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Systems Biology, IET

Issue 5 • Date September 2009

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Displaying Results 1 - 13 of 13
  • Editorial: Selected papers from the Second q-bio Conference on Cellular Information Processing

    Page(s): 297 - 299
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (93 KB)  

    This special issue consists of 12 original papers that elaborate on work presented at The Second q-bio Conference on Cellular Information Processing, which was held on the campus of St. John??s College in Santa Fe, New Mexico, USA, 6-9 August 2008. View full abstract»

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  • Simple model of the transduction of cell-penetrating peptides

    Page(s): 300 - 306
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (284 KB)  

    Cell-penetrating peptides (CPPs) such as HIV's trans-activating transcriptional activator (TAT) and polyarginine rapidly pass through the plasma membranes of mammalian cells by an unknown mechanism called transduction. They may be medically useful when fused to well-chosen chains of fewer than about 35 amino acids. The author offers a simple model of transduction in which phosphatidylserines and CPPs effectively form two plates of a capacitor with a voltage sufficient to cause the formation of transient pores (electroporation). The model is consistent with experimental data on the transduction of oligoarginine into mouse C2C12 myoblasts and makes three testable predictions. View full abstract»

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  • Quantitative statistical description of integrin clusters in adherent cells

    Page(s): 307 - 316
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (591 KB)  

    Regulation of protein-protein interactions because of their spatial organisation in cells often shapes cell signalling responses to external stimuli, yet most current cell signalling models do not include spatial segregation of proteins beyond coarse control volumes like the cytosol or nucleus. A significant hindrance to spatial modelling of cell signalling is a lack of data describing the spatial organisation of proteins in cells. One signalling system in which spatial organisation is critical is integrin signalling, where protein interactions are restricted to small, micron-sized protein complexes that form on clusters of transmembrane integrin proteins. Using confocal microscopy and image analysis to quantify the size, shape and location of integrin clusters, the authors observed that cells exhibit large variability in these integrin cluster properties. To describe these heterogeneous populations of clusters quantitatively, the authors identified appropriate probability models to characterise the size, shape and location of integrin clusters in a population of adherent cells. The authors determined that integrin cluster sizes are lognormally distributed, integrin cluster eccentricities are beta distributed, and the distances of integrin clusters from the cell centre are gamma distributed. The authors estimated the parameters corresponding to these probability models from empirical data describing integrin clusters in a population of cells, and the resulting probability models describe the heterogeneous populations of clusters. These population models provide the means to create an accurate mathematical description of spatially localised integrin signalling compartments for use in computational models of integrin signalling. View full abstract»

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  • Integrating BioPAX pathway knowledge with SBML models

    Page(s): 317 - 328
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (679 KB)  

    Online databases store thousands of molecular interactions and pathways, and numerous modelling software tools provide users with an interface to create and simulate mathematical models of such interactions. However, the two most widespread used standards for storing pathway data (biological pathway exchange; BioPAX) and for exchanging mathematical models of pathways (systems biology markup language; SBML) are structurally and semantically different. Conversion between formats (making data present in one format available in another format) based on simple one-to-one mappings may lead to loss or distortion of data, is difficult to automate, and often impractical and/or erroneous. This seriously limits the integration of knowledge data and models. In this paper we introduce an approach for such integration based on a bridging format that we named systems biology pathway exchange (SBPAX) alluding to SBML and BioPAX. It facilitates conversion between data in different formats by a combination of one-to-one mappings to and from SBPAX and operations within the SBPAX data. The concept of SBPAX is to provide a flexible description expanding around essential pathway data - basically the common subset of all formats describing processes, the substances participating in these processes and their locations. SBPAX can act as a repository for molecular interaction data from a variety of sources in different formats, and the information about their relative relationships, thus providing a platform for converting between formats and documenting assumptions used during conversion, gluing (identifying related elements across different formats) and merging (creating a coherent set of data from multiple sources) data. View full abstract»

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  • Sensitivity analysis predicts that the ERK-pMEK interaction regulates ERK

    Page(s): 329 - 341
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (455 KB)  

    Following phosphorylation, nuclear translocation of the mitogen-activated protein kinases (MAPKs), ERK1 and ERK2, is critical for both gene expression and DNA replication induced by growth factors. ERK nuclear translocation has therefore been studied extensively, but many details remain unresolved, including whether or not ERK dimerisation is required for translocation. Here, we simulate ERK nuclear translocation with a compartmental computational model that includes systematic sensitivity analysis. The governing ordinary differential equations are solved with the backward differentiation formula and decoupled direct methods. To better understand the regulation of ERK nuclear translocation, we use this model in conjunction with a previously published model of the ERK pathway that does not include an ERK dimer species and with experimental measurements of nuclear translocation of wild-type ERK and a mutant form, ERK1-Delta4, which is unable to dimerise. Sensitivity analysis reveals that the delayed nuclear uptake of ERK1-Delta4 compared to that of wild-type ERK1 can be explained by the altered interaction of ERK1-Delta4 with phosphorylated MEK (MAPK/ERK kinase), and so may be independent of dimerisation. Our study also identifies biological experiments that can verify this explanation. View full abstract»

