Hybrid optimization method with general switching strategy for parameter estimation.
BACKGROUND
Modeling and simulation of cellular signaling and metabolic pathways as networks of biochemical reactions yields sets of non-linear ordinary differential equations. These models usually depend on several parameters and initial conditions. If these parameters are unknown, results from simulation studies can be misleading. Such a scenario can be avoided by fitting the model to experimental data before analyzing the system. This involves parameter estimation which is usually performed by minimizing a cost function which quantifies the difference between model predictions and measurements. Mathematically, this is formulated as a non-linear optimization problem which often results to be multi-modal (non-convex), rendering local optimization methods detrimental.


RESULTS
In this work we propose a new hybrid global method, based on the combination of an evolutionary search strategy with a local multiple-shooting approach, which offers a reliable and efficient alternative for the solution of large scale parameter estimation problems.


CONCLUSIONS
The presented new hybrid strategy offers two main advantages over previous approaches: First, it is equipped with a switching strategy which allows the systematic determination of the transition from the local to global search. This avoids computationally expensive tests in advance. Second, using multiple-shooting as the local search procedure reduces the multi-modality of the non-linear optimization problem significantly. Because multiple-shooting avoids possible spurious solutions in the vicinity of the global optimum it often outperforms the frequently used initial value approach (single-shooting). Thereby, the use of multiple-shooting yields an enhanced robustness of the hybrid approach.
Balsa-Canto
Eva
E
Process Engineering Group, Spanish Council for Scientific Research, IIM-CSIC, Spain. ebalsa@iim.csic.es
Peifer
Martin
M
Banga
Julio R
JR
Timmer
Jens
J
Fleck
Christian
C
D000465:Algorithms; D003198:Computer Simulation; D020869:Gene Expression Profiling; D016013:Likelihood Functions; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D011237:Predictive Value of Tests; D012016:Reference Values; D050799:STAT5 Transcription Factor / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*
D050799:STAT5 Transcription Factor