Parameter identification
As part of model building, deals with the computation of model unknowns
(initial or boundary conditions and parameters) from experimental data.
Parameter identification is usually formulated as a nonlinear
optimization problem aimed to find the model unknowns which minimize
some measure of the distance among model predictions and experimental
data.
For the case of (large scale) nonlinear models solving such a
problem is usually a very challenging task due mainly to the presence of
several suboptimal solutions or of several equivalent solutions, in
other words, to poor or lack of practical identifiability.
AMIGO2 covers all the steps of the iterative identification
procedure: local and global sensitivity analysis, local and global
ranking of parameters, parameter estimation, identifiability analysis,
regularization and optimal experimental design.
The ultimate goal is to enable the computation of model unknowns with the maximum accuracy and at a minimum experimental cost.
 Optimization based modeling
Optimality principles have been successfully used to describe the
design, organization and behavior of biological
systems at different levels. In the context of cell biology,
mathematical optimization is the underlying hypothesis in applications
such
as (dynamic) flux balance analysis and the analysis of activation of
metabolic pathways.These problems can be formulated as general dynamic optimization problems;
the objective is to compute time varying control profiles, usually
fluxes or enzyme concentrations and the corresponding expression rates, to maximize or minimize a given cost function, such as the amount
of a specific metabolite or the
time needed to reach a given amount of product, subject to the system
dynamics (the model) and algebraic constraints, for example in the
maximum amount of enzyme available.
In addition, multiobjective formulations can be posed aimed at finding those control profiles which offer the best tradeoffs among different objectives.
AMIGO2 enables the possibility of using optimality principles for modeling through multiobjective dynamic optimization.

Optimal control
Models can be used to confirm hypotheses, to draw predictions but also to design or optimize a certain aspect of the biological system. In this concern, once a suitable model system is available, it is possible to define new multiobjective dynamic optimization problems to find those optimal stimulation conditions that result in a particular desired behavior. This is the case in, for example, model based metabolic engineering, optimal control or drug dose optimization, to name a few.
AMIGO2 enables the possibility of computing optimal designs and stimulation conditions using multiobjective dynamic optimization.
