SEARCH · Search bioRxiv

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 865 records · Page 48Linked to original sources

bioRxiv2017

Optimal Growth Of Microbes On Mixed Carbon Sources

A classic problem in microbiology is that bacteria display two types of growth behavior when cultured on a mixture of two carbon sources: in certain mixtures the bacteria consume the two carbon sources sequentially (diauxie) and in other mixtures the bacteria consume both sources simultaneously (co-utilization). The search for the molecular mechanism of diauxie led to the discovery of the lac operon and gene regulation in general. However, why microbes would bother to have different strategies of taking up nutrients remained a mystery. Here we show that diauxie versus co-utilization can be understood from the topological features of the metabolic network. A model of optimal allocation of protein resources to achieve maximum growth quantitatively explains why and how the cell makes the choice when facing multiple carbon sources. Our work solves a long-standing puzzle and sheds light on microbes optimal growth in different nutrient conditions.

systems biology↗

bioRxiv2017

A Dynamic Bioenergetic Model For Coral-Symbiodinium Symbioses And Coral Bleaching As An Alternate Stable State

Coral reef ecosystems owe their ecological success-and vulnerability to climate change-to the symbiotic metabolism of corals and Symbiodinium spp. The urgency to understand and predict the stability and breakdown of these symbioses (i.e., coral bleaching) demands the development and application of theoretical tools. Here, we develop a dynamic bioenergetic model of coral-Symbiodinium symbioses that demonstrates realistic steady-state patterns in coral growth and symbiont abundance across gradients of light, nutrients, and feeding. Furthermore, by including a mechanistic treatment of photo-oxidative stress, the model displays dynamics of bleaching and recovery that can be explained as transitions between alternate stable states. These dynamics reveal that healthy and bleached states correspond broadly to nitrogen- and carbon-limitation in the system, with transitions between them occurring as integrated responses to multiple environmental factors. Indeed, a suite of complex emergent behaviors reproduced by the model (e.g., bleaching is exacerbated by nutrients and attenuated by feeding) suggests it captures many important attributes of the system; meanwhile, its modular framework and open source R code are designed to facilitate further problem-specific development. We see significant potential for this modeling framework to generate testable hypotheses and predict integrated, mechanistic responses of corals to environmental change, with important implications for understanding the performance and maintenance of symbiotic systems.

systems biology↗

bioRxiv2017

A Map Of Genetic Interactions In Cancer Cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype-dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/{beta}-catenin signaling identified GANAB and PRKCSH as new positive regulators of Wnt/{beta}-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

systems biology↗

bioRxiv2017

Bidirectional Changes Of Hepatic And Peripheral Insulin Clearance Characterize Abnormal Temporal Patterns Of Serum Insulin Concentration In Diabetic Subjects

Insulin plays a central role in glucose homeostasis, and impairment of insulin action causes glucose intolerance and leads to type 2 diabetes mellitus (T2DM). A decrease in the transient peak and sustained increase of circulating insulin by glucose infusion accompany T2DM pathogenesis. However, the mechanism underlying this abnormal temporal pattern of circulating insulin concentration remains unknown. Here we show that bidirectional changes of hepatic and peripheral insulin clearance characterize this abnormal temporal pattern of circulating insulin concentration during the progression of T2DM. We developed a mathematical model using a hyperglycemic and hyperinsulinemic-euglycemic clamp in 111 subjects, including healthy normoglycemic and diabetic subjects. The hepatic and peripheral insulin clearance significantly increase and decrease, respectively, during the progression of glucose intolerance. The increased hepatic insulin clearance reduces the amplitude of circulating insulin concentration, whereas the decreased peripheral insulin clearance changes the temporal patterns of circulating insulin concentration from transient to sustained. These results provide insight that may be useful in treating T2DM associated with aberrant insulin clearance.

systems biology↗

bioRxiv2017

Glycosylation Flux Analysis Reveals Dynamic Changes Of Intracellular Glycosylation Flux Distribution In Chinese Hamster Ovary Fed-Batch Cultures

