Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities

Processes

Nicholas R. Lewis²  John D. Hedengren²  Eric L. Haseltine¹ 

[1] Vertex Pharmaceuticals, 50 Northern Avenue, Boston, MA 02210, USA; E-Mail:;Department of Chemical Engineering, Brigham Young University, 350 CB, Provo, UT 84602, USA; E-Mail:

关键词: large-scale systems biology;    ErbB signaling pathway;    differential algebraic equations;    data reconciliation;    parameter sensitivity;    hybrid simultaneous optimization;    structural decomposition;   

DOI  :  10.3390/pr3030701

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.