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Online ISSN 1751-8857 Print ISSN 1751-8849

IET Systems Biology

Volume 1, Issue 1, January 2007

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Volume 1, Issue 1

January 2007

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Editorial

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    • Reduction of nonlinear dynamic systems with an application to signal transduction pathways
      • Author(s): V. Petrov ; E. Nikolova ; O. Wolkenhauer
      • Source: IET Systems Biology, Volume 1, Issue 1, p. 2 –9
      • DOI:  10.1049/iet-syb:20050030
      • Type: Article
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      • p. 2 –9 (8)
        Mathematical modelling of kinetic processes with different time scales allows a reduction of the governing equations using quasi-steady-state approximations (QSSA). A QSSA theorem is applied to a mathematical model of the influence that Raf kinase inhibitor protein (RKIP) has on the ERK signalling pathway. On the basis of previously published parameter values, the system of 11 ordinary differential equations is rewritten in a form suitable for model reduction. In accordance with the terminology of the QSSA theorem, it is established that four of the protein and protein-complex concentrations are ‘fast varying’, such that the corresponding kinetic equations form an attached system. Another concentration is ‘medium varying’ such that the corresponding equation is reduced with respect to the four fast ones. The other six concentrations are ‘slow varying’, which means the corresponding kinetic equations also present a reduced system with respect to the others. Analytical solutions, relating the steady-state values of the fast varying protein concentrations and the slow varying ones, are derived and interpreted as restrictions on the regulatory role of RKIP on ERK-pathway.
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      Dynamic measure of gene co-regulation
      • Author(s): L. Farina ; A. De Santis ; G. Morelli ; I. Ruberti
      • Source: IET Systems Biology, Volume 1, Issue 1, p. 10 –17
      • DOI:  10.1049/iet-syb:20060031
      • Type: Article
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      • p. 10 –17 (8)
        Gene expression is to a large extent controlled at the level of mRNA accumulation. Genes whose products function together are likely under a common regulatory system (e.g. signal transduction pathways, sets of regulatory proteins) such that they are expressed in a coordinated manner. This property has been frequently used in the analysis of genome-wide expression data, as the experimental observation that a group of genes is co-expressed frequently implies that the genes share a common regulatory mechanism. The authors have investigated the situation in which dissimilarity in gene‐expression time profiles may still result from the presence of the same regulatory signal, as in the case of common transcription factors. To this aim, a dynamic model that takes into account the effect of specific mRNA degradation on the shape of gene-expression time series has been developed, and the concept of ‘dynamically co-regulated’ genes has accordingly been introduced as the goodness‐of‐fit to such a model (called dynamic R2). The statistical analysis of dynamic R2 over a number of different experimental data sets and organisms shows that the presence of dynamically co-regulated genes is by far more significant than that expected from the randomised data. Furthermore, as an example of the usefulness of the proposed method, genome-wide yeast measurements such as cell-cycle time series and transcription factors targets data, were used to prove that dynamic co-regulation is statistically related to the presence of common transcription factor(s). This latter property is very useful when trying to infer computational indications of co-regulation for not-yet annotated genes that do not display a co-expression pattern.
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      Modelling codependence in biological systems
      • Author(s): J.J. Mandel ; N.M. Palfreyman ; W. Dubitzky
      • Source: IET Systems Biology, Volume 1, Issue 1, p. 18 –32
      • DOI:  10.1049/iet-syb:20060002
      • Type: Article
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      • p. 18 –32 (15)
        A central aim of systems biology is to elucidate the complex dynamic structure of biological systems within which functioning and control occur. The success of this endeavour requires a dialogue between the two quite distinct disciplines of life science and systems theory, and so drives the need for graphical notations which facilitate this dialogue. Several methods have been developed for modelling and simulating biochemical networks, some of which provide notations for graphicall4y constructing a model. Such notations must support the full panoply of mechanisms of systems biology, including metabolic, regulatory, signalling and transport processes. Notations in systems biology tend to fall into two groups. The first group derives its orientation from conventional biochemical pathway diagrams, and so tends to ignore the role of information processing. The second group focuses on the processing of information, incorporating information-processing ideas from other systems-oriented disciplines, such as engineering and business. This, however, can lead to the two crucial and related difficulties of impedance mismatch and conceptual baggage. Impedance mismatch concerns the rift between non-biological notations and biological reality, which forces the researcher to employ awkward workarounds when modelling uniquely biological mechanisms. Conceptual baggage can arise when, for instance, an engineering notation is adapted to cater for these distinctively biological needs, since these adaptations will, typically, never completely free the notation of the conceptual structure of its original engineering motivation. A novel formalism, codependence modelling, which seeks to combine the needs of the biologist with the mathematical rigour required to support computer simulation of dynamics is proposed here. The notion of codependence encompasses the transformation of both chemical substance and information, thus integrating both metabolic and gene regulatory processes within a single conceptual schema.
