Hybrid Dynamic Optimization Methods for Systems Biology with Efficient Sensitivities

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