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  • Exploring mechanisms of oscillations in p53 and nuclear factor-κb systems

    Page(s): 342 - 355
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1051 KB)  

    A number of regulatory networks have the potential to generate sustained oscillations of irregular amplitude, but well conserved period. Single-cell experiments revealed that in p53 and nuclear factor (NF)-kappaB systems the oscillation period is homogenous in cell populations, insensitive to the strength of the stimulation, and is not influenced by the overexpression of p53 or NF-kappaB transcription factors. We propose a novel computational method of validation of molecular pathways models, based on the analysis of sensitivity of the oscillation period to the particular gene(s) copy number and the level of stimulation. Using this method, the authors demonstrate that existing p53 models, in which oscillations are borne at a saddle-node-on-invariant-circle or subcritical Hopf bifurcations (characteristic for systems with interlinked positive and negative feedbacks), are highly sensitive to gene copy number. Hence, these models cannot explain existing experiments. Analysing NF-kappaB system, the authors show the importance of saturation in transcription of the NF-kappaB inhibitor IkappaBkappa. Models without saturation predict that the oscillation period is a rapidly growing function of total NF-kappaB level, which is in disagreement with experiments. The study supports the hypothesis that oscillations of robust period are based on supercritical Hopf bifurcation, characteristic for dynamical systems involving negative feedback and time delay. We hypothesise that in the p53 system, the role of positive feedback is not sustaining oscillations, but terminating them in severely damaged cells in which the apoptotic programme should be initiated. View full abstract»

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  • Crosstalk between p53 and nuclear factor-κB systems: pro- and anti-apoptotic functions of NF-κB

    Page(s): 356 - 367
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1151 KB)  

    Nuclear factors p53 and NF-kappaB control many physiological processes including cell cycle arrest, DNA repair, apoptosis, death, innate and adaptive immune responses, and inflammation. There are numerous pathways linking these systems and there is a bulk of evidence for cooperation as well as for antagonisms between p53 and NF-kappaB. In this theoretical study, the authors use earlier models of p53 and NF-kappaB systems and construct a crosstalk model of p53-NF-kappaB network in order to explore the consequences of the two-way coupling, in which NF-kappaB upregulates the transcription of p53, whereas in turn p53 attenuates transcription of NF-kappaB inhibitors IkappaBalpha and A20. We consider a number of protocols in which cells are stimulated by tumour necrosis factor-alpha (TNFalpha) (that activates NF-kappaB pathway) and/or gamma irradiation (that activates p53 pathway). The authors demonstrate that NF-kappaB may have both anti- and pro-apoptotic roles. TNFalpha stimulation, preceding DNA damaging irradiation, makes cells more resistant to irradiation-induced apoptosis, whereas the same TNFalpha stimulation, when preceded by irradiation, increases the apoptotic cell fraction. The finding suggests that diverse roles of NF-kappaB in apoptosis and cancer could be related to the dynamical context of activation of p53 and NF-kappaB pathways. View full abstract»

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  • Evolution of gene auto-regulation in the presence of noise

    Page(s): 368 - 378
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (386 KB)  

    Auto-regulatory negative feedback loops, where the protein expressed from a gene inhibits its own expression are common gene network motifs within cells. We investigate when will introducing a negative feedback mechanism be beneficial in terms of increasing a fitness function that is given by the probability of maintaining protein numbers above a critical threshold. Our results show the existence of a trade-off as introducing feedback decreases the average number of protein molecules driving this number closer to the critical threshold (which decreases fitness) but also reduces stochastic fluctuations around the mean (which increases fitness). We provide analytical conditions under which a negative feedback mechanism can evolve, that is, introducing feedback will increase the above fitness. Analyses of these conditions show that negative feedbacks are more likely to evolve when (i) the source of noise in the protein population is extrinsic (i.e. noise is caused by fluctuations in exogenous signals driving the gene network) and not intrinsic (i.e. the randomness associated with mRNA/protein expression and degradation); (ii) the dynamics of the exogenous signal causing extrinsic noise is slower than the protein dynamics; and (iii) the critical threshold level for the protein number is low. We also show that mRNA/protein degradation rates are critical factors in determining whether transcription or translational negative feedback should evolve. In particular, when the mRNA half-life is much shorter than the protein's half-life, then a transcriptional negative feedback mechanism is more likely to evolve. On the other hand, a translational negative feedback mechanism is preferred with more stable mRNAs that have long half-lifes. View full abstract»

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  • Quantifying evolvability in small biological networks

    Page(s): 379 - 387
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (407 KB)  