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      Coordination of crosstalk between MAPK–PKC pathways: an exploratory study
      • Author(s): S.N. Sreenath ; R. Soebiyanto ; M.D. Mesarovic ; O. Wolkenhauer
      • Source: IET Systems Biology, Volume 1, Issue 1, p. 33 –40
      • DOI:  10.1049/iet-syb:20050085
      • Type: Article
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      • p. 33 –40 (8)
        Networks of signaling pathways provide a robust mechanism for cells to respond to various biological stimuli. Cell adaptation through the viewpoint of an organising principle between two interconnected pathways – mitogen-activated protein kinase (MAPK) and protein kinase C (PKC) is demonstrated. A multilevel system representation of the pathways is used to determine the pathway components contributing to the adaptive behaviour and coordination. The adaptation can be thought of as being manifested by a change in parameters of the coordinator. In silico experiments are conducted using MAPK–PKC mathematical model in the literature, which is modularised using biological functionality. Through extensive, guided parametric in silico experiments, the PLA2 subsystem is shown to be a coordinator. Results show that varying parameters of the coordinator not only activate the network of pathways where otherwise the pathway activity is very low, but also reveal the ability of the system to activate itself in the absence of the input, indicating relevance of the principle of bounded autonomy.
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      Gene networks reconstruction and time-series prediction from microarray data using recurrent neural fuzzy networks
      • Author(s): I.A. Maraziotis ; A. Dragomir ; A. Bezerianos
      • Source: IET Systems Biology, Volume 1, Issue 1, p. 41 –50
      • DOI:  10.1049/iet-syb:20050107
      • Type: Article
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      • p. 41 –50 (10)
        Reverse engineering problems concerning the reconstruction and identification of gene regulatory networks through gene expression data are central issues in computational molecular biology and have become the focus of much research in the last few years. An approach has been proposed for inferring the complex causal relationships among genes from microarray experimental data, which is based on a novel neural fuzzy recurrent network. The method derives information on the gene interactions in a highly interpretable form (fuzzy rules) and takes into account the dynamical aspects of gene regulation through its recurrent structure. To determine the efficiency of the proposed approach, microarray data from two experiments relating to Saccharomyces cerevisiae and Escherichia coli have been used and experiments concerning gene expression time course prediction have been conducted. The interactions that have been retrieved among a set of genes known to be highly regulated during the yeast cell-cycle are validated by previous biological studies. The method surpasses other computational techniques, which have attempted genetic network reconstruction, by being able to recover significantly more biologically valid relationships among genes.
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      Understanding ZHENG in traditional Chinese medicine in the context of neuro-endocrine-immune network
      • Author(s): S. Li ; Z.Q. Zhang ; L.J. Wu ; X.G. Zhang ; Y.D. Li ; Y.Y. Wang
      • Source: IET Systems Biology, Volume 1, Issue 1, p. 51 –60
      • DOI:  10.1049/iet-syb:20060032
      • Type: Article
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      • p. 51 –60 (10)
        Traditional Chinese medicine uses ZHENG as the key pathological principle to understand the human homeostasis and guide the applications of Chinese herbs. Here, a systems biology approach with the combination of computational analysis and animal experiment is used to investigate this complex issue, ZHENG, in the context of the neuro-endocrine-immune (NEI) system. By using the methods of literature mining, network analysis and topological comparison, it is found that hormones are predominant in the Cold ZHENG network, immune factors are predominant in the Hot ZHENG network, and these two networks are connected by neuro-transmitters. In addition, genes related to Hot ZHENG-related diseases are mainly present in the cytokine–cytokine receptor interaction pathway, whereas genes related to both the Cold-related and Hot-related diseases are linked to the neuroactive ligand-receptor interaction pathway. These computational findings were subsequently verified by experiments on a rat model of collagen-induced arthritis, which indicate that the Cold ZHENG-oriented herbs tend to affect the hub nodes in the Cold ZHENG network, and the Hot ZHENG-oriented herbs tend to affect the hub nodes in the Hot ZHENG network. These investigations demonstrate that the thousand-year-old concept of ZHENG may have a molecular basis with NEI as background.
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