N-linked glycosylation of proteins has both functional and structural significance. Importantly, the glycan structure of a therapeutic protein influences its efficacy, pharmacokinetics, pharmacodynamics and immunogenicity. In this work, we developed glycosylation flux analysis (GFA) for predicting intracellular production and consumption rates (fluxes) of glycoforms, and applied this method to CHO fed-batch monoclonal antibody (mAb) production using two different media compositions, with and without additional manganese feeding. The GFA is based on a constraint-based modelling of the glycosylation network, employing a pseudo steady state assumption. While the glycosylation fluxes in the network are balanced at each time point, the GFA allows the fluxes to vary with time by way of two scaling factors: (1) an enzyme-specific factor that captures the temporal changes among glycosylation reactions catalyzed by the same enzyme, and (2) the cell specific productivity factor that accounts for the dynamic changes in the mAb production rate. The GFA of the CHO fed-batch cultivations showed that regardless of the media composition, the fluxes of galactosylation decreased with the cultivation time in comparison to the other glycosylation reactions. Furthermore, the GFA showed that the addition of Mn, a cofactor of galactosyltransferase, has the effect of increasing the galactosylation fluxes but only during the beginning of the cultivation period. The results thus demonstrated the power of the GFA in delineating the dynamic alterations of the glycosylation fluxes by local (enzyme-specific) and global (cell specific productivity) factors.

systems biology↗

bioRxiv2017

Modeling the dynamics of progression of chromosomal alterations in cervical cancer: a computational model

Computational modeling has been applied to simulate the heterogeneity of cancer behavior. The development of Cervical Cancer (CC) is a process in which the cell acquires dynamic behavior among non-deleterious and deleterious mutations, exhibiting chromosomal alterations as a manifestation of this dynamic. To further determine the progression of chromosomal alterations in precursor lesions and CC, we introduce a computational model to study the dynamic of deleterious and non-deleterious mutations as an outcome of tumor progression. Analysis of chromosomal alterations mediated by our model reveals that multiple deleterious mutations are more frequent in precursor lesions than in CC. Cells with lethal deleterious mutations would be eliminated, which would mitigate cancer progression; on the other hand, cells with non-deleterious mutations would become dominant, which could predispose to cancer progression. The study of somatic alterations by computer simulations during cancer progression provides a feasible pathway for insights into the transformation of cell mechanisms in humans. During cancer progression, tumors may acquire new phenotype traits, such as the ability to invade and metastasize or to become clinically important when they develop drug resistance. Chromosomal alterations non deleterious contributes to this progression.

systems biology↗

bioRxiv2017

A Systematic Atlas of Chaperome Deregulation Topologies Across the Human Cancer Landscape

Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Zooming in on the chaperome as a central PN component we turned to a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analysis highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro2) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders.

systems biology↗

bioRxiv2017

Improved Outcome And Cost Effectiveness In Ablation Of Persistent Atrial Fibrillation Under General Anaesthetic

AimsOutcome of persistent atrial fibrillation (AF) ablation remains suboptimal. Techniques employed to reduce arrhythmia recurrence rate are more likely to be embraced if cost-effectiveness can be demonstrated. A single-centre observational study assessed whether use of general anaesthesia (GA) in persistent AF ablation improved outcome and was cost-effective.\n\nMethods292 patients undergoing first ablation procedures for persistent AF under conscious sedation or GA were followed. End points were freedom from listing for repeat ablation at 18 months and freedom from recurrence of atrial arrhythmia at one year.\n\nResultsFreedom from atrial arrhythmia was higher in patients who underwent ablation under GA rather than sedation (63.9% vs 42.3%, HR 1.87, 95% CI: 1.23 to 2.86, p = 0.002). Significantly fewer GA patients were listed for repeat procedures (29.2% vs 42.7%, HR 1.62, 95% CI: 1.01 to 2.60, p = 0.044). Despite GA procedures costing slightly more, a saving of {pound}177 can be made per patient in our centre for a maximum of 2 procedures if all persistent AF ablations are performed under GA.\n\nConclusionsIn patients with persistent AF, it is both clinical and economically more effective to perform ablation under GA rather than sedation.\n\nWhats New?O_LIThere is very little known regarding the clinical outcome of catheter ablation of AF under GA compared with sedation; to our knowledge there is one study only in paroxysmal AF and no studies examining cost effectiveness.\nC_LIO_LIThis study shows that in patients with persistent AF, it is both clinical and economically more effective to perform ablation under GA rather than sedation.\nC_LIO_LIThis study leads us to recommend the use of GA for the ablation of persistent AF. As PAF ablation is now increasingly being undertaken by single shot techniques which do not have the same requirements for analgesia and immobility, GA resources may be allocated for persistent AF ablation.\nC_LI