    The authors introduce a quantitative measure of the capacity of a small biological network to evolve. The measure is applied to a stochastic description of the experimental setup of Guet et al. (Science 2002, 296, pp. 1466), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. The authors take an information-theoretic approach, allowing the system to set parameters that optimise signal processing ability, thus enumerating each network's highest-fidelity functions. All networks studied are highly evolvable by the measure, meaning that change in function has little dependence on change in parameters. Moreover, each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without completely losing it along the way. This property further underscores the evolvability of the networks. View full abstract»

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  • Interampatteness - a generic property of biochemical networks

    Page(s): 388 - 403
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (984 KB)  

    Analysis of gene expression data sets reveals that the variation in expression is concentrated to significantly fewer dasiacharacteristic modesdasia or dasiaeigengenesdasia than the number of both recorded assays and measured genes. Previous works have stressed the importance of these characteristic modes, but neglected the equally important weak modes. Herein a generic system property dasia interampatteness dasia is defined that explains the previous feature, and assigns equal weight to the characteristic and weak modes. An interampatte network is characterised by strong INTERactions enabling simultaneous AMPlification and ATTEnuation of different signals. It is postulated that biochemical networks are interampatte, based on published experimental data and theoretical considerations. Existence of multiple time-scales and feedback loops is shown to increase the degree of interampatteness. Interampatteness has strong implications for the dynamics and reverse engineering of the network. One consequence is highly correlated changes in gene expression in response to external perturbations, even in the absence of common transcription factors, implying that interampatte gene regulatory networks erroneously may be assumed to have co-expressed/co-regulated genes. Data compression or reduction of the system dimensionality using clustering, singular value decomposition, principal component analysis or some other data mining technique results in a loss of information that will obstruct reconstruction of the underlying network. View full abstract»

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  • Scalable learning of large networks

    Page(s): 404 - 413
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (809 KB)  

    Cellular networks inferred from condition-specific microarray data can capture the functional rewiring of cells in response to different environmental conditions. Unfortunately, many algorithms for inferring cellular networks do not scale to whole-genome data with thousands of variables. We propose a novel approach for scalable learning of large networks: cluster and infer networks (CIN). CIN learns network structures in two steps: (a) partition variables into smaller clusters, and (b) learn networks per cluster. We optionally revisit the cluster assignment of variables with poor neighbourhoods. Results on networks with known topologies suggest that CIN has substantial speed benefits, without substantial performance loss. We applied our approach to microarray compendia of glucose-starved yeast cells. The inferred networks had significantly higher number of subgraphs representing meaningful biological dependencies than random graphs. Analysis of subgraphs identified biological processes that agreed well with existing information about yeast populations under glucose starvation, and also implicated novel pathways that were previously not known to be associated with these populations. View full abstract»

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  • Identification of gene interactions in fungal-plant symbiosis through discrete dynamical system modelling

    Page(s): 414 - 428
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (712 KB)  

    Fungal-plant root associations involve nutrient exchanges, between the partners and the soil, particularly phosphate, that benefit both organisms. Discrete dynamical system (DDS) models are reconstructed to capture gene regulation in the arbuscular mycorrhizae Glomus versiforme-Medicago trunculata root symbiosis. Previously published time-course gene expression data derived from various days post-inoculation were clustered to identify genes co-regulated in mycorrhizal roots. Uncolonised roots grown with high phosphate provide a key nutritional control condition. First-order linear DDS models were created using a data-driven method to fit to the observed gene expression data. The result of the modelling constitutes active gene interactions in the regulatory network of the plant root at 8, 15, 22, 31 and 36 days post-inoculation. These genes are involved in basic metabolism, development, oxidative stress and defense pathways, and show consistent dynamic behaviours in the model. The functions of previously unannotated genes were further elucidated from the developed system maps. View full abstract»

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  • Mesoscopic statistical properties of multistep enzyme-mediated reactions

    Page(s): 429 - 437
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (291 KB)  

    Enzyme-mediated reactions may proceed through multiple intermediate conformational states before creating a final product molecule, and one often wishes to identify such intermediate structures from observations of the product creation. In this study, the authors address this problem by solving the chemical master equations for various enzymatic reactions. A perturbation theory analogous to that used in quantum mechanics allows the determination of the first (langnrang) and the second (sigma2) cumulants of the distribution of created product molecules as a function of the substrate concentration and the kinetic rates of the intermediate processes. The mean product flux V = dlangnrang / dt (or dasiadosedasiaresponsedasia curve) and the Fano factor F = sigma2/langnrang are both realistically measurable quantities, and whereas the mean flux can often appear the same for different reaction types, the Fano factor can be quite different. This suggests both qualitative and quantitative ways to discriminate between different reaction schemes, and the authors explore this possibility in the context of four sample multistep enzymatic reactions. Measuring both the mean flux and the Fano factor can not only discriminate between reaction types, but can also provide some detailed information about the internal, unobserved kinetic rates, and this can be done without measuring single-molecule transition events. View full abstract»

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IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches.

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