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See detailpathPSA: A Dynamical Pathway-Based Parametric Sensitivity
Perumal, Thanneer Malai UL; Gunawan, Rudiyanto

in Industrial and Engineering Chemistry (2014)

Normal functioning of biological systems relies on coordinated activities of biomolecules participating in a complex network of biological interactions. As human intuition is often incapable in analyzing ... [more ▼]

Normal functioning of biological systems relies on coordinated activities of biomolecules participating in a complex network of biological interactions. As human intuition is often incapable in analyzing how biological functions emerge from such complex interactions, the use of mathematical models and systems analysis tools has become critical in understanding biological system behavior. While biological functions have been associated with the connectivity (structure) and pathways of the network and the kinetics of the processes involved, most model analyses address each of these aspects separately. In contrast, we present a novel sensitivity analysis, called pathway-based parametric sensitivity analysis (pathPSA), which combines the analysis of both network structure and kinetics. Unlike existing sensitivity analyses, pathPSA relies on perturbing the kinetics of pathways in the network, using persistent perturbations or impulse perturbations at varying time. Consequently, the sensitivity coefficients can give insights on the dominant pathways in a network and any transient shift in the rate controlling mechanism. The efficacy of pathPSA is demonstrated through an application to understand competing signaling pathways in programmed cell death network. [less ▲]

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See detailUnderstanding dynamics using sensitivity analysis: caveat and solution
Perumal, Thanneer Malai UL; Gunawan, Rudiyanto

in BMC Systems Biology (2011), 5

BACKGROUND: Parametric sensitivity analysis (PSA) has become one of the most commonly used tools in computational systems biology, in which the sensitivity coefficients are used to study the parametric ... [more ▼]

BACKGROUND: Parametric sensitivity analysis (PSA) has become one of the most commonly used tools in computational systems biology, in which the sensitivity coefficients are used to study the parametric dependence of biological models. As many of these models describe dynamical behaviour of biological systems, the PSA has subsequently been used to elucidate important cellular processes that regulate this dynamics. However, in this paper, we show that the PSA coefficients are not suitable in inferring the mechanisms by which dynamical behaviour arises and in fact it can even lead to incorrect conclusions. RESULTS: A careful interpretation of parametric perturbations used in the PSA is presented here to explain the issue of using this analysis in inferring dynamics. In short, the PSA coefficients quantify the integrated change in the system behaviour due to persistent parametric perturbations, and thus the dynamical information of when a parameter perturbation matters is lost. To get around this issue, we present a new sensitivity analysis based on impulse perturbations on system parameters, which is named impulse parametric sensitivity analysis (iPSA). The inability of PSA and the efficacy of iPSA in revealing mechanistic information of a dynamical system are illustrated using two examples involving switch activation. CONCLUSIONS: The interpretation of the PSA coefficients of dynamical systems should take into account the persistent nature of parametric perturbations involved in the derivation of this analysis. The application of PSA to identify the controlling mechanism of dynamical behaviour can be misleading. By using impulse perturbations, introduced at different times, the iPSA provides the necessary information to understand how dynamics is achieved, i.e. which parameters are essential and when they become important. [less ▲]

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See detailDynamical analysis of cellular networks based on the Green's function matrix
Perumal, Thanneer Malai UL; Wu, Yan; Gunawan, Rudiyanto

in Journal of Theoretical Biology (2009), 261(2), 248-59

The complexity of cellular networks often limits human intuition in understanding functional regulations in a cell from static network diagrams. To this end, mathematical models of ordinary differential ... [more ▼]

The complexity of cellular networks often limits human intuition in understanding functional regulations in a cell from static network diagrams. To this end, mathematical models of ordinary differential equations (ODEs) have commonly been used to simulate dynamical behavior of cellular networks, to which a quantitative model analysis can be applied in order to gain biological insights. In this paper, we introduce a dynamical analysis based on the use of Green's function matrix (GFM) as sensitivity coefficients with respect to initial concentrations. In contrast to the classical (parametric) sensitivity analysis, the GFM analysis gives a dynamical, molecule-by-molecule insight on how system behavior is accomplished and complementarily how (impulse) signal propagates through the network. The knowledge gained will have application from model reduction and validation to drug discovery research in identifying potential drug targets, studying drug efficacy and specificity, and optimizing drug dosing and timing. The efficacy of the method is demonstrated through applications to common network motifs and a Fas-induced programmed cell death model in Jurkat T cell line. [less ▲]

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