterosis. We describe a budding yeast hybrid that grows faster than its parents under different environments. Phenotypically, the hybrid progresses more rapidly through cell cycle checkpoints, relieves the repression of respiration in fast growing conditions, does not slow down its growth when presented with ethanol stress, and shows increasing signs of DNA damage. A systematic genetic screen identified hundreds of alleles affecting hybrid growth whose identity vastly differed between the hybrid and its parent and between growth conditions. This large-scale rewiring of allele effects suggests that despite showing clear heterosis, the hybrid is perturbed in multiple regulatory processes. We discuss the possibility that incompatibilities contribute to hybrid vigor by perturbing safeguard mechanisms that limit growth in the parental background.

systems biology↗

bioRxiv2017

Single-cell histories in growing populations: relating physiological variability to population growth

Cell size and individual growth rates vary substantially across genetically identical cell populations. This variation cannot entirely be explained by asynchronous cell division cycles, but also needs to take into account the differences in the histories that cells experience during their lifespan. We describe a stochastic framework to characterise cell size histories in an exponentially growing population. We show that these histories differ from cells observed in isolation, such as observed in mother machines. Quantifying these historical fluctuations allows us to predict the population growth rate. We highlight that the maximum attainable population growth cannot exceed the rate at which an average cell grows, but the population doubles faster than an average cell doubles its size. We validate this prediction using recent single-cell data. The theory thus provides fundamental limits on population fitness in terms of individual cell properties.

systems biology↗

bioRxiv2017

The design principles of biochemical timers: circuits that discriminate between transient and sustained stimulation

Many cellular responses for which timing is critical display temporal filtering - the ability to suppress response until stimulated for longer than a given minimal time. Temporal filtering can play a key role in filtering noise, choreographing the timing of events, and mediating the interpretation of dynamically encoded signals. To define the biochemical circuits capable of kinetic filtering, we comprehensively searched the space of three-node networks. We define a metric of \"temporal ultrasensitivity\", a measure of the steepness of activation as a function of stimulus duration. We identified five classes of core network motifs capable of temporal filtering, each with different functional properties such as rejecting high frequency noise, committing to response (bistability), and distinguishing between long stimuli. Combinations of the two most robust motifs, double inhibition (DI) and positive feedback with AND logic (PFAND), underlie several natural timer circuits involved in processes such as cell cycle transitions, T cell activation, and departure from the pluripotent state. The biochemical network motifs described in this study form a basis for understanding the common ways in which cells make dynamic decisions.

systems biology↗

bioRxiv2017

Stem cell differentiation is a stochastic process with memory

Pluripotent stem cells are able to self-renew indefinitely in culture and differentiate into all somatic cell types in vivo. While much is known about the molecular basis of pluripotency, the molecular mechanisms of lineage commitment are complex and only partially understood. Here, using a combination of single cell profiling and mathematical modeling, we examine the differentiation dynamics of individual mouse embryonic stem cells (ESCs) as they progress from the ground state of pluripotency along the neuronal lineage. In accordance with previous reports we find that cells do not transit directly from the pluripotent state to the neuronal state, but rather first stochastically permeate an intermediate primed pluripotent state, similar to that found in the maturing epiblast in development. However, analysis of rate at which individual cells enter and exit this intermediate metastable state using a hidden Markov model reveals that the observed ESC and epiblast-like macrostates conceal a chain of unobserved cellular microstates, which individual cells transit through stochastically in sequence. These hidden microstates ensure that individual cells spend well-defined periods of time in each functional macrostate and encode a simple form of epigenetic memory that allows individual cells to record their position on the differentiation trajectory. To examine the generality of this model we also consider the differentiation of mouse hematopoietic stem cells along the myeloid lineage and observe remarkably similar dynamics, suggesting a general underlying process. Based upon these results we suggest a statistical mechanics view of cellular identities that distinguishes between functionally-distinct macrostates and the many functionally-similar molecular microstates associated with each macrostate. Taken together these results indicate that differentiation is a discrete stochastic process amenable to analysis using the tools of statistical mechanics.

systems biology↗

bioRxiv2017

Fragile dynamics enable diverse genomic determinants to influence arrhythmia propensity

