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WIREs Nanomed Nanobiotechnol
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Informatics approaches for identifying biologic relationships in time‐series data

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Abstract A vital goal of the genomic era is to identify biologic relationships between genes and gene products and to understand how these relationships influence phenotypes. Time course data contain a vast amount of causal and mechanistic information about complex systems, but experimental and informatics challenges must be overcome to produce and extract this information from biologic systems. Mathematical modeling and bioinformatics methods are being developed in anticipation of experiments involving the coordinated measurement of cellular and molecular quantities at various spatial and temporal scales. Experimental methods that probe at the nanoscale will facilitate the exploration of biologic systems at the single‐cell and single‐molecule level, but will also introduce special challenges for mathematical modeling because events at nanoscale concentrations are subject to the influence of intrinsic noise. This review addresses the progress, challenges, and frontiers in the field of time‐series informatics. The ultimate goal of time‐series informatics is to move beyond descriptive relationships and toward predictive models of emergent, or systemic, behaviors of biologic systems as a whole. Copyright © 2008 John Wiley & Sons, Inc. This article is categorized under: Nanotechnology Approaches to Biology > Cells at the Nanoscale

Time‐series profiles for hypothetical gene regulatory system described in Figure 2. Parameters used for the simulation are κ = (0.9, 1.0, 0.6), γ = (1.0, 0.6, 0.8), and θ = 0.9.

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Recursive parameter estimation with unscented Kalman filter for extremely sparse, noisy data (filled circles) simulated with model shown in Figure 2. In each panel, each overlaid predicted time curve corresponds to the recursive steps through the time series. Parameters converge after 11 steps.

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Hypothetical single‐gene regulatory network, simulated in Figure 1, involving a negative feedback loop with measured gene products x, y, and z. A single gene with mRNA concentration x produces an enzyme with concentration y. Enzyme y catalyzes a reaction step leading to metabolite z, which inhibits the gene that codes for the enzyme. Parameters κ and γ are the production and degradation constants and θ modulates the inhibitory Hill function.

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