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Results for “Systems Biology”

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bioRxiv2018

The application of text mining algorithms in summarizing trends in anti-epileptic drug research

1.Content summarization is an important area of research in traditional data mining. The volume of studies published on anti-epileptic drugs (AED) has increased exponentially over the last two decades, making it an important area for the application of text mining based summarization algorithms. In the current study, we use text analytics algorithms to mine and summarize 10,000 PubMed abstracts related to anti-epileptic drugs published within the last 10 years. A Text Frequency - Inverse Document Frequency based filtering was applied to identify drugs with highest frequency of mentions within these abstracts. The US Food and Drug database was scrapped and linked to the results to quantify the most frequently mentioned modes of action and elucidate the pharmaceutical entities marketing these drugs. A sentiment analysis model was created to score the abstracts for sentiment positivity or negativity. Finally, a modified Latent Dirichlet Allocation topic model was generated to extract key topics associated with the most frequently mentioned AEDs. Results of this study provide accurate and data intensive insights on the progress of anti-epileptic drug research.

systems biology↗

bioRxiv2018

Controlling insulin resistance through modulation of interplay between mediators of cellular senescence: A mathematical study

Obesity, metabolic syndrome and premature ageing form a hugely researched and discussed area of interest these days. In the pathology of this cluster of conditions, adipose tissue is gaining attention as a major playground for interplay between metabolic stress, inflammation and accelerated ageing, and not merely being an energy storage tank. Drastic elevation in the levels of reactive oxygen species, due to lipid overload and excessive lipolysis, causes genotoxic damage such as shortening of telomeres (an indicator of accelerated cell ageing), increased mRNA and protein expression for p53, p21, TNF-, IL-6 (interleukin 6), impaired insulin mediated glucose uptake, and decreased TERT mRNA expression. The increase of p53 in adipocytes is deleterious, since elevated p53 results in pre-mature ageing of fat tissues which secrete pro-inflammatory cytokines thereby contributing to insulin resistance. But inhibition of p53 as a therapeutic target, as suggested by many previous studies, could result in developing high risks of cancer. The association between hyperlipidemic/hyperglycaemic stress, premature growth arrest and insulin resistance, thus, forms an interesting premise for searching targets and designing interventions for therapy of metabolic syndrome, type 2 diabetes mellitus etc. We developed a mathematical study involving a 5D ODE model, which revealed crucial parametric conditions governing p53 dynamics in the case of metabolic stress-induced cellular senescence, and shed light on potential strategies to reduce pro-inflammatory cytokine levels which exacerbate insulin resistance through premature cellular ageing.\n\nHighlightsO_LIp53 oscillations signify DNA repair, and persistent stress causes prolonged surge in p53 levels inducing cellular senescence.\nC_LIO_LIp53-induced cellular senescence promotes inflammation and enhances progression of insulin resistance.\nC_LIO_LIRegulation of Mdmx and Akt can be a strategy to rejuvenate ageing cells through management of IL-6 dynamics.\nC_LI

systems biology↗

bioRxiv2018

Probing the network structure of health deficits in human aging

Human aging leads to the stochastic accumulation of damage. We model an aging population using a stochastic network model. Individuals are modeled as a network of interacting nodes, representing health attributes. Nodes in the network stochastically damage and repair, with rates dependent on the state of their neighbors. Damaged nodes represent health deficits. The Frailty Index (FI) assesses age-related damage as the proportion of health deficits an individual has accumulated, from a selection of attributes. Here, we use computational, information-theoretic, and mean-field approaches to show that the degree distribution and degree correlations of the underlying network are important to the models ability to recover the behavior of observational health data. We use different measures of damage in the network to probe the structure of the network. We find that the behavior of different classes of observational health deficits (laboratory or clinical) is similar to the behavior of nodes of low or high degree in the model, respectively. This explains how damage can propagate within the network, leading towards individual mortality.

