针对GMA(Generalized Mass Action)型色氨酸生物合成系统的双目标优化问题,提出了一种求解方法.首先给出色氨酸生物合成系统的GMA模型,然后构建色氨酸生物合成系统的双目标优化模型.为了求解前面构建的双目标优化问题,基于NBI方法给出...针对GMA(Generalized Mass Action)型色氨酸生物合成系统的双目标优化问题,提出了一种求解方法.首先给出色氨酸生物合成系统的GMA模型,然后构建色氨酸生物合成系统的双目标优化模型.为了求解前面构建的双目标优化问题,基于NBI方法给出了求解策略.为了克服NBI方法难以得到双目标优化问题全局Pareto解的不足,应用过滤算法去除经典NBI方法产生的非全局Pareto解.该过滤算法可以得到双目标优化问题的全局Pareto解.通过MATLAB计算,结果表明本文可以获得GMA型色氨酸生物合成系统双目标优化问题的Pareto最优解以及Pareto前沿,验证了所提方法的实用性和有效性.展开更多
Systems biology requires the development of algorithms that use omics data to infer interaction networks among biomolecules working within an organism. One major type of evolutionary algorithm, genetic programming (GP...Systems biology requires the development of algorithms that use omics data to infer interaction networks among biomolecules working within an organism. One major type of evolutionary algorithm, genetic programming (GP), is useful for its high heuristic ability as a search method for obtaining suitable solutions expressed as tree structures. However, because GP determines the values of parameters such as coefficients by random values, it is difficult to apply in the inference of state equations that describe oscillatory biochemical reaction systems with high nonlinearity. Accordingly, in this study, we propose a new GP procedure called “k-step GP” intended for inferring the state equations of oscillatory biochemical reaction systems. The k-step GP procedure consists of two algorithms: 1) Parameter optimization using the modified Powell method—after genetic operations such as crossover and mutation, the values of parameters such as coefficients are optimized by applying the modified Powell method with secondary convergence. 2) GP using divided learning data—to improve the inference efficiency, imposes perturbations through the addition of learning data at various intervals and adaptations to these changes result in state equations with higher fitness. We are confident that k-step GP is an algorithm that is particularly well suited to inferring state equations for oscillatory biochemical reaction systems and contributes to solving inverse problems in systems biology.展开更多
This paper presents a solution methodology for H<sub>∞</sub>-feedback control design problem of Heparin controlled blood clotting network under the presence of stochastic noise. The formulaic solution pro...This paper presents a solution methodology for H<sub>∞</sub>-feedback control design problem of Heparin controlled blood clotting network under the presence of stochastic noise. The formulaic solution procedure to solve nonlinear partial differential equation, the Hamilton-Jacobi-Isaacs equation with Successive Galrkin’s Approximation is sketched and validity is proved. According to Lyapunov’s theory, with solutions of the nonlinear PDEs, robust feedback control is designed. To confirm the performance and robustness of the designed controller, numerical and Monte-Carlo simulation results by Simulink software on MATLAB are provided.展开更多
文摘针对GMA(Generalized Mass Action)型色氨酸生物合成系统的双目标优化问题,提出了一种求解方法.首先给出色氨酸生物合成系统的GMA模型,然后构建色氨酸生物合成系统的双目标优化模型.为了求解前面构建的双目标优化问题,基于NBI方法给出了求解策略.为了克服NBI方法难以得到双目标优化问题全局Pareto解的不足,应用过滤算法去除经典NBI方法产生的非全局Pareto解.该过滤算法可以得到双目标优化问题的全局Pareto解.通过MATLAB计算,结果表明本文可以获得GMA型色氨酸生物合成系统双目标优化问题的Pareto最优解以及Pareto前沿,验证了所提方法的实用性和有效性.
文摘Systems biology requires the development of algorithms that use omics data to infer interaction networks among biomolecules working within an organism. One major type of evolutionary algorithm, genetic programming (GP), is useful for its high heuristic ability as a search method for obtaining suitable solutions expressed as tree structures. However, because GP determines the values of parameters such as coefficients by random values, it is difficult to apply in the inference of state equations that describe oscillatory biochemical reaction systems with high nonlinearity. Accordingly, in this study, we propose a new GP procedure called “k-step GP” intended for inferring the state equations of oscillatory biochemical reaction systems. The k-step GP procedure consists of two algorithms: 1) Parameter optimization using the modified Powell method—after genetic operations such as crossover and mutation, the values of parameters such as coefficients are optimized by applying the modified Powell method with secondary convergence. 2) GP using divided learning data—to improve the inference efficiency, imposes perturbations through the addition of learning data at various intervals and adaptations to these changes result in state equations with higher fitness. We are confident that k-step GP is an algorithm that is particularly well suited to inferring state equations for oscillatory biochemical reaction systems and contributes to solving inverse problems in systems biology.
文摘This paper presents a solution methodology for H<sub>∞</sub>-feedback control design problem of Heparin controlled blood clotting network under the presence of stochastic noise. The formulaic solution procedure to solve nonlinear partial differential equation, the Hamilton-Jacobi-Isaacs equation with Successive Galrkin’s Approximation is sketched and validity is proved. According to Lyapunov’s theory, with solutions of the nonlinear PDEs, robust feedback control is designed. To confirm the performance and robustness of the designed controller, numerical and Monte-Carlo simulation results by Simulink software on MATLAB are provided.
