Features

Summary of the current features


  • Network visualization linking to Cytoscape

  • Maximum flexibility for the definition of models and observation functions.

  • Multi-experiment tasks with local (experiment dependent) and global information.

  • Multiple types of experimental noise conditions and, accordingly, different types of cost functions for parameter estimation and experimental design.

  • Maximum flexibility for the definition of unknowns: parameters and initial conditions that may be local (experiment dependent) or global for all tasks.

  • Several approaches to performing identifiability analysis: i) local and global sensitivity analysis, ii) ranking of parameters, iii) sensitivity clustering, iv) the Fisher Information Matrix (FIM) to asymptotic analyses; v) the plot of two-dimensional projections of the parameter estimation cost function and vi) the robust analysis by means of a Monte-Carlo based approach.

  • Regularization techniques for parameter estimation.

  • Parameter estimation post-analysis: goodness of fit analysis, autocorrelation of the residuals.

  • Sequential-parallel optimal experimental designs: to compute what, how and when to measure.

  • Parameter estimation and optimal experimental design with general non-linear constraints.

  • A suite of state of the art numerical methods for simulation to cover a broad range of problems: integration of stiff, non-stiff and/or sparse dynamic systems.

  • A collection of methods to compute parametric sensitivities: direct methods, finite differences schemes of several orders.

  • A suite of state of the art numerical methods for optimization to cover a broad range of problems: constrained convex and multimodal, multi-objective nonlinear optimization problems.

  • The possibility of adding your optimizers to AMIGO2.

  • Exact jacobian information to accelerate the convergence of local methods.

  • Enables an enhanced operating mode by automatically interfacing to C.

  • Full C mode for parameter estimation.

  • Constrained multi-objective dynamic optimization with the control vector parameterization approach.

  • Maximum flexibility in the definition of objective functions, linear and non-linear constraints.

  • Generates reports and plots according to user requirements.