systems biology↗

bioRxiv2017

Ablation Of Complex Fractionated Electrograms Improves Outcome In Long Standing Persistent Atrial Fibrillation

PurposeThere is controversy and sparse data on whether substrate based techniques in addition to pulmonary vein isolation (PVI) confer benefit in the catheter ablation of persistent atrial fibrillation (AF), especially if long standing. We performed an observational study to assess whether substrate based ablation improved freedom from atrial arrhythmia.\n\nMethods286 patients undergoing first ablation procedures for persistent AF with PVI only, PVI plus linear ablation, or PVI plus complex fractionated electrogram (CFAE) and linear ablation were followed. Primary end point was freedom from atrial arrhythmia at one year.\n\nResultsMean duration of pre-procedure time in AF was 28+/-27 months. Freedom from atrial arrhythmia was higher with a PVI+CFAE+lines strategy then for PVI alone (HR 1.56, 95% CI: 1.04-2.34, p=0.032) but was not higher with PVI+lines. Benefit of substrate modification was conferred for pre-procedure times in AF of over 30 months. The occurrence of atrial tachycardia was higher when lines were added to the ablation strategy (HR 0.08, 95% CI: 0.01-0.59, p=0.014). Freedom from atrial arrhythmia at 1 year was higher with lower patient age, use of general anaesthetic (GA), normal or mildly dilated left atrium and decreasing time in AF.\n\nConclusionsIn patients with long standing persistent AF of over 30 months duration, CFAE ablation resulted in improved freedom from atrial arrhythmia. Increased freedom from atrial arrhythmia occurs in patients who are younger and have smaller atria, and with GA procedures. Linear ablation did not improve outcome and resulted in a higher incidence of atrial tachycardia.

systems biology↗

bioRxiv2017

A Note On Dynamical Compensation And Its Relation To Parameter Identifiability

We recently identified a motif for dynamical compensation (DC) - a property where a system maintains the dynamics and steady-state of a regulated variable robust in the face of fluctuations in key parameters. Such parameters are therefore unidentifiable from measurements of the regulated variable at steady-state. On the other hand, since the models showing dynamical compensation are typically non-redundant, their parameters are identifiable from experimental data. We clarify this apparent discrepancy by requiring that the parameters of DC circuits be identifiable both away from steady-state and when measuring other system variables. We use this observation to provide a definition for DC in terms of parameter identifiability and discuss its relevance for the examples provided in Karin et al.

systems biology↗

bioRxiv2017

Pervasive Discordance Between mRNA And Protein Expression During Embryonic Stem Cell Differentiation

During in vitro differentiation, pluripotent stem cells undergo extensive remodeling of their gene expression profile. While studied extensively at the transcriptome level, much less is known about protein dynamics. Here, we measured mRNA and protein levels of 7459 genes during differentiation of embryonic stem cells (ESCs). This comprehensive data set revealed pervasive discordance between mRNA and protein. The high temporal resolution of the data made it possible to determine protein turnover rates genome-wide by fitting a kinetic model. This model further enabled us to systematically identify dynamic post-transcriptional regulation. Moreover, we linked different modes of regulation to the function of specific gene sets. Finally, we showed that the kinetic model can be applied to singlecell transcriptomics data to predict protein levels in differentiated cell types. In conclusion, our comprehensive data set, easily accessible through a web application, is a valuable resource for the discovery of post-transcriptional regulation in ESC differentiation.