systems biology↗

bioRxiv2018

Rules for PP2A-controlled phosphosignalling and drug responses

Systemic understanding of protein phosphatase 2A (PP2A)-regulated cellular processes is still at infancy. Here, we present mass-spectrometry analysis of phospho-targets (dephosphorylome) regulated by PP2A modulation. In addition to PP2A-regulated processes and targets, the data reveal important general concepts and rules related to PP2A-mediated phosphoregulation. These include the unidirectionality paradigm of regulation of phosphorylation, and differential spatial distribution of kinase-and phosphatase-dominated phosphotargets. Data also present first systemic analysis of targets of PP2A-modulating oncoproteins, CIP2A, PME-1, and SET; including targets via which PP2A may coordinately regulate activities of cancer drivers and tumor suppressors such as MYC or TP53. To validate functional utility of this dataset, PP2A dephosphorylome activity was correlated with cancer cell responses to over 300 drugs. Notably, we find that cancer therapy responses can be broadly classified based on PP2A dephosphorylome activity, both in quantitative and qualitative manner. In summary, our data characterize rules by which PP2A coordinate cancer cell phosphosignaling and drug responses. The results also may also direct the use of emerging pharmacological approaches for PP2A activity modulation in human diseases.

systems biology↗

bioRxiv2018

Active degradation of a regulator controls coordination of downstream genes

Several key transcription factors have unusually short half-lives compared to other cellular proteins. Here, we explore the utility of active degradation in shaping how a master regulator coordinates its downstream targets. We focus our studies on the multi-antibiotic resistance activator MarA, which controls a variety of stress response genes in Escherichia coli. We modify its half-life either by knocking down the protease that targets it via CRISPRi or by engineering MarA to protect it from degradation. Our experimental, analytical, and computational results indicate that active degradation can impact both the rate of coordination and the maximum coordination that downstream genes can achieve. Trade-offs between these properties show that perfect information fidelity and instantaneous coordination cannot coexist.

systems biology↗

bioRxiv2018

COMPUTING SIGNAL TRANSDUCTION IN SIGNALING NETWORKS MODELED AS BOOLEAN NETWORKS, PETRI NETS AND HYPERGRAPHS

Mathematical frameworks circumventing the need of mechanistic detail to build models of signal transduction networks include graphs, hypergraphs, Boolean Networks, and Petri Nets. Predicting how a signal transduces in a signaling network is essential to understand cellular functions and disease. Different formalisms exist to describe how a signal transduces in a given intracellular signaling network represented in the aforementioned modeling frameworks: elementary signaling modes, T-invariants, extreme pathway analysis, elementary flux modes, and simple paths. How do these formalisms compare?\n\nWe present an overview of how signal transduction networks have been modelled using graphs, hypergraphs, Boolean Networks, and Petri Nets in the literature. We provide a review of the different formalisms for capturing signal transduction in a given model of an intracellular signaling network. We also discuss the existing translations between the different modeling frameworks, and the relationships between their corresponding signal transduction representations that have been described in the literature. Furthermore, as a new formalism of signal transduction, we show how minimal functional routes proposed for signaling networks modeled as Boolean Networks can be captured by computing topological factories, a methodology found in the metabolic networks literature. We further show that in the case of signaling networks represented with an acyclic B-hypergraph structure, the definitions are equivalent. In signaling networks represented as directed graphs, it has been shown that computations of elementary modes via its incidence matrix correspond to computations of simple paths and feedback loops. We show that computing elementary modes based on the incidence matrix of a B-hypergraph fails to capture minimal functional routes.

systems biology↗

bioRxiv2018

Characterizing glutamate receptor genes of Rat having vertebrate nervous system and Arabidopsis thaliana a plant having equivalent nervous system based on chemical properties of amino acids and investigating evolutionary relationships between them.

iGluR gene family of a vertebrate, Rat and AtGLR gene family of a plant, Arabidopsis thaliana [4] perform some common functionalities in neuro-transmission, which have been compared quantitatively. Our attempt is based on the chemical properties of amino acids [6, 7, 8] comprising the primary protein sequences of the aforesaid genes. 19 AtGLR genes of length varying from 808 amino acid (aa) to 1039 aa and 16 iGluR genes length varying from 902aa to 1482 aa have been taken as data sets. Thus, we detected the commonalities (conserved elements) during the long evolution of plants and animals from a common ancestor [4]. Eight different conserved regions have been found based on individual amino acids. Two different conserved regions are also found, which are based on chemical groups of amino acids. We have tried too to find different possible patterns which are common throughout the data set taken. 9 such patterns have been found with size varying from 2 to 5 amino acids at different regions in each primary protein sequences. Phylogenetic trees of AtGLR and iGluR families have also been constructed. This approach is likely to shed light on the long course of evolution.

systems biology↗

bioRxiv2018

A Comprehensive Mathematical Model of Surface Electromyography and Force Generation

