Bayesian parameter estimation for nonlinear modelling of biological pathways
| العنوان: | Bayesian parameter estimation for nonlinear modelling of biological pathways |
|---|---|
| المؤلفون: | Merry L. Lindsey, Tianyi Yang, Nguyen K Nguyen, Yufang Jin, Omid Ghasemi, Yufei Huang |
| المصدر: | BMC Systems Biology, Vol 5, Iss Suppl 3, p S9 (2011) BMC Systems Biology |
| بيانات النشر: | BMC, 2011. |
| سنة النشر: | 2011 |
| مصطلحات موضوعية: | Mathematical optimization, Computer science, Differential equation, Heart Ventricles, Myocardial Infarction, 030204 cardiovascular system & hematology, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Transforming Growth Factor beta, Structural Biology, Modelling and Simulation, Nonlinear modelling, Applied mathematics, Molecular Biology, lcsh:QH301-705.5, 030304 developmental biology, 0303 health sciences, Hill differential equation, Mathematical model, Estimation theory, Macrophages, Systems Biology, Applied Mathematics, Bayes Theorem, Markov chain Monte Carlo, Interleukin-10, Computer Science Applications, Nonlinear system, Nonlinear Dynamics, lcsh:Biology (General), Modeling and Simulation, Ordinary differential equation, symbols, Research Article |
| الوصف: | Background The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. Results We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC) method. We applied this approach to the biological pathways involved in the left ventricle (LV) response to myocardial infarction (MI) and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly parameterized dynamic systems. Conclusions Our proposed Bayesian algorithm successfully estimated parameters in nonlinear mathematical models for biological pathways. This method can be further extended to high order systems and thus provides a useful tool to analyze biological dynamics and extract information using temporal data. |
| اللغة: | English |
| تدمد: | 1752-0509 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02f331a9276463c83d46e60604c11effTest http://www.biomedcentral.com/1752-0509/5/S3/S9Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....02f331a9276463c83d46e60604c11eff |
| قاعدة البيانات: | OpenAIRE |
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To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. Results We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC) method. We applied this approach to the biological pathways involved in the left ventricle (LV) response to myocardial infarction (MI) and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly parameterized dynamic systems. 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