Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities

Nicholas Lewis,Eric Haseltine,John Hedengren

doi:10.3390/pr3030701

Nicholas Lewis, Eric Haseltine + Show 1 more

Open Access

https://doi.org/10.3390/pr3030701

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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.

Highlights

Advances in biomedical research have led to an increase of experimental data to be interpreted in the context of reaction pathways, molecular transport, and population dynamics
The purpose of using the ErbB model in this study is to show the ability of the proposed methods to decrease computational time required to obtain a good fit to data in a large systems biology model
This study shows that the solver algorithms used for parameter optimization in dynamic biological models are effective in reducing the computational time (144,000 CPU min [20] for simulated annealing, a sequential method, versus approximately 1 CPU min for either the hybrid or simultaneous method) for parameter estimation

Summary

Introduction

Advances in biomedical research have led to an increase of experimental data to be interpreted in the context of reaction pathways, molecular transport, and population dynamics. Kinetic modeling is one method employed to interpret this data and is used in the pharmaceutical industry in developing clinical trials for new medications [1]. Many of these models are based on first principles, such as species balance equations and kinetic reactions. Often in the development of the model there are parameters and initial conditions that are costly to measure or cannot be measured directly through experimental procedures. These parameters are potentially estimated through the use of optimization techniques. The ability of simultaneous and sequential solvers used in kinetic modeling is proposed as a more efficient mechanism to model systems biology behavior of newly developed treatments

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