The purpose of this article is to provide a unified description of a comprehensive mathematical model of surface electromyographic (EMG) measurements and the corresponding force signal in skeletal muscles. The model comprises motor unit pool organization, recruitment and rate coding, intracellular action potential generation and the resulting EMG measurements, as well as the generated muscular force during voluntary isometric contractions. It consolidates and extends the results of several previous publications that proposed mathematical models for the individual model components. A parameterization of the electrical and mechanical components of the model is proposed that ensures a physiologically meaningful EMG-force relation in the simulated signals. Moreover, a novel nonlinear transformation of the excitation model input is proposed, which ensures that the model force output equals the desired target force. Finally, an alternative analytical formulation of the EMG model is proposed, which renders the physiological meaning of the model more clear and facilitates a mathematical proof that muscle fibers in this model at no point in time represent a net current source or sink.\n\nNeuromuscular physiology is a vibrant research field that has recently seen exciting advances. Many previous publications have focused on thorough analyses of particular aspects of neuromuscular physiology, yet an integration of the various novel findings into a single, comprehensive model is missing. A consistent description of a complete physiological model as presented here, including thorough justification of model component choices, will facilitate the use of these advanced models in future research. Results of a numerical simulation highlight the models capability to reproduce many physiological effects observed in experimental measurements, and to produce realistic synthetic data that are useful for the validation of signal processing algorithms. The model is based on recent advances in the understanding of muscular physiology and hence also applicable for analyzing the influence of various physiological and measurement setup parameters on the measured force and EMG signals.

systems biology↗

bioRxiv2018

Integrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes

Rett syndrome (RTT) is a rare disorder causing severe intellectual and physical disability. The cause is a mutation in the gene coding for the methyl-CpG binding protein 2 (MECP2), a multifunctional regulator protein. Purpose of the study was integration and investigation of multiple gene expression profiles in human cells with impaired MECP2 gene to obtain a data-driven insight in downstream effects. Information about changed gene expression was extracted from five previously published studies. We identified a set of genes which are significantly changed not in all but several transcriptomics datasets and were not mentioned in the context of RTT before. Using overrepresentation analysis of molecular pathways and gene ontology we found that these genes are involved in several processes and molecular pathways known to be affected in RTT. Integrating transcription factors we identified a possible link how MECP2 regulates cytoskeleton organization via MEF2C and CAPG. Integrative analysis of omics data and prior knowledge databases is a powerful approach to identify links between mutation and phenotype especially in rare disease research where little data is available.\n\nAbbreviationsRett syndrome (RTT), embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), fold change (FC), Gene Ontology (GO), EIF (eukaryotic initiation of transcription factor)\n\nFor genes the symbols according to the HGNC nomenclature were used.

systems biology↗

bioRxiv2018

MoDentify: a tool for phenotype-driven module identification in multilevel metabolomics networks

SummaryMetabolomics is an established tool to gain insights into (patho)physiological outcomes. Associations of metabolism with such outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites, which is an aspect that has not been addressed by previous methods. Here we present MoDentify, a freely available R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct visualization of results as interactive networks in Cytoscape. We present an application example using a complex, multifluid metabolomics dataset. Owing to its generic character, the method is widely applicable to any dataset with a phenotype variable, a data matrix, and optional pathway annotations.\n\nAvailability and ImplementationMoDentify is freely available from GitHub: https://github.com/krumsiek/MoDentify\n\nThe package vignette contains a detailed tutorial of the analysis workflow.\n\nContactjan.krumsiek@helmholtz-muenchen.de

systems biology↗

bioRxiv2018

Bistable emergence of oscillations in structured cell populations

Biofilm communities of Bacillus subtilis bacteria have recently been shown to exhibit collective growth-rate oscillations mediated by electrochemical signaling to cope with nutrient starvation. These oscillations emerge once the colony reaches a large enough number of cells. However, it remains unclear whether the amplitude of the oscillations, and thus their effectiveness, builds up over time gradually, or if they can emerge instantly with a non-zero amplitude. Here we address this question by combining microfluidics-based time-lapse microscopy experiments with a minimal theoretical description of the system in the form of a delay-differential equation model. Analytical and numerical methods reveal that oscillations arise through a subcritical Hopf bifurcation, which enables instant high amplitude oscillations. Consequently, the model predicts a bistable regime where an oscillating and a non-oscillating attractor coexist in phase space. We experimentally validate this prediction by showing that oscillations can be triggered by perturbing the media conditions, provided the biofilm size lies within an appropriate range. The model also predicts that the minimum size at which oscillations start decreases with stress, a fact that we also verify experimentally. Taken together, our results show that collective oscillations in cell populations can emerge suddenly with non-zero amplitude via a discontinuous transition.

