1,720,967 research outputs found
Unraveling the functional interaction structure of a biomolecular network through alternate perturbation of initial conditions
No full textVarious approaches attempting to infer the functional interaction structure of a hidden biontolecular network from experimental time-series measurements have been developed; however, due to both experimental limitations and methodological complexities, a large majority of these approaches have been unsuccessful. In particular, with respect to the elucidation of such networks, there are (i) a dimensionality problem: too many network nodes with too few available sampling points, (ii) a computational complexity problem: exponential complexity if a priori information is unavailable for regulatory nodes, and (iii) an experimental measurement problem: no guidelines for an appropriate experimental design for distinguishing direct and indirect influences among network nodes. Here, we sought to develop a new methodology capable of identifying the correct functional interaction structure with only a few sampling points through relatively simple computations. We also attempted to provide guidelines for an experimental design capable of supporting this methodology by taking proper measurements of the direct influences among the network nodes. In the present study, we considered an experiment where measurements were taken at two sampling time points with alternate perturbation (upregulation or down-regulation) of initial conditions while keeping the same initial conditions for unperturbed network nodes, and propose a new method of identifying the functional interaction structure from such measurements. The proposed method is able to avoid the dimensionality problem caused by the practically limited number of sampling time points, and does not suffer from the computational complexity problem, as it only uses a simple algebra based on the Mean Value Theorem (see Supplementary mathematical descriptions) without any other complicated computation. In addition, we provide a detailed guideline for an experimental design that can take proper measurements of the direct influences among the network nodes through perturbation of initial conditions. The proposed method is particularly useM for cases investigating the local interaction structure around a specific network node of interest. An example,-based on simulated data, is provided to illustrate the proposed method. (c) 2007 Elsevier B.V. All rights reserved
Inference of transcriptional regulatory networks based on initial mRNA expression profiles and DNA-binding motifs
No full textSystem-level investigations into the functional role of RKIP in the Ras-Raf-MEK-ERK signal transduction pathway
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System-level investigation into the regulatory mechanism of the calcineurin/NFAT signaling pathway
No full textCalcineum/nuclear factor of the activated T cell (CaN/NFAT) signaling pathway plays crucial roles in the development of cardiac hypertrophy, Down's syndrome, and autoimmune diseases in response to pathological stimuli. The aim of the present study is to get a system-level understanding on the regulatory mechanism of CaN/NFAT signaling pathway in consideration of the controversial roles of myocyte-enriched calcineurin interacting protein1 (MCIP1) for varying stress stimuli. To this end, we have developed an experimentally validated mathematical model and carried out computer simulations as well as cell-based experiments. Quantitative overexpression and knock-down experiments in C2C12 myoblasts have revealed that MCIP1 functions only as a calcineurin inhibitor. We have also observed a biphasic response of the NFAT activity with increasing stimuli of isoproterenol. Through extensive in silico simulations, we have discovered that the NFAT activity is primarily modulated by ERK5 and MCIP1 under mild isoproterenol stimuli whereas it is mainly modulated by atrogin1 (muscle atrophy F-box protein) under strong isoproterenol stimuli. This study shows that a system-level analysis may help understanding CaN/NFAT signaling-associated disease. (C) 2008 Elsevier Inc. All rights reserved
Reverse engineering of gene regulatory networks
No full textSystems 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
A unified framework for unraveling the functional interaction structure of a biomolecular network based on stimulus-response experimental data
No full textAbstractWe propose a unified framework for the identification of functional interaction structures of biomolecular networks in a way that leads to a new experimental design procedure. In developing our approach, we have built upon previous work. Thus we begin by pointing out some of the restrictions associated with existing structure identification methods and point out how these restrictions may be eased. In particular, existing methods use specific forms of experimental algebraic equations with which to identify the functional interaction structure of a biomolecular network. In our work, we employ an extended form of these experimental algebraic equations which, while retaining their merits, also overcome some of their disadvantages. Experimental data are required in order to estimate the coefficients of the experimental algebraic equation set associated with the structure identification task. However, experimentalists are rarely provided with guidance on which parameters to perturb, and to what extent, to perturb them. When a model of network dynamics is required then there is also the vexed question of sample rate and sample time selection to be resolved. Supplying some answers to these questions is the main motivation of this paper.The approach is based on stationary and/or temporal data obtained from parameter perturbations, and unifies the previous approaches of Kholodenko et al. (PNAS 99 (2002) 12841–12846) and Sontag et al. (Bioinformatics 20 (2004) 1877–1886). By way of demonstration, we apply our unified approach to a network model which cannot be properly identified by existing methods. Finally, we propose an experiment design methodology, which is not limited by the amount of parameter perturbations, and illustrate its use with an in numero example
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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