Genotype-phenotype relationships are determinants of human diseases. Often, we know little about why so many genes are involved in complex common diseases. We hypothesized that this multigene effect arises from the relationship between genes and physiological dynamics. We tested this hypothesis for arrhythmias as physiological dynamics define this disease. We integrated graph theory analysis of genomic and protein-protein interaction networks with dynamical models of ion channel function to identify the physiological dynamics of genome wide variation for five different arrhythmias. Regulatory networks for the cardiac conduction system and arrhythmias were constructed from GWAS and known disease genes. Electrophysiological models of myocyte action potentials were used to conduct extensive parameter variations to identify robust and fragile kinetic parameters that were then, using regulatory networks, associated with genomic determinants. We find that genome-wide determinants of arrhythmias that represent many cellular processes are selectively associated with fragile physiological dynamics of ion channel kinetics. This association predicts disease propensity. Deep RNA sequencing from human left ventricular tissue of arrhythmia and control subjects confirmed the predictive relationship. Taken together these studies show that the varied multigene effects of arrhythmias arises because of associations with fragile kinetic parameters of cardiac electrophysiology.\n\nSignificance StatementOur understanding of the genetics of common diseases has advanced exponentially over the past decade. We now know that differences and variation in multiple genes contribute to disease susceptibility with significant heterogeneity in the phenotype. However, how genetic variation contributes to disease phenotypes remains unknown. We hypothesized that the relationships between physiological dynamics and genetic architecture is a fundamental determinant of disease susceptibility and genetic heterogeneity. To test our hypothesis, we integrated mathematical models of cardiac electrophysiology with genetic network models of cardiac arrhythmias. We found that disease related genome variants were selectively associated with fragile kinetic parameters that predict disease propensity and identified several novel cellular processes associated with arrhythmogenesis.

systems biology↗

bioRxiv2017

Multiplexed dynamic imaging of genomic loci in single cells by combined CRISPR imaging and DNA sequential FISH

Visualization of chromosome dynamics allows the investigation of spatiotemporal chromatin organization and its role in gene regulation and other cellular processes. However, current approaches to label multiple genomic loci in live cells have a fundamental limitation in the number of loci that can be labelled and uniquely identified. Here we describe an approach we call \"track first and identify later\" for multiplexed visualization of chromosome dynamics by combining two techniques: CRISPR labeling and DNA sequential fluorescence in situ hybridization (DNA seqFISH). Our approach first labels and tracks chromosomal loci in live cells with the CRISPR system, then barcodes those loci by DNA seqFISH in fixed cells and resolves their identities. We demonstrate our approach by tracking telomere dynamics, identifying 12 unique subtelomeric regions with variable detection efficiencies, and tracking back the telomere dynamics of respective chromosomes in mouse embryonic stem cells.

systems biology↗

bioRxiv2017

Diverse yet highly selective interorgan crosstalk mechanisms shape the bodywide transcriptome landscape

The cardiovascular system facilitates body-wide distribution of oxygen, a vital process for development and survival of virtually all vertebrates. However, zebrafish, a vertebrate model organism, appears to form organs and survive mid-larval periods without the functional cardiovascular system. Despite such dispensability, it is the first organ to develop. Such enigma prompted us to hypothesize yet other cardiovascular functions that are important for developmental and/or physiological processes. Hence, systematic cellular ablations and functional perturbations are performed on zebrafish cardiovascular system to gain comprehensive and body-wide understanding of such functions and to elucidate underlying mechanisms. This approach identifies a set of organ-specific genes, each implicated for important functions. The study also unveils distinct cardiovascular mechanisms, each differentially regulating their expressions in organ-specific and oxygen-independent manners. Such mechanisms are mediated by organ-vessel interactions, circulation-dependent signals, and circulation-independent beating-heart-derived signals. Hence, a comprehensive and body-wide functional landscape of the cardiovascular system reported herein may provide a clue as to why it is the first organ to develop. Furthermore, the dataset herein could serve as a resource for the study of organ development and function.\n\nSUMMARY STATEMENTThe body-wide landscape of the cardiovascular functions during development is reported. Such landscape may provide a clue as to why the cardiovascular system is the first organ to develop.