systems biology↗

bioRxiv2018

Central dogma rates and the trade-off between precision and economy

Steady-state protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. A given protein abundance can be obtained from infinitely many combinations of these rates. This raises the question of whether the natural rates for each gene result from historical accidents, or are there rules that give certain combinations a selective advantage? We address this question using high-throughput measurements in rapidly growing cells from diverse organisms to find that about half of the rate combinations do not exist: genes that combine high transcription with low translation are strongly depleted. This depletion is due to a trade-off between precision and economy: high transcription decreases stochastic fluctuations but increases transcription costs. Our theory quantitatively explains which rate combinations are missing, and predicts the curvature of the fitness function for each gene. It may guide the design of gene circuits with desired expression levels and noise.

systems biology↗

bioRxiv2018

Gene regulatory network analysis identifies sex-linked differences in colon cancer drug metabolism processes

Significant sex differences are observed in colon cancer, and understanding these differences is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. We used both transcript-based and gene regulatory network methods to analyze RNA-Seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and found no significant sex differences except for sex-chromosome genes. We then inferred patient-specific gene regulatory networks, and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset that included 1,193 patients from five independent studies. While targeting of the drug metabolism pathway did not change the overall survival for males treated with adjuvant chemotherapy, females with greater targeting had an increase in 10-year overall survival probability, with 89% (95% CI: 78%-100%) survival compared to 61% (95% CI: 45%-82%) for women with lower targeting, respectively (p=0.034). Our network analysis uncovered patterns of transcriptional regulation that differentiate male and female colon cancer. Most importantly, targeting of the drug metabolism pathway was predictive of survival in women who received adjuvant chemotherapy. This network-based approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.

systems biology↗

bioRxiv2018

Microbiome inhibition of IRAK-4 by trimethylamine mediates metabolic and immune benefits in high-fat-diet-induced insulin resistance

The global type 2 diabetes epidemic is a major health crisis and there is a critical need for innovative strategies to fight it. Although the microbiome plays important roles in the onset of insulin resistance (IR) and low-grade inflammation, the microbial compounds regulating these phenomena remain to be discovered. Here, we reveal that the microbiome inhibits a central kinase, eliciting immune and metabolic benefits. Through a series of in vivo experiments based on choline supplementation, blocking trimethylamine (TMA) production then administering TMA, we demonstrate that TMA decouples inflammation and IR from obesity in the context of high-fat diet (HFD) feeding. Through in vitro kinome screens, we reveal TMA specifically inhibits Interleukin-1 Receptor-associated Kinase 4 (IRAK4), a central kinase integrating signals from various toll-like receptors and cytokine receptors. TMA blunts TLR4 signalling in primary human hepatocytes and peripheral blood monocytic cells, and improves mouse survival after a lipopolysaccharide-induced septic shock. Consistent with this, genetic deletion and chemical inhibition of IRAK4 result in similar metabolic and immune improvements in HFD. In summary, TMA appears to be a key microbial compound inhibiting IRAK4 and mediating metabolic and immune effects with benefits upon HFD. Thereby we highlight the critical contribution of the microbial signalling metabolome in homeostatic regulation of host disease and the emerging role of the kinome in microbial-mammalian chemical crosstalk.

systems biology↗

bioRxiv2018

Time Dependent Stochastic mRNA and Protein Synthesis in Piecewise-deterministic Models of Gene Networks.

