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Online ISSN 1751-8857 Print ISSN 1751-8849

IET Systems Biology

Volume 1, Issue 3, May 2007

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Volume 1, Issue 3

May 2007

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    • Reverse engineering of gene regulatory networks
      • Author(s): K.-H. Cho ; S.-M. Choo ; S.H. Jung ; J.-R. Kim ; H.-S. Choi ; J. Kim
      • Source: IET Systems Biology, Volume 1, Issue 3, p. 149 –163
      • DOI:  10.1049/iet-syb:20060075
      • Type: Article
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      • p. 149 –163 (15)
        Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided.
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      Linear matrix inequalities approach to reconstruction of biological networks
      • Author(s): C. Cosentino ; W. Curatola ; F. Montefusco ; M. Bansal ; D. di Bernardo ; F. Amato
      • Source: IET Systems Biology, Volume 1, Issue 3, p. 164 –173
      • DOI:  10.1049/iet-syb:20060054
      • Type: Article
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      • p. 164 –173 (10)
        The general problem of reconstructing a biological interaction network from temporal evolution data is tackled via an approach based on dynamical linear systems identification theory. A novel algorithm, based on linear matrix inequalities, is devised to infer the interaction network. This approach allows to directly taking into account, within the optimisation procedure, the a priori available knowledge of the biological system. The effectiveness of the proposed algorithm is statistically validated, by means of numerical tests, demonstrating how the a priori knowledge positively affects the reconstruction performance. A further validation is performed through an in silico biological experiment, exploiting the well-assessed cell-cycle model of fission yeast developed by Novak and Tyson.
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      S-system parameter estimation for noisy metabolic profiles using Newton-flow analysis
      • Author(s): Z. Kutalik ; W. Tucker ; V. Moulton
      • Source: IET Systems Biology, Volume 1, Issue 3, p. 174 –180
      • DOI:  10.1049/iet-syb:20060064
      • Type: Article
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      • p. 174 –180 (7)
        Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.
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      System-level analysis of tryptophan regulation in Escherichia coli – performance under starved and well-fed conditions
      • Author(s): N. Chaudhary ; S. Bhartiya ; K.V. Venkatesh
      • Source: IET Systems Biology, Volume 1, Issue 3, p. 181 –189
      • DOI:  10.1049/iet-syb:20060057
      • Type: Article
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      • p. 181 –189 (9)
        Biological systems respond appropriately to a variety of environments thus representing complex systems with rich physiological behaviour. Quantitative models can be used to identify the design components that result in the system complexity. In this work, the tryptophan system of Escherichia coli that synthesises tryptophan internally when faced with starvation in a rapid manner and shuts off the synthesis sluggishly when the cells are exposed to a medium replete with tryptophan has been discussed. The evolved regulatory design is capable of providing such an asymmetric response that represents an appropriate behaviour to ensure survival. The tryptophan system uses three distinct regulatory mechanisms namely genetic regulation, transcriptional attenuation and enzyme inhibition to achieve its goals. It has been shown that genetic repression and attenuation are the only active regulatory mechanisms during moderate and severe starvation. However, as the degree of starvation increases, repression is relieved prior to attenuation. The analysis also shows that enzyme inhibition does not play a role under severe starvation and plays a marginal role in increasing the rate of repression when the cells are exposed to well-fed conditions. Finally, we use tools from linear systems theory to rationalise the above observations based on the poles and zeros of an approximated linear system.
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      Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model
      • Author(s): F.P. Casey ; D. Baird ; Q. Feng ; R.N. Gutenkunst ; J.J. Waterfall ; C.R. Myers ; K.S. Brown ; R.A. Cerione ; J.P. Sethna
      • Source: IET Systems Biology, Volume 1, Issue 3, p. 190 –202
      • DOI:  10.1049/iet-syb:20060065
      • Type: Article
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      • p. 190 –202 (13)
        We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (β-Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system.
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