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Online ISSN 1751-8857 Print ISSN 1751-8849

IET Systems Biology

Volume 5, Issue 5, September 2011

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Volume 5, Issue 5

September 2011

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    • Brute force meets Bruno force in parameter optimisation: introduction of novel constraints for parameter accuracy improvement by symbolic computation
      • Author(s): M. Nakatsui ; K. Horimoto ; F. Lemaire ; A. Ürgüplü ; A. Sedoglavic ; F. Boulier
      • Source: IET Systems Biology, Volume 5, Issue 5, p. 281 –292
      • DOI:  10.1049/iet-syb.2010.0051
      • Type: Article
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      • p. 281 –292 (12)
        Recent remarkable advances in computer performance have enabled us to estimate parameter values by the huge power of numerical computation, the so-called ‘Brute force’, resulting in the high-speed simultaneous estimation of a large number of parameter values. However, these advancements have not been fully utilised to improve the accuracy of parameter estimation. Here the authors review a novel method for parameter estimation using symbolic computation power, ‘Bruno force’, named after Bruno Buchberger, who found the Gröbner base. In the method, the objective functions combining the symbolic computation techniques are formulated. First, the authors utilise a symbolic computation technique, differential elimination, which symbolically reduces an equivalent system of differential equations to a system in a given model. Second, since its equivalent system is frequently composed of large equations, the system is further simplified by another symbolic computation. The performance of the authors' method for parameter accuracy improvement is illustrated by two representative models in biology, a simple cascade model and a negative feedback model in comparison with the previous numerical methods. Finally, the limits and extensions of the authors' method are discussed, in terms of the possible power of ‘Bruno force’ for the development of a new horizon in parameter estimation.
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      Model-based investigation of the transcriptional activity of p53 and its feedback loop regulation via 14-3-3σ
      • Author(s): J. Vera ; S. Nikolov ; X. Lai ; A. Singh ; O. Wolkenhauer
      • Source: IET Systems Biology, Volume 5, Issue 5, p. 293 –307
      • DOI:  10.1049/iet-syb.2010.0080
      • Type: Article
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      • p. 293 –307 (15)
        Experiments have recently shown that p53 expression can display oscillations in response to certain stress signals. In this work, mathematical modelling and bifurcation analysis are combined to investigate under which conditions the oscillation of p53 could propagate to its direct downstream transcription targets. The authors' analysis suggests that oscillations of p53 will propagate only to proteins with medium-fast mRNA and protein turnover rates. The authors retrieved data concerning the half-life of mRNA and protein for a number of p53-promoted genes and found that, according to their model, most of them are not able to inherit the oscillation of p53 because of their slow turnover rates. However, their analysis indicates that p53 oscillation may actually fine-tune the expression pattern of a protein when it is integrated with a second oscillatory signal. The authors also consider the case of additional regulatory loops affecting p53 oscillations and involving proteins transcriptionally induced by p53. Their results for 14-3-3σ, a protein that targets the p53 inhibitor MDM2 for degradation, suggest that the addition of feedback-loop regulation may modulate basic properties of p53 oscillation and induce quick cessation of them under certain physiological conditions. Moreover, the interplay between DNA damage and 14-3-3σ may induce bistability in the oscillation of p53. [Includes supplementary material]
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      Qualitative modelling and formal verification of the FLR1 gene mancozeb response in Saccharomyces cerevisiae
      • Author(s): P.T. Monteiro ; P.J. Dias ; D. Ropers ; A.L. Oliveira ; I. Sá-Correia ; M.C. Teixeira ; A.T. Freitas
      • Source: IET Systems Biology, Volume 5, Issue 5, p. 308 –316
      • DOI:  10.1049/iet-syb.2011.0001
      • Type: Article
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      • p. 308 –316 (9)
        Background: Qualitative models allow understanding the relation between the structure and the dynamics of gene regulatory networks. The dynamical properties of these models can be automatically analysed by means of formal verification methods, like model checking. This facilitates the model-validation process and the test of new hypotheses to reconcile model predictions with the experimental data. Results: The authors report in this study the qualitative modelling and simulation of the transcriptional regulatory network controlling the response of the model eukaryote Saccharomyces cerevisiae to the agricultural fungicide mancozeb. The model allowed the analysis of the regulation level and activity of the components of the gene mancozeb-induced network controlling the transcriptional activation of the FLR1 gene, which is proposed to confer multidrug resistance through its putative role as a drug eflux pump. Formal verification analysis of the network allowed us to confront model predictions with the experimental data and to assess the model robustness to parameter ordering and gene deletion. Conclusions: This analysis enabled us to better understand the mechanisms regulating the FLR1 gene mancozeb response and confirmed the need of a new transcription factor for the full transcriptional activation of YAP1. The result is a computable model of the FLR1 gene response to mancozeb, permitting a quick and cost-effective test of hypotheses prior to experimental validation.
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      New index for categorising cardiac reentrant wave: in silico evaluation
      • Author(s): E.B. Shim ; S.-B. Hong ; K.M. Lim ; C.H. Leem ; C.-H. Youn ; H.-N. Pak ; Y.E. Earm ; D. Noble
      • Source: IET Systems Biology, Volume 5, Issue 5, p. 317 –323
      • DOI:  10.1049/iet-syb.2011.0019
      • Type: Article
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      • p. 317 –323 (7)
        Based on the similarity between a reentrant wave in cardiac tissue and a vortex in fluid dynamics, the authors hypothesised that a new non-dimensional index, like the Reynolds number in fluid dynamics, may play a critical role in categorising reentrant wave dynamics. Therefore the goal of the present study is to devise a new index to characterise electric wave conduction in cardiac tissue and examined whether this index can be used as a biomarker for categorising the reentrant wave pattern in cardiac tissue. Similar to the procedure used to derive the Reynolds number in fluid dynamics, the authors used a non-dimensionalisation technique to obtain the new index. Its usefulness was verified using a two-dimensional simulation model of electrical wave propagation in cardiac tissue. The simulation results showed that electrical waves in cardiac tissue move into an unstable region when the index exceeds a threshold value.
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