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Toward failure analyses in systems biology

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Abstract Parallels between designed and biological systems with respect to formal failure analyses have been presented. Failure analysis in designed systems depends on an identified, limited set of parameters or operation variables with high predictive value. In contrast, the biological systems pose problems in identification of operation variables and the identified variables may not be accurate predictors of failure. The difficulty in parameter identification is because of large numbers of components and the inability to envelope variables at each compartment or contour level. Contour level maps for biological systems are currently non‐existent, and most failure models are based on very limited, unilateral operation variables (a mutant gene). Operation variable identification within each contour level will enhance failure analyses of complex biological systems. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Analytical and Computational Methods > Analytical Methods

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(a) A simplistic representation of non‐biological system parameters for failure analysis. A non‐biological system has been depicted as a box. All operation variables on the outside of the contour are represented by large arrows. Internal operation variables (variables within the contour of the system) have been shown by small arrows. The same box has been shown where input variables (I/O/U) and output variables (I1/O1/U1) depict critical operation variables for failure analysis. In the bottom, a typical failure analysis with any of the three parameters (I/O/U) has been depicted. Representative figure of scalability and life span in (b) designer and (c) biological systems. Different examples have been shown to draw size and life span comparisons between biological and non‐biological systems.

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(a) Contour map: a snapshot of a network of cellular regulatory pathways derived using Genbank protein IDs and the Genego portal is shown. Adopted from Benjamin et al.62 The proteins identified from a proteomic identification experiment from the optic tectum of the brain of Taeniopygia guttata was subjected to analysis of protein–protein interaction network using Genogo. In this schematic diagram, all calcium‐interacting proteins (Calmodulin, ANT or ADP/ATP translocase 1, SLC25A5 or ADP/ATP translocase 2, Calcineurin A, Dynamin, Destrin, Actin, and CAPZ beta or F‐actin‐capping protein subunit beta) are shown to be connected and forming a network. The symbols represent different proteins identified in the experiment and a number of proteins do not interact with other identified proteins and therefore are not connected. Such connections of identified proteins usually represent intra‐contour interactions usually in the cytoplasm. Representation of the idea of contour maps at each level of compartmentalization. (b) Contour maps at subcellular levels are shown. The boundary of the cytoplasm and nucleus are shown by the solid and dashed circle, respectively. Cyt and n represents parameters or molecules that are important across the boundary of the cell and nucleus; subscripts I and O and IO represent input and output and parameters/molecules that can serve as both input and output. Some intra‐contour parameters for cytosol and nucleus that interact among themselves are shown by lines with linehead representing the molecules/factors. For cytosolic boundary, the nucleus may be considered as a second intra‐contour boundary. (c) Contour maps at organ levels have been schematically represented. Integration of such contour level input–output parameters will ultimately provide an integrated view of the multicellular organism and such a process will help in understanding homeostasis and pathogenesis. Thus beginning with contours at organ level [shown in (c)], one can reach to intra‐contour at cellular level, at nuclear level [shown in (b)] and arrive at the molecular level (shown in A). At each organ level contour, key parameters need to be identified. For each organ contour, a top down evaluation of contours can be achieved up to a cellular or subcellular parameter/factor/molecule level. Evaluation for failure can be restricted to organ contour level or could move from organ to subcellular contours or vice versa depending upon a given situation for assessment.

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