Systems biology - Reverse engineering the cell

Nature (Impact Factor: 42.35). 09/2008; 454(7208):1059-62. DOI: 10.1038/4541059a
Source: PubMed

ABSTRACT 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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    • "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 · 2.19 Impact Factor
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    • "The coefficients c 1 , c 2 are two positive acceleration constants used to scale the contribution of the cognitive and social components; they are often determined empirically. The values r 1 , r 2 are both random values within the range [0] [1]. The products c 1 r 1 and c 2 r 2 thus stochastically control the overall velocity of a particle. "
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    ABSTRACT: The construction of gene regulatory networks from expression data is one of the most important issues in systems biology research. However, building such networks is a tedious task, especially when both the number of genes and the complexity of gene regulation increase. In this work, we adopt the S-system model to represent the gene network and establish a methodology to infer the model. Our work mainly includes an adaptive genetic algorithm-particle swarm optimization hybrid method to infer appropriate network parameters, and a gene clustering method to decompose a large network into several smaller networks for dimension reduction. To validate the proposed methods, different series of experiments have been conducted and the results show that the proposed methods can be used to infer S-system models of gene networks efficiently and successfully.
    The Computer Journal 09/2011; 54(9):1449-1464. DOI:10.1093/comjnl/bxr038 · 0.89 Impact Factor
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    • "In addition, despite knowing many of the components of a cellular signaling pathway, the way that these components interact is very complex and needs elucidation (Brabant et al. 1992). This provides the motivation for the development of next generation microfluidic platforms for investigation of temporal dynamics, since dynamic stimulation patterns can be used to probe signaling mechanisms in ways that static systems cannot (Ingolia and Weissman 2008; Paliwal et al. 2008). The results of such studies hold enormous physiological and therapeutic importance, since many times disruption of the intrinsic rhythms of these systems leads to pathological conditions (Brabant et al. 1992; Gambacciani et al. 1987; Van Couter and Refetoff 1985) and knowing the signaling mechanisms can help with drug development (Persidis 1998). "
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    ABSTRACT: In biology, it is not only the magnitude of a chemical stimulus that determines cellular response; it is becoming increasingly clear that the timing of the stimulus is vastly important as well. Currently there is a paucity of data regarding cell behavior under dynamic stimulation conditions that are representative of what occurs in vivo. This is, at least in part, attributed to the lack of appropriate tools for generating time-varying stimulatory signals in highly diverse patterns. Fluidics on the macro and micro scale has provided a practical platform for dynamically stimulating cells in a highly controllable manner at physiological and supra-physiological time scales (seconds to a few hours). These fluidic systems have contributed substantially to our understanding of how cells process and react to dynamic stimulatory environments; while these setups provide the means to analyze and manipulate cellular behavior in these types of environments on the single-cell level and on a high-throughput level, improvements can be made to these platforms to enhance their utility for high-impact biological investigations of temporal dynamics.
    Microfluidics and Nanofluidics 06/2009; 6(6):717-729. DOI:10.1007/s10404-009-0413-x · 2.67 Impact Factor
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