Systems biology - Reverse engineering the cell

Nature (Impact Factor: 41.46). 09/2008; 454(7208):1059-62. DOI: 10.1038/4541059a
Source: PubMed


Borrowing ideas that were originally developed to study electronic circuits, two reports decipher how yeast reacts to changes in its environment by analysing the organism's responses to oscillating input signals.

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    • "Our approach is based on the concept of sparse Bayesian learning [5], [6] and on the definition of a unified optimisation problem allowing the consideration of different parameter values for different experimental conditions, and whose solution is a model consistent with all datasets available for identification. The ability to consider various datasets simultaneously can potentially avoid non-identifiability issues arising when a single dataset is used [7]. Furthermore, by comparing the identified parameter values associated with different conditions, we can pinpoint the influence specific experimental changes have on system parameters. "
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    ABSTRACT: In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.
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    • "A second area of active research involves data-based or datadriven modeling approaches that do not rely on a priori knowledge of the internal state of the system but rather on input-output data measured directly on the system [62] [63] [64]. Frequently used data-driven approaches applied to biological system analysis include input-output transfer function models [65] [66] [67] [68], autoregressive time series analysis [69] [70], nonlinear time series, and Voltera integral series analysis methods (such as principal component analysis [54] [55] [71] [72]), and network-centric models [54]. For monitoring of biological systems, these data-driven approaches have several advantages. "
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    ABSTRACT: The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches in order to address this unmet need. Two main paths of development have characterized the Society’s approach: i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiological signals or multivariate analyses of molecular and genetic data, and ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control, and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience, and the impact that merging these modeling approaches can have on general anesthesia.
    Journal of critical care 08/2014; 29(4). DOI:10.1016/j.jcrc.2014.03.018 · 2.00 Impact Factor
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    • "It is a procedure used to manipulate experimentally measured time-series data for building a model that can describe the observed phenotypic behavior of a system to be studied. To save the effort of testing experimentally which interactions in the gene networks are possible and then deriving the network accordingly, an automated reverse engineering procedure has been advocated [1,2]. This work means to establish a practical computational methodology for the inference of real gene networks. "
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    ABSTRACT: To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks.
    BMC Systems Biology 01/2014; 8(1):5. DOI:10.1186/1752-0509-8-5 · 2.44 Impact Factor
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