systems biology↗

bioRxiv2017

Predicting Novel Metabolic Pathways Through Subgraph Mining

The ability to predict pathways for biosynthesis of metabolites is very important in metabolic engineering. It is possible to mine the repertoire of biochemical transformations from reaction databases, and apply the knowledge to predict reactions to synthesize new molecules. However, this usually involves a careful understanding of the mechanism and the knowledge of the exact bonds being created and broken. There is clearly a need for a method to rapidly predict reactions for synthesizing new molecules, which relies only on the structures of the molecules, without demanding additional information such as thermodynamics or hand-curated information such as atom-atom mapping, which are often hard to obtain accurately.\n\nWe here describe a robust method based on subgraph mining, to predict a series of biochemical transformations, which can convert between two (even previously unseen) molecules. We first describe a reliable method based on subgraph edit distance to map reactants and products, using only their chemical structures. Having mapped reactants and products, we identify the reaction centre and its neighbourhood, the reaction signature, and store this in a reaction rule network. This novel representation enables us to rapidly predict pathways, even between previously unseen molecules. We also propose a heuristic that predominantly recovers natural biosynthetic pathways from amongst hundreds of possible alternatives, through a directed search of the reaction rule network, enabling us to provide a reliable ranking of the different pathways. Our approach scales well, even to databases with > 100,000 reactions. A Java-based implementation of our algorithms is available at https://github.com/RamanLab/ReactionMiner\n\nCCS CONCEPTS*Information systems [->]Data mining; *Applied computing [->]Bioinformatics;

systems biology↗

bioRxiv2017

Multiple Sources Of Signaling Noise In Bacterial Chemotaxis Network

Cellular networks are intrinsically subject to stochastic fluctuations, but analysis of the resulting noise remained largely limited to gene expression. The pathway controlling chemotaxis of Escherichia coli provides one example where posttranslational signaling noise has been deduced from cellular behavior. This noise was proposed to result from stochasticity in chemoreceptor methylation, and it is believed to enhance environment exploration by bacteria. Here we combined single-cell FRET measurements with analysis based on the fluctuation-dissipation theorem (FDT) to characterize origins of activity fluctuations within the chemotaxis pathway. We observed surprisingly large methylation-independent thermal fluctuations of receptor activity, which contribute to noise comparably to the energy-consuming methylation dynamics. Interactions between clustered receptors involved in amplification of chemotactic signals are also necessary to produce the observed large activity fluctuations. Our work thus shows that the high response sensitivity of this cellular pathway also increases its susceptibility to noise, from thermal and out-of-equilibrium processes.

systems biology↗

bioRxiv2017

Generation And Attenuation Of Variability In A Bacterial Signalling Network Revealed By Single-Cell FRET

We present in vivo single-cell FRET measurements in the Escherichia coli chemotaxis system that reveal pervasive signaling variability, both across cells in isogenic populations and within individual cells over time. We quantify cell-to-cell variability of adaptation, ligand response, as well as steady-state output level, and analyze the role of network design in shaping this diversity from gene expression noise. In the absence of changes in gene expression, we find that single cells demonstrate strong temporal fluctuations. We provide evidence that such signaling noise can arise from at least two sources: (i) stochastic activities of adaptation enzymes, and (ii) receptor-kinase dynamics in the absence of adaptation. We demonstrate that under certain conditions, (ii) can generate giant fluctuations that drive signaling activity of the entire cell into a stochastic two-state switching regime. Our findings underscore the importance of molecular noise, arising not only in gene expression but also in protein networks.

systems biology↗

bioRxiv2017

The fundamental principles of antibody repertoire architecture revealed by large-scale network analysis

