A quasi‐sequential approach to large‐scale dynamic optimization problems

Hong, Weirong; Wang, Shuqing; Li, Pu; Wozny, Günter; Biegler, Lorenz T.

doi:10.1002/aic.10625

Cited by 69 publications

(40 citation statements)

References 35 publications

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“…The continuous optimization problem was transformed to a nonlinear programming problem by the quasi-sequential approach 40 . The quasi-sequential approach was extended to handle approximation errors in moving finite element strategies (called qMFE), with constraints on state and optimization variables 41 .…”

Section: Methodsmentioning

confidence: 99%

Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes

et al. 2013

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To survive in fluctuating environmental conditions, microorganisms must be able to quickly react to environmental challenges by upregulating the expression of genes encoding metabolic pathways. Here we show that protein abundance and protein synthesis capacity are key factors that determine the optimal strategy for the activation of a metabolic pathway. If protein abundance relative to protein synthesis capacity increases, the strategies shift from the simultaneous activation of all enzymes to the sequential activation of groups of enzymes and finally to a sequential activation of individual enzymes along the pathway. In the case of pathways with large differences in protein abundance, even more complex pathway activation strategies with a delayed activation of low abundance enzymes and an accelerated activation of high abundance enzymes are optimal. We confirm the existence of these pathway activation strategies as well as their dependence on our proposed constraints for a large number of metabolic pathways in several hundred prokaryotes.

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Section: Methodsmentioning

confidence: 99%

Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes

et al. 2013

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“…This methods starts as a steepest descent algorithm and then transitions to a Newton-Raphson approach as the Hessian information is obtained. Instead of finite difference methods, accurate first derivatives (∇ u J(ū)) can be obtained by solving adjoint sensitivity equations [73][74][75]. The cost is the additional computational expense and configuration to solve additional equations at every time step.…”

Section: Hybrid Simultaneous and Sequential Dae Optimizationmentioning

confidence: 99%

Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities

2015

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Abstract:In recent years, model optimization in the field of computational biology has become a prominent area for development of pharmaceutical drugs. The increased amount of experimental data leads to the increase in complexity of proposed models. With increased complexity comes a necessity for computational algorithms that are able to handle the large datasets that are used to fit model parameters. In this study the ability of simultaneous, hybrid simultaneous, and sequential algorithms are tested on two models representative of computational systems biology. The first case models the cells affected by a virus in a population and serves as a benchmark model for the proposed hybrid algorithm. The second model is the ErbB model and shows the ability of the hybrid sequential and simultaneous method to solve large-scale biological models. Post-processing analysis reveals insights into the model formulation that are important for understanding the specific parameter optimization. A parameter sensitivity analysis reveals shortcomings and difficulties in the ErbB model parameter optimization due to the model formulation rather than the solver capacity. Suggested methods are model reformulation to improve input-to-output model linearity, sensitivity ranking, and choice of solver.

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“…DYNAMIC OPTIMIZATION PROBLEM TO FIND THE OPTIMUM CONTROLLER GAINS FOR CONSTANT INPUT VELOCITY OF THE FBM-SDF The dynamic optimization problem [15] consist on finding the optimum design variables p ∈ R 3 such that minimize the objective function (44) subject to the closed-loop system of the FNM-SDF (45) with the initial state vector x 0 , inequalities constraints (48) and bounds in the design variable (49).…”

Section: Dynamic Modelmentioning

confidence: 99%

Differential Evolution for the Control Gain's Optimal Tuning of a Four-bar Mechanism

Calva-Yáñez¹,

Niño-Suárez²,

Villarreal-Cervantes³

et al. 2013

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Abstract-In this paper the variation of the velocity error of a four-bar mechanism with spring and damping forces is reduced by solving a dynamic optimization problem using a differential evolution algorithm with a constraint handling mechanism. The optimal design of the velocity control for the mechanism is formulated as a dynamic optimization problem. Moreover, in order to compare the results of the differential evolution algorithm, a simulation experiment of the proposed control strategy was carried out. The simulation results and discussion are presented in order to evaluate the performance of both approaches in the control of the mechanism.

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A quasi‐sequential approach to large‐scale dynamic optimization problems

Cited by 69 publications

References 35 publications

Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes

Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes

Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities

Differential Evolution for the Control Gain's Optimal Tuning of a Four-bar Mechanism

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