Dynamic sensitivity analysis of biological systems

Wu, Wu‐Hsiung; Wang, Feng Sheng; Chang, Mau Song

doi:10.1186/1471-2105-9-s12-s17

Cited by 40 publications

(24 citation statements)

References 28 publications

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“…By exploiting various experimental data and annotations, they were able to uncover different interactive and co-operative relationships between processes. Wu et al [40], who also considered an application to pathway network analysis, proposed a different methodology to analyse time delay biological systems. Particularly, they consider an adaptive direct-decoupled algorithm to compute the dynamic sensitivities of biological systems described by Delay Differential Equation (DDE) nonlinear models, where the user needs to supply the model equations.…”

Section: Network and Graph Modelsmentioning

confidence: 99%

Fusion methodologies for biomedical data

Tsiliki

Κοσσίδα

2011

Journal of Proteomics

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Section: Network and Graph Modelsmentioning

confidence: 99%

Fusion methodologies for biomedical data

Tsiliki

Κοσσίδα

2011

Journal of Proteomics

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“…There exist, however, variations around this methodology, which mostly aim at improving numerical accuracy and efficiency-as well as alternatives, such as the adjoint-method (Cacuci 1981;Tortorelli and Michaleris 1994;Chaniotis et al 2001;Dunker et al 2002;Wu et al 2008;Perumal and Gunawan 2011;Zi 2011). We also note that, in contrast to discrete-time models, an additional kind of error arises when analysing periodic dynamics because of the effect of parameter perturbation on the period of the cycles.…”

Section: Discussionmentioning

confidence: 94%

Sensitivity analysis of continuous-time models for ecological and evolutionary theories

2015

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National audienceSensitivity analyses are of paramount importance in ecological and evolutionary theories, but their application to continuous time models has been virtually ignored from these fields. We present a simple and general method that makes this analysis possible for any model specified by a system of ordinary differential equations, using the direct method from mathematical theory. The resulting analysis may be used to study the effect of parameter perturbation on the whole trajectories of the state variables as well as for deriving the sensitivity of composite metrics such as the population growth rate. We also present methods for analyzing the sensitivity of discrete events within a continuous-time framework, such as the age at maturation, where timing may be affected by the perturbation. These methods are applied to a model for the energetics of individual growth, reproduction, and mortality. The method is versatile and can be applied to study transient as well as asymptotic dynamics, and its application may benefit many fields of ecology and evolution

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“…Shi and colleagues studied the balance between robustness and fragility in biochemical networks [406]. Several articles expanded the concepts of gains and sensitivities to questions of dynamics [124][125][126][127][407][408][409][410][411][412]. ese analyses assess how changes in initial values affect the transient responses of a system.…”

Section: Sensitivity and Robustnessmentioning

confidence: 99%

Biochemical Systems Theory: A Review

Voit

2013

ISRN Biomathematics

148

154

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Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. is paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. e paper is intended as a guide for investigators entering the fascinating �eld of biological systems analysis and as a resource for practitioners and experts. PreambleBiochemical systems theory (BST) is a mathematical and computational framework for analyzing and simulating systems. It was originally developed for biochemical pathways but by now has become much more widely applied to systems throughout biology and beyond. BST is called "canonical, " which means that model construction, diagnosis, and analysis follow stringent rules, which will be discussed throughout this paper. e key ingredient of BST is the power-law representation of all processes in a system.BST has been the focus of a number of books [1-6], including some in Chinese and Japanese [7][8][9]. e most detailed modern text dedicated speci�cally to BST is [3]. Moreover, since its inception, numerous reviews have portrayed the evolving state of the art in BST. Most of these reviews summarized methodological advances for the analysis of biochemical pathway systems . Others compared BST models with alternative modeling frameworks [32,[51][52][53][54][55][56][57][58][59][60][61][62][63]; some of these comparisons will be discussed later. Yet other reviews focused on specialty areas, such as customized methods for optimizing BST models (e.g., [64][65][66][67]) or estimating their parameters (e.g., [68][69][70][71][72]), strategies for discovering design and operating principles in natural system (e.g., [73][74][75][76][77][78]), and even the use of BST models in statistics [79][80][81]. A historical account of the �rst twenty years of BST was presented in [82].Supporting the methodological developments in the �eld, several soware packages were developed for different aspects of BST analysis. e earliest was ESSYNS [83][84][85], which supported all standard steady-state analyses and also contained a numerical solver that, at the time, was many times faster than any off-the-shelf soware [86,87]; see also [88]. Utilizing advances in computing and an extension of the solver from ESSYNS, Ferreira created the very user-friendly package PLAS, which is openly available and still very widely used [89] (see also [3]). Yamada and colleagues developed soware to translate E-cell models into BST models in PLAS format [90]. Okamoto's group created the soware BestKit, which permits the graphical design and translation of models, along with their analysis [91][92][93]. Vera and colleagues developed the soware tool PLMaddon for analyzing BST models within SBML [94]; see also [95]. It expands the Matlab SBToolbox with speci�c functionalities for power-law repr...

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Dynamic sensitivity analysis of biological systems

Cited by 40 publications

References 28 publications

Fusion methodologies for biomedical data

Fusion methodologies for biomedical data

Sensitivity analysis of continuous-time models for ecological and evolutionary theories

Biochemical Systems Theory: A Review

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