The antibody repertoire is a vast and diverse collection of B-cell receptors and antibodies that confer protection against a plethora of pathogens. The architecture of the antibody repertoire, defined by the network similarity landscape of its sequences, is unknown. Here, we established a novel high-performance computing platform to construct large-scale networks from high-throughput sequencing data (>100000 unique antibodies), in order to uncover the architecture of antibody repertoires. We identified three fundamental principles of antibody repertoire architecture across B-cell development: reproducibility, robustness and redundancy. Reproducibility of network structure explains clonal expansion and selection. Robustness ensures a functional immune response even under extensive loss of clones (50%). Redundancy in mutational pathways suggests that there is a pre-programmed evolvability in antibody repertoires. Our analysis provides guidelines for a quantitative network analysis of antibody repertoires, which can be applied to other facets of adaptive immunity (e.g., T cell receptors), and may direct the construction of synthetic repertoires for biomedical applications.

systems biology↗

bioRxiv2017

Windows of opportunity for Ebola virus infection treatment and vaccination

Ebola virus (EBOV) infection causes a high death toll, killing a high proportion of EBOV infected patients within 7 days. Comprehensive data on EBOV infection are very fragmented, hampering efforts in developing therapeutics and vaccines against EBOV. Under this circumstance, mathematical models become valuable resources to explore potential controlling strategies. In this paper, we employed experimental data of EBOV-infected nonhuman primates (NHPs) to construct a mathematical framework for determining windows of opportunity for treatment and vaccination. Considering a prophylactic vaccine based on recombinant vesicular stomatitis virus expressing the EBOV glycoprotein (VSV-EBOV), we found that the time window can be subject-specific, but vaccination could be protective if a subject is vaccinated during a period from one week to four months before infection. For the case of a therapeutic vaccine based on monoclonal antibodies (mAbs), a single dose might resolve the invasive EBOV replication even it was administrated as late as four days after infection. Our mathematical models can be used as building blocks for developing therapeutic and vaccine modalities as well as for evaluating public health intervention strategies in outbreaks. Future laboratory experiments will help to validate and refine the estimates of the windows of opportunity proposed here.

systems biology↗

bioRxiv2017

A measure of agreement to distinguish between condition levels: Reproducibility of co-expression networks across tissues

There is great interest to study how co-expression gene networks change across tissues. However, the reproducibility assessment of these studies is challenged by a lack of fully confirmatory experiments from independent researchers. While an increment in the number of studies with expression data for several tissues is expected, statistical measures are still needed to assess the reproducibility between studies. We identified a gap in the statistical literature concerning the assessment of agreement between studies that independently measure the effect of a varying condition on a population sample. The gap precluded us to test, using standard statistics, the level of agreement between the GTEX (RNAseq) and BRAINEAC (microarray) studies to distinguish the structure of co-expression networks across four brain tissues. We, therefore, generalized a classical measure of agreement, Cohens{kappa} , derived its distributional characteristics and determined its reliability properties. In the gene expression studies, we found full agreement of genome-wide networks in BRAINEAC benchmarked against GTEX, and highest agreement for brain specific pathways. Our highly interpretable measure can contribute to anticipated efforts on reproducibility research.

systems biology↗

bioRxiv2017

High Cooperativity In Negative Feedback Can Amplify Noisy Gene Expression

Burst-like synthesis of protein is a significant source of cell-to-cell variability in protein levels. Negative feedback is a common example of a regulatory mechanism by which such stochasticity can be controlled. Here we consider a specific kind of negative feedback, which makes bursts smaller in the excess of protein. Increasing the strength of the feedback may lead to dramatically different outcomes depending on a key parameter, the noise load, which is defined as the squared coefficient of variation the protein exhibits in the absence of feedback. Combining stochastic simulation with asymptotic analysis, we identify a critical value of noise load: for noise loads smaller than critical, the coefficient of variation remains bounded with increasing feedback strength; contrastingly, if the noise load is larger than critical, the coefficient of variation diverges to infinity in the limit of ever greater feedback strengths. Interestingly, high-cooperativity feedbacks have lower critical noise loads, implying that low-cooperativity feedbacks in burst size can be preferable for noisy proteins. Finally, we discuss our findings in the context of previous results on the impact of negative feedback in burst size and burst frequency on gene-expression noise.

systems biology↗