NLoed

NLoed implements optimal experimental design (OED) methods to construct and optimize experiments for fitting nonlinear, multi-input/output, and dynamic models in systems and synthetic biology to improve parameter estimation and model calibration.

Key Features:

• Optimal Experimental Design (OED): Implements OED techniques to identify experiments that efficiently inform specified modeling objectives.
• Model support: Supports nonlinear, multi-input/output, and dynamic models and accommodates non-normal data types.
• Objective functions: Uses objectives based on the expected Fisher information matrix and supports relaxed formulations of design optimization problems.
• Parameter estimation: Provides maximum likelihood fitting and diagnostic tools for model calibration.
• Modular architecture: Provides a modular and flexible architecture to represent diverse experimental scenarios.

Scientific Applications:

• Systems biology: Designs experiments to improve parameter estimation and predictive accuracy of systems biology models.
• Synthetic biology: Optimizes experimental designs for calibration and validation of synthetic biological circuits.
• Non-normal data handling: Supports experimental planning and analysis for data types that deviate from normal assumptions.
• Optogenetics characterization: Applied to experimental design for characterizing a bacterial optogenetic system.

Methodology:

Computational methods explicitly include optimal experimental design techniques, objective functions based on the expected Fisher information matrix (including relaxed design formulations), and maximum likelihood fitting with diagnostic tools.