Specifying informative experiment stimulation conditions for resolving dynamical uncertainty in biological systems

Area of Science:

• Systems Biology
• Computational Biology
• Experimental Design

Background:

• Model-based design of experiments (MBDOE) traditionally optimizes measurable species and time points.
• Resolving uncertainties in complex biological system dynamics requires careful experimental planning.

Purpose of the Study:

• To develop a computationally efficient MBDOE strategy that incorporates experimental stimulation magnitudes and measurement points.
• To investigate if including system inputs (perturbations) improves the MBDOE method's ability to resolve model uncertainties.

Main Methods:

• Utilized a sparse-grid approximation for computational tractability of model output dynamics.
• Pre-specified time points for input/perturbation application and employed scenario trees to explore uncertainty.
• Integrated consecutive scenario trees to determine optimal input magnitudes and select measurement species/time points.

Main Results:

• Demonstrated the effectiveness of the enhanced MBDOE strategy on a T-Cell Receptor (TCR) signaling pathway model.
• The inclusion of input perturbations was shown to enhance the resolution of system dynamics uncertainties.

Conclusions:

• The developed MBDOE strategy offers a computationally efficient approach for optimizing experimental design in complex biological systems.
• Incorporating system inputs into the experimental design process is crucial for effectively resolving model uncertainties.