![]() ![]() Multi-start local optimization usually performs very well in terms of finding the assumingly global optimum ( Raue et al., 2013) ( Supplementary Fig. The user can choose between different strategies for automated starting point selection, such as latin hypercube or uniform sampling, or provide custom starting points. To deal with multiple local optima, PESTO provides a multi-start local optimization strategy which has been shown to perform well in terms of convergence and computation time for biological ODE and PDE models ( Hross and Hasenauer, 2016 Raue et al., 2013). Therefore, there are diverse stochastic, deterministic or hybrid algorithms available ( Raue et al., 2013). These optima cannot be derived analytically and evaluating the whole parameter space is computationally prohibitive. Many biological problems are non-convex and exhibit several local optima of which usually the global one is of interest. PESTO can be applied to many optimization problems within the systems biology field and beyond. The optimization can be accelerated when gradient and Hessian of the objective function are provided. The objective function is treated as black box, no further assumptions are made. PESTO is agnostic of the underlying model or objective function and can be used in conjunction with any deterministic optimization problem formulated as a MATLAB function. Several sampling methods for uncertainty and identifiability analysisĮfficient work flow and optional parallelization Optimization-based, integration-based or hybrid profile calculation for uncertainty and identifiability analysis Multi-start local optimization and interfaces to global and hybrid optimizers PESTO offers the following features ( Supplementary Fig. PESTO is a freely available MATLAB toolbox offering scalable state-of-the-art algorithms for optimization, uncertainty and identifiability analysis, which do not depend on any problem-specific assumptions. In an effort to provide a universally applicable parameter estimation toolbox, we developed PESTO. A feature comparison of a selection of toolboxes related to parameter estimation is provided as Supplementary Table S1. Hence, the user must adapt to different tools, depending on the current problem structure. ordinary differential equations (ODEs) or partial differential equations (PDEs). There are various toolboxes available which offer this functionality, however, most of them focus either on individual aspects or are tailored to specific model types, e.g. ![]() In many disciplines such as computational biology, engineering and operations research, researchers are faced with the problem of finding a set of parameters optimizing a given objective function and determining uncertainties of those estimates.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |