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Sensitivity analysis of dynamic biological systems with time-delays.

Wu WH, Wang FS, Chang MS - BMC Bioinformatics (2010)

Bottom Line: These systems are represented as delay differential equation (DDE) models.The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors.To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan. wwh@cs.ccu.edu.tw

ABSTRACT

Background: Mathematical modeling has been applied to the study and analysis of complex biological systems for a long time. Some processes in biological systems, such as the gene expression and feedback control in signal transduction networks, involve a time delay. These systems are represented as delay differential equation (DDE) models. Numerical sensitivity analysis of a DDE model by the direct method requires the solutions of model and sensitivity equations with time-delays. The major effort is the computation of Jacobian matrix when computing the solution of sensitivity equations. The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors. To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary.

Results: We have proposed an efficient algorithm with an adaptive step size control to compute the solution and dynamic sensitivities of biological systems described by ordinal differential equations (ODEs). The adaptive direct-decoupled algorithm is extended to solve the solution and dynamic sensitivities of time-delay systems describing by DDEs. To save the human effort and avoid the human errors in the computation of partial derivatives, an automatic differentiation technique is embedded in the extended algorithm to evaluate the Jacobian matrix. The extended algorithm is implemented and applied to two realistic models with time-delays: the cardiovascular control system and the TNF-α signal transduction network. The results show that the extended algorithm is a good tool for dynamic sensitivity analysis on DDE models with less user intervention.

Conclusions: By comparing with direct-coupled methods in theory, the extended algorithm is efficient, accurate, and easy to use for end users without programming background to do dynamic sensitivity analysis on complex biological systems with time-delays.

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Stacked 100% column chart for individual state variables. Each column inthe stack column chart shows all semi-relative parameter sensitivities for astate variable. The proportion of a parameter sensitivity to the totalsensitivity for a state variable is displayed as a color area in each column.The values of time-averaged semi-relative parameter sensitivities are used asthe data.
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Figure 6: Stacked 100% column chart for individual state variables. Each column inthe stack column chart shows all semi-relative parameter sensitivities for astate variable. The proportion of a parameter sensitivity to the totalsensitivity for a state variable is displayed as a color area in each column.The values of time-averaged semi-relative parameter sensitivities are used asthe data.

Mentions: The EAMCM program is applied to the TNF-α signal transduction modelusing the initial conditions as described in Table S1 of Additional file 1. All dynamic sensitivities with respect to 29 parameters and31 initial conditions are computed simultaneously without any difficulty. Alltime-averaged semi-relative parameter sensitivities for each state variable are shownin Figure 6. Most of the time-averaged semi-relative parametersensitivities for each state variable are too small compared with the largest and canbe ignored. It is easy to find from Figure 6 that only some fewparameter sensitivities get significant percentage of the total sensitivity for eachstate variable.


Sensitivity analysis of dynamic biological systems with time-delays.

Wu WH, Wang FS, Chang MS - BMC Bioinformatics (2010)

Stacked 100% column chart for individual state variables. Each column inthe stack column chart shows all semi-relative parameter sensitivities for astate variable. The proportion of a parameter sensitivity to the totalsensitivity for a state variable is displayed as a color area in each column.The values of time-averaged semi-relative parameter sensitivities are used asthe data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2957680&req=5

Figure 6: Stacked 100% column chart for individual state variables. Each column inthe stack column chart shows all semi-relative parameter sensitivities for astate variable. The proportion of a parameter sensitivity to the totalsensitivity for a state variable is displayed as a color area in each column.The values of time-averaged semi-relative parameter sensitivities are used asthe data.
Mentions: The EAMCM program is applied to the TNF-α signal transduction modelusing the initial conditions as described in Table S1 of Additional file 1. All dynamic sensitivities with respect to 29 parameters and31 initial conditions are computed simultaneously without any difficulty. Alltime-averaged semi-relative parameter sensitivities for each state variable are shownin Figure 6. Most of the time-averaged semi-relative parametersensitivities for each state variable are too small compared with the largest and canbe ignored. It is easy to find from Figure 6 that only some fewparameter sensitivities get significant percentage of the total sensitivity for eachstate variable.

Bottom Line: These systems are represented as delay differential equation (DDE) models.The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors.To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan. wwh@cs.ccu.edu.tw

ABSTRACT

Background: Mathematical modeling has been applied to the study and analysis of complex biological systems for a long time. Some processes in biological systems, such as the gene expression and feedback control in signal transduction networks, involve a time delay. These systems are represented as delay differential equation (DDE) models. Numerical sensitivity analysis of a DDE model by the direct method requires the solutions of model and sensitivity equations with time-delays. The major effort is the computation of Jacobian matrix when computing the solution of sensitivity equations. The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors. To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary.

Results: We have proposed an efficient algorithm with an adaptive step size control to compute the solution and dynamic sensitivities of biological systems described by ordinal differential equations (ODEs). The adaptive direct-decoupled algorithm is extended to solve the solution and dynamic sensitivities of time-delay systems describing by DDEs. To save the human effort and avoid the human errors in the computation of partial derivatives, an automatic differentiation technique is embedded in the extended algorithm to evaluate the Jacobian matrix. The extended algorithm is implemented and applied to two realistic models with time-delays: the cardiovascular control system and the TNF-α signal transduction network. The results show that the extended algorithm is a good tool for dynamic sensitivity analysis on DDE models with less user intervention.

Conclusions: By comparing with direct-coupled methods in theory, the extended algorithm is efficient, accurate, and easy to use for end users without programming background to do dynamic sensitivity analysis on complex biological systems with time-delays.

Show MeSH