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Online ISSN 1751-8857 Print ISSN 1751-8849

IET Systems Biology

Volume 6, Issue 5, October 2012

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Volume 6, Issue 5

October 2012

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    • Comparison of statistical and optimisation-based methods for data-driven network reconstruction of biochemical systems
      • Author(s): B. Asadi ; M.R. Maurya ; D.M. Tartakovsky ; S. Subramaniam
      • Source: IET Systems Biology, Volume 6, Issue 5, p. 155 –163
      • DOI:  10.1049/iet-syb.2011.0052
      • Type: Article
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      • p. 155 –163 (9)
        Data-driven reconstruction of biological networks is a crucial step towards making sense of large volumes of biological data. Although several methods have been developed recently to reconstruct biological networks, there are few systematic and comprehensive studies that compare different methods in terms of their ability to handle incomplete datasets, high data dimensions and noisy data. The authors use experimentally measured and synthetic datasets to compare three popular methods – principal component regression (PCR), linear matrix inequalities (LMI) and least absolute shrinkage and selection operator (LASSO) – in terms of root-mean-squared error (RMSE), average fractional error in the value of the coefficients, accuracy, sensitivity, specificity and the geometric mean of sensitivity and specificity. This comparison enables the authors to establish criteria for selection of an appropriate approach for network reconstruction based on a priori properties of experimental data. For instance, although PCR is the fastest method, LASSO and LMI perform better in terms of accuracy, sensitivity and specificity. Both PCR and LASSO are better than LMI in terms of fractional error in the values of the computed coefficients. Trade-offs such as these suggest that more than one aspect of each method needs to be taken into account when designing strategies for network reconstruction. [Includes supplementary material]
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      Building blocks of biological networks: a review on major network motif discovery algorithms
      • Author(s): A. Masoudi-Nejad ; F. Schreiber ; Z.R.M. Kashani
      • Source: IET Systems Biology, Volume 6, Issue 5, p. 164 –174
      • DOI:  10.1049/iet-syb.2011.0011
      • Type: Article
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      • p. 164 –174 (11)
        In recent years, there has been a great interest in studying different aspects of complex networks in a range of fields. One important local property of networks is network motifs, recurrent and statistically significant sub-graphs or patterns, which assists researchers in the identification of functional units in the networks. Although network motifs may provide a deep insight into the network's functional abilities, their detection is computationally challenging. Therefore several algorithms have been introduced to resolve this computationally hard problem. These algorithms can be classified under various paradigms such as exact counting methods, sampling methods, pattern growth methods and so on. Here, the authors will give a review on computational aspects of major algorithms and enumerate their related benefits and drawbacks from an algorithmic perspective.
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      Identifying similar functional modules by a new hybrid spectral clustering method
      • Author(s): S. Madani ; K. Faez ; M. Aminghafari
      • Source: IET Systems Biology, Volume 6, Issue 5, p. 175 –186
      • DOI:  10.1049/iet-syb.2010.0066
      • Type: Article
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      • p. 175 –186 (12)
        Recently, a large number of researches have focused on finding cellular modules within protein–protein interaction networks. Until now, most of the works have concentrated on finding small modules and protein complexes. The authors have extended the concept of functional module and have identified larger functional modules which are the most similar to the entire network. To this end, a new hybrid spectral-based method is proposed here. First, the original graph is transformed into a line graph. Next, the nodes of the new graph are represented in the Euclidean space by using spectral methods and finally, a self-organising map is applied to the points in the new feature space. The experimental results show that similar modules, obtained from the proposed method, have own local hubs and lots of significant functional subunits concerning each other. These modules not only detect general biological processes that each protein is involved in, but also due to great similarities to the original network, it can be used as significant subnetworks for predicting protein function as detailed as possible. Some interesting properties of these modules are also investigated in this research. [Includes supplementary material]
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      Systemic modelling of human bioenergetics and blood circulation
      • Author(s): K.M. Lim ; S.-H. Yang ; E.B. Shim
      • Source: IET Systems Biology, Volume 6, Issue 5, p. 187 –195
      • DOI:  10.1049/iet-syb.2011.0035
      • Type: Article
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      • p. 187 –195 (9)
        This work reviews the main aspects of human bioenergetics and the dynamics of the cardiovascular system, with emphasis on modelling their physiological characteristics. The methods used to study human bioenergetics and circulation dynamics, including the use of mathematical models, are summarised. The main characteristics of human bioenergetics, including mitochondrial metabolism and global energy balance, are first described, and the systemic aspects of blood circulation and related physiological issues are introduced. The authors also discuss the present status of studies of human bioenergetics and blood circulation. Then, the limitations of the existing studies are described in an effort to identify directions for future research towards integrated and comprehensive modelling. This review emphasises that a multi-scale and multi-physical approach to bioenergetics and blood circulation that considers multiple scales and physiological factors are necessary for the appropriate clinical application of computational models.
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      Analysis of a bio-dynamic model via Lyapunov principle and small-world network for tuberculosis
      • Author(s): H.-Y. Chung ; C.-Y. Chung ; S.-C. Ou
      • Source: IET Systems Biology, Volume 6, Issue 5, p. 196 –206
      • DOI:  10.1049/iet-syb.2011.0078
      • Type: Article
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      • p. 196 –206 (11)
        The study will apply Lyapunov principle to construct a dynamic model for tuberculosis (TB). The Lyapunov principle is commonly used to examine and determine the stability of a dynamic system. To simulate the transmissions of vector-borne diseases and discuss the related health policies effects on vector-borne diseases, the authors combine the multi-agent-based system, social network and compartmental model to develop an epidemic simulation model. In the identity level, the authors use the multi-agent-based system and the mirror identity concept to describe identities with social network features such as daily visits, long-distance movement, high degree of clustering, low degree of separation and local clustering. The research will analyse the complex dynamic mathematic model of TB epidemic and determine its stability property by using the popular Matlab/Simulink software and relative software packages. Facing the current TB epidemic situation, the development of TB and its developing trend through constructing a dynamic bio-mathematical system model of TB is investigated. After simulating the development of epidemic situation with the solution of the SMIR epidemic model, the authors will come up with a good scheme to control epidemic situation to analyse the parameter values of a model that influence epidemic situation evolved. The authors will try to find the quarantining parameters that are the most important factors to control epidemic situation. The SMIR epidemic model and the results via numerical analysis may offer effective prevention with reference to controlling epidemic situation of TB.
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