We discuss piecewise-deterministic approximations of gene networks dynamics. These approximations capture in a simple way the stochasticity of gene expression and the propagation of expression noise in networks and circuits. By using partial omega expansions, piecewise deterministic approximations can be formally derived from the more commonly used Markov pure jump processes (chemical master equation). We are interested in time dependent multivariate distributions that describe the stochastic dynamics of the gene networks. This problem is difficult even in the simplified framework of piecewise-determinisitic processes. We consider three methods to compute these distributions: the direct Monte-Carlo, the numerical integration of the Liouville-master equation and the push-forward method. This approach is applied to multivariate fluctuations of gene expression, generated by gene circuits. We find that stochastic fluctuations of the proteome and much less those of the transcriptome can discriminate between various circuit topologies.

systems biology↗

bioRxiv2018

Comparative Transcriptomic and Metabolomic Analyses in Response to Cold in Tartary Buckwheat (Fagopyrum tataricum)

Plants recognize multiple environmental signals that lead to substantial changes in the regulation of primary and secondary metabolism in order to adapt to environmental stresses. In this study, we investigate the effects of cold on the metabolome and transcriptome of tartary buckwheat, focusing on the phenylpropanoid biosynthetic pathway. Using RNA-sequencing analysis of the cold-regulated transcriptome of buckwheat, we identified several phenylpropanoid biosynthetic transcripts that accumulated in response to cold. To confirm the transcriptome data, we analyzed the expression of the phenylpropanoid biosynthetic transcripts in cold-treated buckwheat and showed that all the phenylpropanoid biosynthetic transcripts were upregulated in coldtreated buckwheat seedlings with the exception of FtDFR. From the metabolic profiling based on the GC-TOF-MS analysis, we show that most of the sugars and their derivatives significantly increase in response to cold, while some of amino acids and their derivatives decrease in cold-treated plants. Some organic acids derived from the tricarboxylic acid (TCA) cycle increased in the cold-treated plants compared with in the plants grown at 25{degrees}C. In particular, the contents of anthocyanins and proanthocyanidins were significantly increased by cold treatment. In summary, these results indicate that the metabolome and transcriptome of tartary buckwheat are extensively affected by cold stresses.\n\nHighlightUsing the RNA-sequencing data and the metabolic profiling analysis, we identified the changes that occur in the transcriptome and metabolome of tartary buckwheat in response to cold, focusing on the flavonoid biosynthetic pathway.\n\nAbbreviations

systems biology↗

bioRxiv2018

Binding Affinity Regression Models from Repeats Mutation in Polyglutamine Disease

Diagnosing and curing neurodegenarative disorder such as spinocerebellar ataxia is complicated when there is differences in formation of protein sequences and structures. Affinity prediction plays vital role to identify drugs for various genetic disorders. Spinocerebellar ataxia occurs but mainly it occurs due to polyglutamine repeats. This research work aims in predicting the affinity of spinocerebellar ataxia from the protein complexes by extracting the well-defined descriptors. Regression models are built to predict the affinity through machine learning techniques coded in python using the Scikit-Learn framework. Energy complexes and protein sequence descriptors are defined and extracted from the complex and sequences. Results show that the SVR is found to predict the affinity with high accuracy of 98% for spinocerebellar ataxia. This paper also deliberates the results of statistical learning carried out with the same set of complexes with various regression techniques.

systems biology↗

bioRxiv2018

Automated high-quality reconstruction of metabolic networks from high-throughput data

While new genomes are sequenced at ever increasing rates, their phenotypic analysis remains a major bottleneck of biomedical research. The generation of genome-scale metabolic models capable of accurate phenotypic predictions is a labor-intensive endeavor; accordingly, such models are available for only a small percentage of sequenced species. The standard metabolic reconstruction process starts from a (semi-)automatically generated draft model, which is then refined through extensive manual curation. Here, we present a novel strategy suitable for full automation, which exploits high-throughput gene knockout or nutritional growth data. We test this strategy by reconstructing accurate genome-scale metabolic models for three strains of Streptococcus, a major human pathogen. The resulting models contain a lower proportion of reactions unsupported by genomic evidence than the most widely used E. coli model, but reach the same accuracy in terms of knockout prediction. We confirm the models predictive power by analyzing experimental data for auxotrophy, additional nutritional environments, and double gene knockouts, and we generate a list of potential drug targets. Our results demonstrate the feasibility of reconstructing high-quality genome-scale metabolic models from high-throughput data, a strategy that promises to massively accelerate the exploration of metabolic phenotypes.\n\nSignificance statementReading bacterial genomes has become a cheap, standard laboratory procedure. A genome by itself, however, is of little information value - we need a way to translate its abstract letter sequence into a model that describes the capabilities of its carrier. Until now, this endeavor required months of manual work by experts. Here, we show how this process can be automated by utilizing high-throughput experimental data. We use our novel strategy to generate highly accurate metabolic models for three strains of Streptococcus, a major threat to human health.

systems biology↗