systems biology↗

bioRxiv2017

Translating ceRNA susceptibilities into correlation functions

Competition to bind microRNAs induces an effective positive crosstalk between their targets, therefore known as competing endogenous RNAs or ceRNAs. While such an effect is known to play a significant role in specific situations, estimating its strength from data and, experimentally, in physiological conditions appears to be far from simple. Here we show that the susceptibility of ceRNAs to different types of perturbations affecting their competitors (and hence their tendency to crosstalk) can be encoded in quantities as intuitive and as simple to measure as correlation functions. We confirm this scenario by extensive numerical simulations and validate it by re-analyzing PTENs crosstalk pattern from TCGA breast cancer database. These results clarify the links between different quantities used to estimate the intensity of ceRNA crosstalk and provide new keys to analyze transcriptional datasets and effectively probe ceRNA networks in silico.

systems biology↗

bioRxiv2017

WIDE-SCALE COMPARATIVE ANALYSIS OF LONGEVITY GENES AND GENETIC INTERVENTIONS

Hundreds of genes have been identified as being involved in the control of lifespan in the four common model organisms (yeast, worm, fruit fly and mouse). A major challenge is to determine if longevity-associated genes (LAGs) are model-specific or may play a universal role as longevity regulators across diverse taxa. A wide-scale comparative analysis of the 1,805 known LAGs across 205 species revealed that (i) LAG orthologs are substantially over-represented, from bacteria to mammals, especially noted for essential LAGs; (ii) the effects on lifespan, when manipulating orthologous LAGs in different model organisms, were mostly concordant, despite of a high evolutionary distance between them; (iii) the most conserved LAGs were enriched in translational processes, energy metabolism, development, and DNA repair. The least conserved LAGs were enriched in autophagy (Fungi), G-proteins (Nematodes), and neuroactive ligand-receptor interactions (Chordata). The results also suggest that antagonistic pleiotropy is a conserved principle of aging.

systems biology↗

bioRxiv2017

A Multi-Parameter Analysis of Cellular Coordination of Multiple Transcriptome Regulation Mechanisms

To understand cellular coordination of multiple transcriptome regulation mechanisms, we simultaneously measured transcription rate (TR), mRNA abundance (RA) and translation activity (TA). This revealed multiple quantitative insights. First, the genomic profiles of the three parameters are systematically different in key statistical features. Sequentially more genes exhibit extreme low or high expression values from TR to RA, then to TA. That is, because of cellular coordination of these regulatory mechanisms, sequentially higher levels of gene expression selectivity are achieved as genetic information flow from the genome to the proteome. Second, the contribution of the stabilization-by-translation regulatory mechanism to the cellular coordination process was assessed. The data enabled an estimation of mRNA stability, revealing a moderate but significant positive correlation between the estimated mRNA stability and translation activity. Third, the proportion of a mRNA occupied by un-translated regions (UTR) exhibits a negative relationship with the level of this correlation, and is thus a major determinant of the mode of regulation of the mRNA. High-UTR-proportion mRNAs tend to defy the stabilization-by-translation regulatory mechanism, staying out of the polysome but remaining stable; mRNAs with little UTRs largely follow this regulation. In summary, we quantitatively delineated the relationship among multiple transcriptome regulation parameters, i.e., cellular coordination of corresponding regulatory mechanisms.

systems biology↗

bioRxiv2017

Gene length as a regulator for ribosome recruitmentand protein synthesis: theoretical insights

Protein synthesis rates are determined, at the translational level, by properties of the transcripts sequence. The efficiency of an mRNA can be tuned by varying the ribosome binding sites controlling the recruitment of the ribosomes, or the codon usage establishing the speed of protein elongation. In this work we propose transcript length as a further key determinant of translation efficiency. Based on a physical model that considers the kinetics of ribosomes advancing on the mRNA and diffusing in its surrounding, as well as mRNA circularisation and ribosome drop-off, we explain how the transcript length may play a central role in establishing ribosome recruitment and the overall translation rate of an mRNA. We also demonstrate how this process may be involved in shaping the experimental ribosome density-gene length dependence. Finally, we argue that cells could exploit this mechanism to adjust and balance the usage of its ribosomal resources.

