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Processes 2015, 3(3), 701-729; doi:10.3390/pr3030701

Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities

1
Department of Chemical Engineering, Brigham Young University, 350 CB, Provo, UT 84602, USA
2
Vertex Pharmaceuticals, 50 Northern Avenue, Boston, MA 02210, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Carl D. Laird
Received: 15 May 2015 / Accepted: 15 September 2015 / Published: 21 September 2015
(This article belongs to the Special Issue Algorithms and Applications in Dynamic Optimization)
View Full-Text   |   Download PDF [3373 KB, uploaded 21 September 2015]   |  

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. View Full-Text
Keywords: large-scale systems biology; ErbB signaling pathway; differential algebraic equations; data reconciliation; parameter sensitivity; hybrid simultaneous optimization; structural decomposition large-scale systems biology; ErbB signaling pathway; differential algebraic equations; data reconciliation; parameter sensitivity; hybrid simultaneous optimization; structural decomposition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lewis, N.R.; Hedengren, J.D.; Haseltine, E.L. Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities. Processes 2015, 3, 701-729.

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