systems biology↗

bioRxiv2017

Linking circadian time to growth rate quantitatively via carbon metabolism

Predicting a multicellular organisms phenotype quantitatively from its genotype is challenging, as genetic effects must propagate across scales. Circadian clocks are intracellular regulators that control temporal gene expression patterns and hence metabolism, physiology and behaviour. Here we explain and predict canonical phenotypes of circadian timing in a multicellular, model organism. We used diverse metabolic and physiological data to combine and extend mathematical models of rhythmic gene expression, photoperiod-dependent flowering, elongation growth and starch metabolism within a Framework Model for the vegetative growth of Arabidopsis thaliana, sharing the model and data files in a structured, public resource. The calibrated model predicted the effect of altered circadian timing upon each particular phenotype in clock-mutant plants under standard laboratory conditions. Altered night-time metabolism of stored starch accounted for most of the decrease in whole-plant biomass, as previously proposed. Mobilisation of a secondary store of malate and fumarate was also mis-regulated, accounting for any remaining biomass defect. We test three candidate mechanisms for the accumulation of these organic acids. Our results link genotype through specific processes to higher-level phenotypes, formalising our understanding of a subtle, pleiotropic syndrome at the whole-organism level, and validating the systems approach to understand complex traits starting from intracellular circuits. This work updates the first biorXiv version, February 2017, https://doi.org/10.1101/105437, with an expanded description and additional analysis of the same core data sets and the same FMv2 model, summary tables and supporting, follow-on data from three further studies with further collaborators. This biorXiv revision constitutes the second version of this report.

systems biology↗

bioRxiv2017

Microvessel Chaste: An Open Library for Spatial Modelling of Vascularized Tissues

Spatial models of vascularized tissues are widely used in computational physiology, to study for example, tumour growth, angiogenesis, osteogenesis, coronary perfusion and oxygen delivery. Composition of such models is time-consuming, with many researchers writing custom software for this purpose. Recent advances in imaging have produced detailed three-dimensional (3D) datasets of vascularized tissues at the scale of individual cells. To fully exploit such data there is an increasing need for software that allows user-friendly composition of efficient, 3D models of vascularized tissue growth, and comparison of predictions with in vivo or in vitro experiments and other models. Microvessel Chaste is a new open-source library for building spatial models of vascularized tissue growth. It can be used to simulate vessel growth and adaptation in response to mechanical and chemical stimuli, intra- and extra-vascular transport of nutrient, growth factor and drugs, and cell proliferation in complex 3D geometries. The library provides a comprehensive Python interface to solvers implemented in C++, allowing user-friendly model composition, and integration with experimental data. Such integration is facilitated by interoperability with a growing collection of scientific Python software for image processing, statistical analysis, model annotation and visualization. The library is available under an open-source Berkeley Software Distribution (BSD) licence at https://jmsgrogan.github.io/MicrovesselChaste. This article links to two reproducible example problems, showing how the library can be used to model tumour growth and angiogenesis with realistic vessel networks.

systems biology↗

bioRxiv2017

Heterogeneous chromatin mobility derived from chromatin states is a determinant of genome organisation in S. cerevisiae

Spatial organisation of the genome is essential for regulating gene activity, yet the mechanisms that shape this three-dimensional organisation in eukaryotes are far from understood. Here, we combine bioinformatic determination of chromatin states during normal growth and heat shock, and computational polymer modelling of genome structure, with quantitative microscopy and Hi-C to demonstrate that differential mobility of yeast chromosome segments leads to spatial self-organisation of the genome. We observe that more than forty percent of chromatin-associated proteins display a poised and heterogeneous distribution along the chromosome, creating a heteropolymer. This distribution changes upon heat shock in a concerted, state-specific manner. Simulating yeast chromosomes as heteropolymers, in which the mobility of each segment depends on its cumulative protein occupancy, results in functionally relevant structures, which match our experimental data. This thermodynamically driven self-organisation achieves spatial clustering of poised genes and mechanistically contributes to the directed relocalisation of active genes to the nuclear periphery upon heat shock.\n\nOne Sentence SummaryUnequal protein occupancy and chromosome segment mobility drive 3D organisation of the genome.

systems biology↗